finetuning.txt

Understanding mind-brain-environment relations is one of the key topics inpsychology. Kurt Lewin, inspired by theoretical physics, tried to establishtopological and vector psychology analyzing patterns of interaction between theindividual and her/his environment. The time is ripe to reformulate hisambitious goals, searching for ways to interpret objectively measured brainprocesses in terms of suitable psychological constructs. Connecting cognitiveand social psychology constructs to neurophenomics, as it is done now inpsychiatry, should ground them in physical reality.
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COVID-19 pandemic has shaken the roots of healthcare facilities worldwide,with the US being one of the most affected countries irrespective of being asuperpower. Along with the current pandemic, COVID-19 can cause a secondarycrisis of mental health pandemic if left unignored. Various studies from pastepidemics, financial turmoil and pandemic, especially SARS and MERS, have showna steep increase in mental and psychological issues like depression, lowquality of life, self-harm and suicidal tendencies among general populations.The most venerable being the individuals infected and cured due to socialdiscrimination. The government is taking steps to contain and prevent furtherinfections of COVID-19. However, the mental and psychological wellbeing ofpeople is still left ignored in developing countries like India. There is asignificant gap in India concerning mental and psychological health still beingstigmatized and considered 'non-existent'. This study's effort is to highlightthe importance of mental and psychological health and to suggest interventionsbased on positive psychology literature. These interventions can support thewellbeing of people acting as a psychological first aid. Keywords: COVID-19,Coronavirus, Pandemic, Mental wellbeing, Psychological Wellbeing, PositivePsychology Interventions.  KEYWORDS - COVID-19, Coronavirus, Pandemic, Wellbeing, Positive Psychology,Interventions, PPI.
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A clear explanation is given on how the causal, psychological, andelectrodynamic time arrows emerge from the thermodynamic time arrow.
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Computer administering of a psychological investigation is the computerrepresentation of the entire procedure of psychological assessments - testconstruction, test implementation, results evaluation, storage and maintenanceof the developed database, its statistical processing, analysis andinterpretation. A mathematical description of psychological assessment with theaid of personality tests is discussed in this article. The set theory and therelational algebra are used in this description. A relational model of data,needed to design a computer system for automation of certain psychologicalassessments is given. Some finite sets and relation on them, which arenecessary for creating a personality psychological test, are described. Thedescribed model could be used to develop real software for computeradministering of any psychological test and there is full automation of thewhole process: test construction, test implementation, result evaluation,storage of the developed database, statistical implementation, analysis andinterpretation. A software project for computer administering personalitypsychological tests is suggested.
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Many psychological experiments have subjects repeat a task to gain thestatistical precision required to test quantitative theories of psychologicalperformance. In such experiments, time-on-task can have sizable effects onperformance, changing the psychological processes under investigation. Mostresearch has either ignored these changes, treating the underlying process asstatic, or sacrificed some psychological content of the models for statisticalsimplicity. We use particle Markov chain Monte-Carlo methods to studypsychologically plausible time-varying changes in model parameters. Using datafrom three highly-cited experiments we find strong evidence in favor of ahidden Markov switching process as an explanation of time-varying effects. Thisembodies the psychological assumption of "regime switching", with subjectsalternating between different cognitive states representing different modes ofdecision-making. The switching model explains key long- and short-term dynamiceffects in the data. The central idea of our approach can be applied quitegenerally to quantitative psychological theories, beyond the models and datasets that we investigate.
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Visualization is a useful technology in health science, and especially forcommunity network analysis. Because visualization applications in healthcareare typically risk-averse, health psychologists can play a significant role inensuring appropriate and effective uses of visualization techniques inhealthcare. In this paper, we examine the role of health psychologists in thetriangle of "health science", "visualization technology", and "visualizationpsychology". We conclude that health psychologists can use visualization to aiddata intelligence workflows in healthcare and health psychology, whileresearching into visualization psychology to aid the improvement andoptimization of data visualization processes.
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In this paper, a novel anti-jamming mechanism is proposed to analyze andenhance the security of adversarial Internet of Battlefield Things (IoBT)systems. In particular, the problem is formulated as a dynamic psychologicalgame between a soldier and an attacker. In this game, the soldier seeks toaccomplish a time-critical mission by traversing a battlefield within a certainamount of time, while maintaining its connectivity with an IoBT network. Theattacker, on the other hand, seeks to find the optimal opportunity tocompromise the IoBT network and maximize the delay of the soldier's IoBTtransmission link. The soldier and the attacker's psychological behavior arecaptured using tools from psychological game theory, with which the soldier'sand attacker's intentions to harm one another are considered in theirutilities. To solve this game, a novel learning algorithm based on Bayesianupdating is proposed to find a $\epsilon$-like psychological self-confirmingequilibrium of the game. Simulation results show that, based on the error-freebeliefs on the attacker's psychological strategies and beliefs, the soldier'smaterial payoff can be improved by up to 15.11\% compared to a conventionaldynamic game without psychological considerations.
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The purpose of this paper is to suggest additional aspects of socialpsychology that could help when making sense of autonomous agile teams. To makeuse of well-tested theories in social psychology and instead see how theyreplicated and differ in the autonomous agile team context would avoidreinventing the wheel. This was done, as an initial step, through looking atsome very common agile practices and relate them to existing findings insocial-psychological research. The two theories found that I argue could bemore applied to the software engineering context are social identity theory andgroup socialization theory. The results show that literature providessocial-psychological reasons for the popularity of some agile practices, butthat scientific studies are needed to gather empirical evidence on theseunder-researched topics. Understanding deeper psychological theories couldprovide a better understanding of the psychological processes when buildingautonomous agile team, which could then lead to better predictability andintervention in relation to human factors.
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Psychological safety has been postulated as a key factor for the success ofagile software development teams, yet there is a lack of empirical studiesinvestigating the role of psychological safety in this context. The presentstudy examines how work design characteristics of software development teams(autonomy, task interdependence, and role clarity) influence psychologicalsafety and, further, how psychological safety impacts team performance, eitherdirectly or indirectly through team reflexivity. We test our model using surveydata from 236 team members in 43 software development teams in Norway. Ourresults show that autonomy boosts psychological safety in software teams, andthat psychological safety again has a positive effect on team reflexivity and adirect effect on team performance.
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Designing recommendation systems that serve content aligned with time varyingpreferences requires proper accounting of the feedback effects ofrecommendations on human behavior and psychological condition. We argue thatmodeling the influence of recommendations on people's preferences must begrounded in psychologically plausible models. We contribute a methodology fordeveloping grounded dynamic preference models. We demonstrate this method withmodels that capture three classic effects from the psychology literature:Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conductsimulation-based studies to show that the psychological models manifestdistinct behaviors that can inform system design. Our study has two directimplications for dynamic user modeling in recommendation systems. First, themethodology we outline is broadly applicable for psychologically groundingdynamic preference models. It allows us to critique recent contributions basedon their limited discussion of psychological foundation and their implausiblepredictions. Second, we discuss implications of dynamic preference models forrecommendation systems evaluation and design. In an example, we show thatengagement and diversity metrics may be unable to capture desirablerecommendation system performance.
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Psychological safety is a precondition for learning and success in softwareteams. Companies such as SavingsBank, which is discussed in this article, havedeveloped good practices to facilitate psychological safety, most of whichdepend on face-to-face interaction. However, what happens to psychologicalsafety when working remotely? In this article, we explore how Norwegiansoftware developers experienced pandemic and post-pandemic remote work anddescribe simple behaviors and attitudes related to psychological safety. We payspecial attention to the hybrid work mode, in which team members alternate daysin the office with days working from home. Our key takeaway is that spontaneousinteraction in the office facilitates psychological safety, while remote workincreases the thresholds for both spontaneous interaction and psychologicalsafety. We recommend that software teams synchronize their office presence toincrease chances for spontaneous interaction in the office while benefittingfrom focused work while at home.
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We propose the simple model of learning based on which we derive and explainthe Yerkes-Dodson law - one of the oldest laws of experimental psychology. Theapproach uses some ideas of quantum theory of open systems (QTOS) and developsthe method of statistical description of psychological systems that wasproposed by author earlier.
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When developing devices to encourage positive change in users, socialpsychology can offer useful conceptual resources. This article outlines threemajor theories from the discipline and discusses their implications fordesigning persuasive technologies.
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Modelling Consumer Indebtedness has proven to be a problem of complex nature.In this work we utilise Data Mining techniques and methods to explore themultifaceted aspect of Consumer Indebtedness by examining the contribution ofPsychological Factors, like Impulsivity to the analysis of Consumer Debt. Ourresults confirm the beneficial impact of Psychological Factors in modellingConsumer Indebtedness and suggest a new approach in analysing Consumer Debt,that would take into consideration more Psychological characteristics ofconsumers and adopt techniques and practices from Data Mining.
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Academic performance of any individual is dependent upon numerous aspectsregarding the day to day life of the individual under consideration. Academicperformance is measured in terms of the grade point average or GPA as it iscalled. Grade point average is dependent not only on the faculty but also onvarious psychological parameters including the study habits, social anxiety andallied. In this study, a detail analysis of numerous psychological factorsimpacting the grade point was carried and based upon various psychologicalfactors the performance for the student in forth coming examination wasforecasted.
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This paper describes our efforts in predicting current and futurepsychological health from childhood essays within the scope of the CLPsych-2018Shared Task. We experimented with a number of different models, includingrecurrent and convolutional networks, Poisson regression, support vectorregression, and L1 and L2 regularized linear regression. We obtained the bestresults on the training/development data with L2 regularized linear regression(ridge regression) which also got the best scores on main metrics in theofficial testing for task A (predicting psychological health from essayswritten at the age of 11 years) and task B (predicting later psychologicalhealth from essays written at the age of 11).
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Research has shown that the maturity of small workgroups from a psychologicalperspective is intimately connected to team agility. We, therefore, tested ifagile team members appreciated group development psychology training. Ourresults show that the participating teams seem to have a very positive view ofgroup development training and state that they now have a new way of thinkingabout teamwork and new tools to deal with team-related problems. We, therefore,see huge potential in training agile teams in group development psychologysince the positive effects might span over the entire software developmentorganization.
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Human psychology plays an important role in organizational performance.However, understanding our employees is a difficult task due to issues such aspsychological complexities, unpredictable dynamics, and the lack of data.Leveraging evidence-based psychology knowledge, this paper proposes a hybridmachine learning plus ontology-based reasoning system for detecting humanpsychological artifacts at scale. This unique architecture provides a balancebetween system's processing speed and explain-ability. System outputs can befurther consumed by graph science and/or model management system for optimizingbusiness processes, understanding team dynamics, predicting insider threats,managing talents, and beyond.
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As the first step to model emotional state of a person, we build sentimentanalysis models with existing deep neural network algorithms and compare themodels with psychological measurements to enlighten the relationship. In theexperiments, we first examined psychological state of 64 participants and askedthem to summarize the story of a book, Chronicle of a Death Foretold (Marquez,1981). Secondly, we trained models using crawled 365,802 movie review data;then we evaluated participants' summaries using the pretrained model as aconcept of transfer learning. With the background that emotion affects onmemories, we investigated the relationship between the evaluation score of thesummaries from computational models and the examined psychologicalmeasurements. The result shows that although CNN performed the best among otherdeep neural network algorithms (LSTM, GRU), its results are not related to thepsychological state. Rather, GRU shows more explainable results depending onthe psychological state. The contribution of this paper can be summarized asfollows: (1) we enlighten the relationship between computational models andpsychological measurements. (2) we suggest this framework as objective methodsto evaluate the emotion; the real sentiment analysis of a person.
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We propose that the Jungian psychological type of an individual is naturallymodelled as a quantum state: a maximally entangled two-qubit state, one ofwhose qubits is undergoing quantum teleportation.
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This article summarizes why physics instructors should use socialpsychological interventions to make physics classes equitable and inclusive.
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Research in psychology generates interesting data sets and unique statisticalmodelling tasks. However, these tasks, while important, are often veryspecific, so appropriate statistical models and methods cannot be found inaccessible Bayesian tools. As a result, the use of Bayesian methods is limitedto those that have the technical and statistical fundamentals that are requiredfor probabilistic programming. Such knowledge is not part of the typicalpsychology curriculum and is a difficult obstacle for psychology students andresearchers to overcome. The goal of the bayes4psy package is to bridge thisgap and offer a collection of models and methods to be used for data analysisthat arises from psychology experiments and as a teaching tool for Bayesianstatistics in psychology. The package contains Bayesian t-test andbootstrapping and models for analyzing reaction times, success rates, andcolors. It also provides all the diagnostic, analytic and visualization toolsfor the modern Bayesian data analysis workflow.
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There are numerous opportunities for engaging in research at the intersectionof psychology and visualization. While most opportunities taken up by the VIScommunity will likely focus on the psychology of users, there are alsoopportunities for studying the psychology of designers. In this position paper,I argue the importance of studying design cognition as a necessary component ofa holistic program of research on visualization psychology. I provide a briefoverview of research on design cognition in other disciplines, and discussopportunities for VIS to build an analogous research program. Doing so can leadto a stronger integration of research and design practice, can provide a betterunderstanding of how to educate and train future designers, and will likelysurface both challenges and opportunities for future research.
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Psychological sandplay, as an important psychological analysis tool, is avisual scene constructed by the tester selecting and placing sand objects(e.g., sand, river, human figures, animals, vegetation, buildings, etc.). Asthe projection of the tester's inner world, it contains high-level semanticinformation reflecting the tester's thoughts and feelings. Most of the existingcomputer vision technologies focus on the objective basic semantics (e.g.,object's name, attribute, boundingbox, etc.) in the natural image, while fewrelated works pay attention to the subjective psychological semantics (e.g.,emotion, thoughts, feelings, etc.) in the artificial image. We take the lattersemantics as the research object, take "split" (a common psychologicalsemantics reflecting the inner integration of testers) as the research goal,and use the method of machine learning to realize the automatic detection ofsplit semantics, so as to explore the application of machine learning in thedetection of subjective psychological semantics of sandplay images. To thisend, we present a feature dimensionality reduction and extraction algorithm toobtain a one-dimensional vector representing the split feature, and build thesplit semantic detector based on Multilayer Perceptron network to get thedetection results. Experimental results on the real sandplay datasets show theeffectiveness of our proposed algorithm.
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The usage of psychological networks that conceptualize psychological behavioras a complex interplay of psychological and other components has gainedincreasing popularity in various fields of psychology. While prior publicationshave tackled the topics of estimating and interpreting such networks, littlework has been conducted to check how accurate (i.e., prone to samplingvariation) networks are estimated, and how stable (i.e., interpretation remainssimilar with less observations) inferences from the network structure (such ascentrality indices) are. In this tutorial paper, we aim to introduce the readerto this field and tackle the problem of accuracy under sampling variation. Wefirst introduce the current state-of-the-art of network estimation. Second, weprovide a rationale why researchers should investigate the accuracy ofpsychological networks. Third, we describe how bootstrap routines can be usedto (A) assess the accuracy of estimated network connections, (B) investigatethe stability of centrality indices, and (C) test whether network connectionsand centrality estimates for different variables differ from each other. Weintroduce two novel statistical methods: for (B) the correlation stabilitycoefficient, and for (C) the bootstrapped difference test for edge-weights andcentrality indices. We conducted and present simulation studies to assess theperformance of both methods. Finally, we developed the free R-package bootnetthat allows for estimating psychological networks in a generalized framework inaddition to the proposed bootstrap methods. We showcase bootnet in a tutorial,accompanied by R syntax, in which we analyze a dataset of 359 women withposttraumatic stress disorder available online.
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A meaningful and deep understanding of the human aspects of softwareengineering (SE) requires psychological constructs to be considered. Psychologytheory can facilitate the systematic and sound development as well as theadoption of instruments (e.g., psychological tests, questionnaires) to assessthese constructs. In particular, to ensure high quality, the psychometricproperties of instruments need evaluation. In this paper, we provide anintroduction to psychometric theory for the evaluation of measurementinstruments for SE researchers. We present guidelines that enable usingexisting instruments and developing new ones adequately. We conducted acomprehensive review of the psychology literature framed by the Standards forEducational and Psychological Testing. We detail activities used whenoperationalizing new psychological constructs, such as item pooling, itemreview, pilot testing, item analysis, factor analysis, statistical property ofitems, reliability, validity, and fairness in testing and test bias. We providean openly available example of a psychometric evaluation based on ourguideline. We hope to encourage a culture change in SE research towards theadoption of established methods from psychology. To improve the quality ofbehavioral research in SE, studies focusing on introducing, validating, andthen using psychometric instruments need to be more common.
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Story generation, which aims to generate a long and coherent storyautomatically based on the title or an input sentence, is an important researcharea in the field of natural language generation. There is relatively littlework on story generation with appointed emotions. Most existing works focus onusing only one specific emotion to control the generation of a whole story andignore the emotional changes in the characters in the course of the story. Inour work, we aim to design an emotional line for each character that considersmultiple emotions common in psychological theories, with the goal of generatingstories with richer emotional changes in the characters. To the best of ourknowledge, this work is first to focuses on characters' emotional lines instory generation. We present a novel model-based attention mechanism that wecall SoCP (Storytelling of multi-Character Psychology). We show that theproposed model can generate stories considering the changes in thepsychological state of different characters. To take into account theparticularity of the model, in addition to commonly used evaluationindicators(BLEU, ROUGE, etc.), we introduce the accuracy rate of psychologicalstate control as a novel evaluation metric. The new indicator reflects theeffect of the model on the psychological state control of story characters.Experiments show that with SoCP, the generated stories follow the psychologicalstate for each character according to both automatic and human evaluations.
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We propose the consistent statistical approach for the quantitativedescription of simple psychological phenomena using the methods of quantumtheory of open systems (QTOS). Taking as the starting point the K. Lewin'spsychological field theory we show that basic concepts of this theory can benaturally represented in the language of QTOS. In particular provided that allstimuli acting on psychological system (that is individual or group ofinterest) are known one can associate with these stimuli correspondingoperators and after that to write down the equation for evolution of densitymatrix of the relevant open system which allows one to find probabilities ofall possible behavior alternatives. Using the method proposed we consider indetail simple model describing such interesting psychological phenomena ascognitive dissonance and the impact of competition among group members on itsunity.
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Sequential measurements of non-commuting observables produce order effectsthat are well-known in quantum physics. But their conceptual basis, asignificant measurement interaction, is relevant for far more generalsituations. We argue that non-commutativity is ubiquitous in psychology wherealmost every interaction with a mental system changes that system in anuncontrollable fashion. Psychological order effects for sequential measurementsare therefore to be expected as a rule. In this paper we focus on thetheoretical basis of such effects. We classify several families of ordereffects theoretically, relate them to psychological observations, and predicteffects yet to be discovered empirically. We assess the complexity, related tothe predictive power, of particular (Hilbert space) models of order effects anddiscuss possible limitations of such models.
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This article starts out with a detailed example illustrating the utility ofapplying quantum probability to psychology. Then it describes severalalternative mathematical methods for mapping fundamental quantum concepts (suchas state preparation, measurement, state evolution) to fundamentalpsychological concepts (such as stimulus, response, information processing).For state preparation, we consider both pure states and densities withmixtures. For measurement, we consider projective measurements and positiveoperator valued measurements. The advantages and disadvantages of each methodwith respect to applications in psychology are discussed.
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An accurate qualitative and comprehensive assessment of human potential isone of the most important challenges in any company or collective. We applyBayesian networks for developing more accurate overall estimations ofpsychological characteristics of an individual, based on psychological testresults, which identify how much an individual possesses a certain trait.Examples of traits could be a stress resistance, the readiness to take a risk,the ability to concentrate on certain complicated work. The most common way ofstudying psychological characteristics of each individual is testing.Additionally, the overall estimation is usually based on personal experiencesand the subjective perception of a psychologist or a group of psychologistsabout the investigated psychological personality traits.
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In the software engineering industry today, companies primarily conduct theirwork in teams. To increase organizational productivity, it is thus crucial toknow the factors that affect team effectiveness. Two team-related concepts thathave gained prominence lately are psychological safety and team norms. Still,few studies exist that explore these in a software engineering context.  Therefore, with the aim of extending the knowledge of these concepts, weexamined if psychological safety and team norm clarity associate positivelywith software developers' self-assessed team performance and job satisfaction,two important elements of effectiveness.  We collected industry survey data from practitioners (N = 217) in 38development teams working for five different organizations. The result ofmultiple linear regression analyses indicates that both psychological safetyand team norm clarity predict team members' self-assessed performance and jobsatisfaction. The findings also suggest that clarity of norms is a stronger(30\% and 71\% stronger, respectively) predictor than psychological safety.  This research highlights the need to examine, in more detail, therelationship between social norms and software development. The findings ofthis study could serve as an empirical baseline for such, future work.
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Language is a popular resource to mine speakers' attitude bias, supposingthat speakers' statements represent their bias on concepts. However, psychologystudies show that people's explicit bias in statements can be different fromtheir implicit bias in mind. Although both explicit and implicit bias areuseful for different applications, current automatic techniques do notdistinguish them. Inspired by psychological measurements of explicit andimplicit bias, we develop an automatic language-based technique to reproducepsychological measurements on large population. By connecting eachpsychological measurement with the statements containing the certaincombination of special words, we derive explicit and implicit bias byunderstanding the sentiment of corresponding category of statements. Extensiveexperiments on English and Chinese serious media (Wikipedia) and non-seriousmedia (social media) show that our method successfully reproduce thesmall-scale psychological observations on large population and achieve newfindings.
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While human beings have a right to digital experiences that support, ratherthan diminish, their psychological wellbeing, technology designers lackresearch-based practices for ensuring psychological needs are met. To helpaddress this gap, we draw on findings from over 30 years of research inpsychology (specifically, self-determination theory) that has identifiedcontextual factors shown to support psychological wellbeing. We translate thesefindings into a list of 15 heuristics and 30 design strategies to providetechnology makers with theoretically grounded, research-based, and actionableways to support wellbeing in user experience.
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Phishing emails exhibit some unique psychological traits which are notpresent in legitimate emails. From empirical analysis and previous research, wefind three psychological traits most dominant in Phishing emails - A Sense ofUrgency, Inducing Fear by Threatening, and Enticement with Desire. We manuallylabel 10% of all phishing emails in our training dataset for these threetraits. We leverage that knowledge by training BERT, Sentence-BERT (SBERT), andCharacter-level-CNN models and capturing the nuances via the last layers thatform the Phishing Psychological Trait (PPT) scores. For the phishing emaildetection task, we use the pretrained BERT and SBERT model, and concatenate thePPT scores to feed into a fully-connected neural network model. Our resultsshow that the addition of PPT scores improves the model performancesignificantly, thus indicating the effectiveness of PPT scores in capturing thepsychological nuances. Furthermore, to mitigate the effect of the imbalancedtraining dataset, we use the GPT-2 model to generate phishing emails (Radfordet al., 2019). Our best model outperforms the current State-of-the-Art (SOTA)model's F1 score by 4.54%. Additionally, our analysis of individual PPTssuggests that Fear provides the strongest cue in detecting phishing emails.
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Research methods and results from physics and psychology are used to explorea question in quantum mechanics. In this investigation, an observer variable ismanipulated in an experimental context concerning the trajectory of electronsaffected by their movement through a Stern-Gerlach apparatus. The basis inpsychology concerning why the observer variable is significant in theexperimental context is discussed. This investigation can serve as a prototypefor more complicated investigations.
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This paper introduces two ongoing research projects which seek to applycomputer modelling techniques in order to simulate human behaviour withinorganisations. Previous research in other disciplines has suggested thatcomplex social behaviours are governed by relatively simple rules which, whenidentified, can be used to accurately model such processes using computertechnology. The broad objective of our research is to develop a similarcapability within organisational psychology.
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We analyze, from a theoretical viewpoint, the bidirectional interdisciplinaryrelation between mathematics and psychology, focused on the mathematical theoryof deterministic dynamical systems, and in particular, on the theory of chaos.On one hand, there is the direct classic relation: the application ofmathematics to psychology. On the other hand, we propose the converse relationwhich consists in the formulation of new abstract mathematical problemsappearing from processes and structures under research of psychology. Thebidirectional multidisciplinary relation from-to pure mathematics, largelyholds with the "hard" sciences, typically physics and astronomy. But it israther new, from the social and human sciences, towards pure mathematics.
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The computer-assisted analysis is not currently a novelty, but a necessity inall areas of psychology. A number of studies that examine the limits of thecomputer assisted and analyzed interpretations, also its advantages. A seriesof studies aim to assess how the computer assisting programs are able toestablish a diagnosis referring to the presence of certain mental disorders. Wewill present the results of one computer application in clinical psychologyregarding the assessment of Theory of Mind capacity by animation.
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The problem considered is how to map the concepts of Quantum Theory (QT) toelements of a psychological experiment. The QT concepts are "measurement,""state," and "observable". The elements of a psychological experiment aretrial, stimulus, instructions, questions, and responses.
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This note is a discussion commenting on the paper by Ly et al. on "HaroldJeffreys's Default Bayes Factor Hypothesis Tests: Explanation, Extension, andApplication in Psychology" and on the perceived shortcomings of the classicalBayesian approach to testing, while reporting on an alternative approachadvanced by Kamary, Mengersen, Robert and Rousseau (2014. arxiv:1412.2044) as asolution to this quintessential inference problem.
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A recent study of the replicability of key psychological findings is a majorcontribution toward understanding the human side of the scientific process.Despite the careful and nuanced analysis reported in the paper, mass and socialmedia adhered to the simple narrative that only 36% of the studies replicatedtheir original results. Here we show that 77% of the replication effect sizesreported were within a prediction interval based on the original effect size.In this light, the results of Reproducibility Project: Psychology can be viewedas a positive result for the scientific process.
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This paper presents two models that exemplify psychological factors as adeterminant and as a consequence of social network characteristics. There is anendogeneity considered in network formation: while the social experiences haveimpacts on people, their current psychological states and traits affect networkevolution. The first model is an agent-based model over Bianconi-Barabasinetworks, used to explain the relation between network size, extroversion, andage of individuals. The second model deals with the emergence of urban tribesas a consequence of a smaller propensity to communicate with different withdifferent traits and opinions.
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In this paper, we present a Virtual-Suspect system which can be used to traininexperienced law enforcement personnel in interrogation strategies. The systemsupports different scenario configurations based on historical data. Theresponses presented by the Virtual-Suspect are selected based on thepsychological state of the suspect, which can be configured as well.Furthermore, each interrogator's statement affects the Virtual-Suspect'scurrent psychological state, which may lead the interrogation in differentdirections. In addition, the model takes into account the context in which thestatements are made. Experiments with 24 subjects demonstrate that theVirtual-Suspect's behavior is similar to that of a human who plays the role ofthe suspect.
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Natural language processing techniques are increasingly applied to identifysocial trends and predict behavior based on large text collections. Existingmethods typically rely on surface lexical and syntactic information. Yet,research in psychology shows that patterns of human conceptualisation, such asmetaphorical framing, are reliable predictors of human expectations anddecisions. In this paper, we present a method to learn patterns of metaphoricalframing from large text collections, using statistical techniques. We apply themethod to data in three different languages and evaluate the identifiedpatterns, demonstrating their psychological validity.
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We present a real-time algorithm, SocioSense, for socially-aware navigationof a robot amongst pedestrians. Our approach computes time-varying behaviors ofeach pedestrian using Bayesian learning and Personality Trait theory. Thesepsychological characteristics are used for long-term path prediction andgenerating proximic characteristics for each pedestrian. We combine thesepsychological constraints with social constraints to perform human-aware robotnavigation in low- to medium-density crowds. The estimation of time-varyingbehaviors and pedestrian personalities can improve the performance of long-termpath prediction by 21%, as compared to prior interactive path predictionalgorithms. We also demonstrate the benefits of our socially-aware navigationin simulated environments with tens of pedestrians.
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In the early 2010s, a crisis of reproducibility rocked the field ofpsychology. Following a period of reflection, the field has responded withradical reform of its scientific practices. More recently, similar questionsabout the reproducibility of machine learning research have also come to thefore. In this short paper, we present select ideas from psychology'sreformation, translating them into relevance for a machine learning audience.
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We quantify the impact of COVID-19 vaccination on psychological well-beingusing information from a large-scale panel survey representative of the UKpopulation. Exploiting exogenous variation in the timing of vaccinations, wefind that vaccination increases psychological well-being (GHQ-12) by 0.12standard deviation, compensating for around one-half of the overall decreasecaused by the pandemic. This effect persists for at least two months, and it isassociated with a decrease in the perceived likelihood of contracting COVID-19and higher engagement in social activities. The improvement is 1.5 times largerfor mentally distressed individuals, supporting the prioritization of thisgroup in vaccination roll-outs.
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How accurately can behavioral scientists predict behavior? To answer thisquestion, we analyzed data from five studies in which 640 professionalbehavioral scientists predicted the results of one or more behavioral scienceexperiments. We compared the behavioral scientists' predictions to randomchance, linear models, and simple heuristics like "behavioral interventionshave no effect" and "all published psychology research is false." We find thatbehavioral scientists are consistently no better than - and often worse than -these simple heuristics and models. Behavioral scientists' predictions are notonly noisy but also biased. They systematically overestimate how wellbehavioral science "works": overestimating the effectiveness of behavioralinterventions, the impact of psychological phenomena like time discounting, andthe replicability of published psychology research.
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Recently foundational issues of applicability of the formalism of quantummechanics (QM) to cognitive psychology, decision making, and psychophysicsattracted a lot of interest. In particular, in \cite{DKBB} the possibility touse of the projection postulate and representation of "mental observables" byHermitian operators was discussed in very detail. The main conclusion of therecent discussions on the foundations of "quantum(-like) cognitive psychology"is that one has to be careful in determination of conditions of applicabilityof the projection postulate as a mathematical tool for description ofmeasurements of observables represented by Hermitian operators. To representsome statistical experimental data (both physical and mental) in thequantum(-like) way, one has to use generalized quantum observables given bypositive operator-valued measures (POVMs). This paper contains a brief reviewon POVMs which can be useful for newcomers to the field of quantum(-like)studies. Especially interesting for cognitive psychology is a variant of theformula of total probability (FTP) with the interference term derived forincompatible observables given by POVMs. We present an interpretation of theinterference term from the psychological viewpoint. As was shown before, theappearance of such a term (perturbing classical FTP) plays the important rolein cognitive psychology, e.g., recognition of ambiguous figures and thedisjunction effect. The interference term for observables given by POVMs hasmuch more complicated structure than the corresponding term for observablesgiven by Hermitian operators. We elaborate cognitive interpretations ofdifferent components of the POVMs-interference term and apply our analysis to aquantum(-like) model of decision making.
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In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU),which was officially implemented in 2020. During this period, UK residentsexperienced a great deal of uncertainty around the UK's continued relationshipwith the EU. Many people have used social media platforms to express theiremotions about this critical event. Sentiment analysis has been recentlyconsidered as an important tool for detecting mental well-being in Twittercontents. However, detecting the psychological distress status inpolitical-related tweets is a challenging task due to the lack of explicitsentences describing the depressive or anxiety status. To address this problem,this paper leverages a transfer learning approach for sentiment analysis tomeasure the non-clinical psychological distress status in Brexit tweets. Theframework transfers the knowledge learnt from self-reported psychologicaldistress tweets (source domain) to detect the distress status in Brexit tweets(target domain). The framework applies a domain adaptation technique todecrease the impact of negative transfer between source and target domains. Thepaper also introduces a Brexit distress index that can be used to detect levelsof psychological distress of individuals in Brexit tweets. We design anexperiment that includes data from both domains. The proposed model is able todetect the non-clinical psychological distress status in Brexit tweets with anaccuracy of 66% and 62% on the source and target domains, respectively.
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For machine agents to successfully interact with humans in real-worldsettings, they will need to develop an understanding of human mental life.Intuitive psychology, the ability to reason about hidden mental variables thatdrive observable actions, comes naturally to people: even pre-verbal infantscan tell agents from objects, expecting agents to act efficiently to achievegoals given constraints. Despite recent interest in machine agents that reasonabout other agents, it is not clear if such agents learn or hold the corepsychology principles that drive human reasoning. Inspired by cognitivedevelopment studies on intuitive psychology, we present a benchmark consistingof a large dataset of procedurally generated 3D animations, AGENT (Action,Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goalpreferences, action efficiency, unobserved constraints, and cost-rewardtrade-offs) that probe key concepts of core intuitive psychology. We validateAGENT with human-ratings, propose an evaluation protocol emphasizinggeneralization, and compare two strong baselines built on Bayesian inverseplanning and a Theory of Mind neural network. Our results suggest that to passthe designed tests of core intuitive psychology at human levels, a model mustacquire or have built-in representations of how agents plan, combining utilitycomputations and core knowledge of objects and physics.
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We describe the links between group theory and psychology, in particularthrough the works of Piaget. We show that groups appear universally in hisdescription of children's intelligence, and that the notion of groupoid, whichwas little considered in psychology, may be fundamental. We study in particularthe applicability of group theory concepts to the development of educativegames.
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Corroboration or confirmation is a prominent philosophical debate of the 20thcentury. Many philosophers have been involved in this debate most notably theproponents of confirmation led by Hempel and its most powerful criticism by thefalsificationists led by Popper. In both cases however the debates wereprimarily based on the arguments from logic. In this paper we review thesedebates and suggest that a different perspective on falsification versusconfirmation can be taken by grounding arguments in cognitive psychology.
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Participant needs to achieve a given power are frequently underestimated.This is particularly problematic when effect sizes are small, such as is commonin neuroscience and psychology. We provide tools to make these demandsimmediately obvious in the form of a powerscape visualization.
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To well understand crowd behavior, microscopic models have been developed inrecent decades, in which an individual's behavioral/psychological status can bemodeled and simulated. A well-known model is the social-force model innovatedby physical scientists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek,2000; Helbing et al., 2002). This model has been widely accepted and mainlyused in simulation of crowd evacuation in the past decade. A problem, however,is that the testing results of the model were not explained in consistency withthe psychological findings, resulting in misunderstanding of the model bypsychologists. This paper will bridge the gap between psychological studies andphysical explanation about this model. We reinterpret this physics-based modelfrom a psychological perspective, clarifying that the model is consistent withpsychological theories on stress, including time-related stress andinterpersonal stress. Based on the conception of stress, we renew the model atboth micro-and-macro level, referring to multi-agent simulation in amicroscopic sense and fluid-based analysis in a macroscopic sense. Thecognition and behavior of individual agents are critically modeled as responseto environmental stimuli. Existing simulation results such as faster-is-slowereffect will be reinterpreted by Yerkes-Dodson law, and herding and groupingeffect as well as oscillation phenomenon are further discussed for pedestriancrowd. In brief the social-force model exhibits a bridge between the physicslaws and psychological principles regarding crowd motion, and this paper willrenew and reinterpret the model on the foundation of psychological studies.
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In this paper, we study the psychological effect in a SIS epidemic model. Thebasic reproduction number is obtained. However, the disease free equilibrium isalways asymptotically stable, which doesn't depends on the basic reproductionnumber. The system has a saddle-node bifurcation appear and displays bistablebehavior, which is a new phenomenon in epidemic dynamics and different from thebackward bifurcation behavior.
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The first quantitative neural network model of feelings and emotions isproposed on the base of available data on their neuroscience and evolutionarybiology nature, and on a neural network human memory model which admitsdistinct description of conscious and unconscious mental processes in a timedependent manner. As an example, proposed model is applied to quantitativedescription of the feeling of knowing.
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We try to perform geometrization of cognitive science and psychology byrepresenting information states of cognitive systems by points of {\it mentalspace} given by a hierarchic $m$-adic tree. Associations are represented byballs and ideas by collections of balls. We consider dynamics of ideas based onlifting of dynamics of mental points. We apply our dynamical model for modelingof flows of unconscious and conscious information in the human brain. In seriesof models, Models 1-3, we consider cognitive systems with increasing complexityof psychological behavior determined by structure of flows of associations andideas.
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We present a new variant of the V\"axj\"o interpretation: contextualisticstatistical realistic. Basic ideas of the V\"axj\"o interpretation-2001 areessentially clarified. We also discuss applications to biology, psychology,sociology, economy,...
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In this paper we explore the use of Answer Set Programming (ASP) toformalize, and reason about, psychological knowledge. In the field ofpsychology, a considerable amount of knowledge is still expressed using onlynatural language. This lack of a formalization complicates accurate studies,comparisons, and verification of theories. We believe that ASP, a knowledgerepresentation formalism allowing for concise and simple representation ofdefaults, uncertainty, and evolving domains, can be used successfully for theformalization of psychological knowledge. To demonstrate the viability of ASPfor this task, in this paper we develop an ASP-based formalization of themechanics of Short-Term Memory. We also show that our approach can have ratherimmediate practical uses by demonstrating an application of our formalizationto the task of predicting a user's interaction with a graphical interface.
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This paper presents our investigations on emotional state categorization fromspeech signals with a psychologically inspired computational model againsthuman performance under the same experimental setup. Based on psychologicalstudies, we propose a multistage categorization strategy which allowsestablishing an automatic categorization model flexibly for a given emotionalspeech categorization task. We apply the strategy to the Serbian EmotionalSpeech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where humanperformance was reported in previous psychological studies. Our work is thefirst attempt to apply machine learning to the GEES corpus where the humanrecognition rates were only available prior to our study. Unlike the previouswork on the DES corpus, our work focuses on a comparison to human performanceunder the same experimental settings. Our studies suggest thatpsychology-inspired systems yield behaviours that, to a great extent, resemblewhat humans perceived and their performance is close to that of humans underthe same experimental setup. Furthermore, our work also uncovers somedifferences between machine and humans in terms of emotional state recognitionfrom speech.
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We suggest a procedure that is relevant both to electronic performance andhuman psychology, so that the creative logic and the respect for human natureappear in a good agreement. The idea is to create an electronic card containingbasic information about a person's psychological behavior in order to make itpossible to quickly decide about the suitability of one for another. This"psychological electronics" approach could be tested via student projects.
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Suicide is among the leading causes of death in China. However, technicalapproaches toward preventing suicide are challenging and remaining underdevelopment. Recently, several actual suicidal cases were preceded by users whoposted microblogs with suicidal ideation to Sina Weibo, a Chinese social medianetwork akin to Twitter. It would therefore be desirable to detect suicidalideations from microblogs in real-time, and immediately alert appropriatesupport groups, which may lead to successful prevention. In this paper, wepropose a real-time suicidal ideation detection system deployed over Weibo,using machine learning and known psychological techniques. Currently, we haveidentified 53 known suicidal cases who posted suicide notes on Weibo prior totheir deaths.We explore linguistic features of these known cases using apsychological lexicon dictionary, and train an effective suicidal Weibo postdetection model. 6714 tagged posts and several classifiers are used to verifythe model. By combining both machine learning and psychological knowledge, SVMclassifier has the best performance of different classifiers, yielding anF-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.
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Global recruitment into radical Islamic movements has spurred renewedinterest in the appeal of political extremism. Is the appeal a rationalresponse to material conditions or is it the expression of psychological andpersonality disorders associated with aggressive behavior, intolerance,conspiratorial imagination, and paranoia? Empirical answers using surveys havebeen limited by lack of access to extremist groups, while field studies havelacked psychological measures and failed to compare extremists with contrastgroups. We revisit the debate over the appeal of extremism in the U.S. contextby comparing publicly available Twitter messages written by over 355,000political extremist followers with messages written by non-extremist U.S.users. Analysis of text-based psychological indicators supports the moralfoundation theory which identifies emotion as a critical factor in determiningpolitical orientation of individuals. Extremist followers also differ fromothers in four of the Big Five personality traits.
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Actionable analytics are those that humans can understand, andoperationalize. What kind of data mining models generate such actionableanalytics? According to psychological scientists, humans understand models thatmost match their own internal models, which they characterize as lists of"heuristic" (i.e., lists of very succinct rules). One such heuristic rulegenerator is the Fast-and-Frugal Trees (FFT) preferred by psychologicalscientists. Despite their successful use in many applied domains, FFTs have notbeen applied in software analytics. Accordingly, this paper assesses FFTs forsoftware analytics.  We find that FFTs are remarkably effective. Their models are very succinct (5lines or less describing a binary decision tree). These succinct modelsoutperform state-of-the-art defect prediction algorithms defined by Ghortra etal. at ICSE'15. Also, when we restrict training data to operational attributes(i.e., those attributes that are frequently changed by developers), FFTsperform much better than standard learners.  Our conclusions are two-fold. Firstly, there is much that software analyticscommunity could learn from psychological science. Secondly, proponents ofcomplex methods should always baseline those methods against simpleralternatives. For example, FFTs could be used as a standard baseline learneragainst which other software analytics tools are compared.
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The cognitive framework of conceptual spaces bridges the gap between symbolicand subsymbolic AI by proposing an intermediate conceptual layer whereknowledge is represented geometrically. There are two main approaches forobtaining the dimensions of this conceptual similarity space: using similarityratings from psychological experiments and using machine learning techniques.In this paper, we propose a combination of both approaches by usingpsychologically derived similarity ratings to constrain the machine learningprocess. This way, a mapping from stimuli to conceptual spaces can be learnedthat is both supported by psychological data and allows generalization tounseen stimuli. The results of a first feasibility study support our proposedapproach.
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Starting with the idea that sentiment analysis models should be able topredict not only positive or negative but also other psychological states of aperson, we implement a sentiment analysis model to investigate the relationshipbetween the model and emotional state. We first examine psychologicalmeasurements of 64 participants and ask them to write a book report about astory. After that, we train our sentiment analysis model using crawled moviereview data. We finally evaluate participants' writings, using the pretrainedmodel as a concept of transfer learning. The result shows that sentimentanalysis model performs good at predicting a score, but the score does not haveany correlation with human's self-checked sentiment.
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Psychological models are increasingly being used to explain online behavioraltraces. Aside from the commonly used personality traits as a general usermodel, more domain dependent models are gaining attention. The use of domaindependent psychological models allows for more fine-grained identification ofbehaviors and provide a deeper understanding behind the occurrence of thosebehaviors. Understanding behaviors based on psychological models can provide anadvantage over data-driven approaches. For example, relying on psychologicalmodels allow for ways to personalize when data is scarce. In this preliminarywork we look at the relation between users' musical sophistication and theironline music listening behaviors and to what extent we can successfully predictmusical sophistication. An analysis of data from a study with 61 participantsshows that listening behaviors can successfully be used to infer users' musicalsophistication.
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Prediction rule ensembles (PREs) are a relatively new statistical learningmethod, which aim to strike a balance between predictive accuracy andinterpretability. Starting from a decision tree ensemble, like a boosted treeensemble or a random forest, PREs retain a small subset of tree nodes in thefinal predictive model. These nodes can be written as simple rules of the formif [condition] then [prediction]. As a result, PREs are often much less complexthan full decision tree ensembles, while they have been found to providesimilar predictive accuracy in many situations. The current paper introducesthe methodology and shows how PREs can be fitted using the R package prethrough several real-data examples from psychological research. The examplesalso illustrate a number of features of package \textbf{pre} that may beparticularly useful for applications in psychology: support for categorical,multivariate and count responses, application of (non-)negativity constraints,inclusion of confirmatory rules and standardized variable importance measures.
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Empirical studies form an integral part of visualization research. Not onlycan they facilitate the evaluation of various designs, techniques, systems, andpractices in visualization, but they can also enable the discovery of thecausalities explaining why and how visualization works. This state-of-the-artreport focuses on controlled and semi-controlled empirical studies conducted inlaboratories and crowd-sourcing environments. In particular, the surveyprovides a taxonomic analysis of over 129 empirical studies in thevisualization literature. It juxtaposes these studies with topic developmentsbetween 1978 and 2017 in psychology, where controlled empirical studies haveplayed a predominant role in research. To help appreciate this broad context,the paper provides two case studies in detail, where specificvisualization-related topics were examined in the discipline of psychology aswell as the field of visualization. Following a brief discussion on some latestdevelopments in psychology, it outlines challenges and opportunities in makingnew discoveries about visualization through empirical studies.
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Why are we good? Why are we bad? Questions regarding the evolution ofmorality have spurred an astoundingly large interdisciplinary literature. Somesignificant subset of this body of work addresses questions regarding our moralpsychology: how did humans evolve the psychological properties which underpinour systems of ethics and morality? Here I do three things. First, I discusssome methodological issues, and defend particularly effective methods foraddressing many research questions in this area. Second, I give an in-depthexample, describing how an explanation can be given for the evolution ofguilt---one of the core moral emotions---using the methods advocated here.Last, I lay out which sorts of strategic scenarios generally are the ones thatour moral psychology evolved to `solve', and thus which models are the mostuseful in further exploring this evolution.
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Challenge Theory (CT), a new approach to decision under risk departssignificantly from expected utility, and is based on firmly psychological,rather than economic, assumptions. The paper demonstrates that a purelycognitive-psychological paradigm for decision under risk can yield excellentpredictions, comparable to those attained by more complex economic orpsychological models that remain attached to conventional economic constructsand assumptions. The study presents a new model for predicting the popularityof choices made in binary risk problems. A CT-based regression model is testedon data gathered from 126 respondents who indicated their preferences withrespect to 44 choice problems. Results support CT's central hypothesis,strongly associating between the Challenge Index (CI) attributable to everybinary risk problem, and the observed popularity of the bold prospect in thatproblem (with r=-0.92 and r=-0.93 for gains and for losses, respectively). Thenovelty of the CT perspective as a new paradigm is illuminated by its simple,single-index (CI) representation of psychological effects proposed by ProspectTheory for describing choice behavior (certainty effect, reflection effect,overweighting small probabilities and loss aversion).
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Recently there have been numerous proposed solutions to the problem oflogical omniscience in doxastic and epistemic logic. Though these solutionsdisplay an impressive breadth of subtlety and motivation, the crux of theseapproaches seems to have a common theme-minor revisions around the ubiquitousKripke semantics-rooted approach. In addition, the psychological mechanisms atwork in and around both belief and knowledge have been left largely untouched.In this paper, we cut straight to the core of the problem of logicalomniscience, taking a psychologically-rooted approach, taking as bedrock the"quanta" of given percepts, qualia and cognitions, terming our approach "PQGlogic", short for percept, qualia, cognition logic. Building atop these quanta,we reach a novel semantics of belief, knowledge, in addition to a semantics forpsychological necessity and possibility. With these notions we arewell-equipped to not only address the problem of logical omniscience but tomore deeply investigate the psychical-logical nature of belief and knowledge.
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Hypothesis testing is a central statistical method in psychological researchand the cognitive sciences. While the problems of null hypothesis significancetesting (NHST) have been debated widely, few attractive alternatives exist. Inthis paper, we provide a tutorial on the Full Bayesian Significance Test (FBST)and the e-value, which is a fully Bayesian alternative to traditionalsignificance tests which rely on p-values. The FBST is an advancedmethodological procedure which can be applied to several areas. In thistutorial, we showcase with two examples of widely used statistical methods inpsychological research how the FBST can be used in practice, provideresearchers with explicit guidelines on how to conduct it and make availableR-code to reproduce all results. The FBST is an innovative method which hasclearly demonstrated to perform better than frequentist significance testing.However, to our best knowledge, it has not been used so far in thepsychological sciences and should be of wide interest to a broad range ofresearchers in psychology and the cognitive sciences.
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Reinforcement learning methods have recently been very successful atperforming complex sequential tasks like playing Atari games, Go and Poker.These algorithms have outperformed humans in several tasks by learning fromscratch, using only scalar rewards obtained through interaction with theirenvironment. While there certainly has been considerable independent innovationto produce such results, many core ideas in reinforcement learning are inspiredby phenomena in animal learning, psychology and neuroscience. In this paper, wecomprehensively review a large number of findings in both neuroscience andpsychology that evidence reinforcement learning as a promising candidate formodeling learning and decision making in the brain. In doing so, we construct amapping between various classes of modern RL algorithms and specific findingsin both neurophysiological and behavioral literature. We then discuss theimplications of this observed relationship between RL, neuroscience andpsychology and its role in advancing research in both AI and brain science.
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In recent years, trends towards studying simulated games have gained momentumin the fields of artificial intelligence, cognitive science, psychology, andneuroscience. The intersections of these fields have also grown recently, asresearchers increasing study such games using both artificial agents and humanor animal subjects. However, implementing games can be a time-consumingendeavor and may require a researcher to grapple with complex codebases thatare not easily customized. Furthermore, interdisciplinary researchers studyingsome combination of artificial intelligence, human psychology, and animalneurophysiology face additional challenges, because existing platforms aredesigned for only one of these domains. Here we introduce ModularObject-Oriented Games, a Python task framework that is lightweight, flexible,customizable, and designed for use by machine learning, psychology, andneurophysiology researchers.
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Psychological curiosity plays a significant role in human intelligence toenhance learning through exploration and information acquisition. In theArtificial Intelligence (AI) community, artificial curiosity provides a naturalintrinsic motivation for efficient learning as inspired by human cognitivedevelopment; meanwhile, it can bridge the existing gap between AI research andpractical application scenarios, such as overfitting, poor generalization,limited training samples, high computational cost, etc. As a result,curiosity-driven learning (CDL) has become increasingly popular, where agentsare self-motivated to learn novel knowledge. In this paper, we first present acomprehensive review on the psychological study of curiosity and summarize aunified framework for quantifying curiosity as well as its arousal mechanism.Based on the psychological principle, we further survey the literature ofexisting CDL methods in the fields of Reinforcement Learning, Recommendation,and Classification, where both advantages and disadvantages as well as futurework are discussed. As a result, this work provides fruitful insights forfuture CDL research and yield possible directions for further improvement.
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Despite rapidly-expanding academic and policy interest in the links betweennatural resource wealth and development failures (commonly referred to as theresource curse) little attention has been devoted to the psychology behind thephenomenon. Rent-seeking and excessive reliance on mineral revenues can beattributed largely to social psychology. Mineral booms (whether due to thediscovery of mineral reserves or to the drastic rise in commodity prices) startas positive income shocks that can subsequently evolve into influential andexpectation-changing public and media narratives; these lead consecutively tounrealistic demands that favor immediate consumption of accrued mineralrevenues and to the postponement of productive investment. To our knowledge,this paper is the first empirical analysis that tests hypotheses regarding thepsychological underpinnings of resource mismanagement in mineral-rich states.Our study relies on an extensive personal survey (of 1977 respondents) carriedout in Almaty, Kazakhstan, between May and August 2018. We find empiricalsupport for a positive link between exposure to news and inflated expectationsregarding mineral availability, as well as evidence that the latter cangenerate preferences for excessive consumption, and hence, rent-seeking.
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Publication selection bias undermines the systematic accumulation ofevidence. To assess the extent of this problem, we survey over 26,000meta-analyses containing more than 800,000 effect size estimates from medicine,economics, and psychology. Our results indicate that meta-analyses in economicsare the most severely contaminated by publication selection bias, closelyfollowed by meta-analyses in psychology, whereas meta-analyses in medicine arecontaminated the least. The median probability of the presence of an effect ineconomics decreased from 99.9% to 29.7% after adjusting for publicationselection bias. This reduction was slightly lower in psychology (98.9%$\xrightarrow{}$ 55.7%) and considerably lower in medicine (38.0%$\xrightarrow{}$ 27.5%). The high prevalence of publication selection biasunderscores the importance of adopting better research practices such aspreregistration and registered reports.
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This study puts forward a conceptual model linking interpersonal influences'impact on Employee Engagement, Psychological contracts, and Human ResourcePractices. It builds on human and social capital, as well as the socialexchange theory (SET), projecting how interpersonal influences can impact thepsychological contract (PC) and employee engagement (EE) of employees. Thisresearch analyzes the interpersonal influences of Wasta in the Middle East,Guanxi in China, Jeitinho in Brazil, Blat in Russia, and Pulling Strings inEngland. Interpersonal influences draw upon nepotism, favoritism, andcorruption in organizations in many countries. This paper draws on thequalitative methods of analyzing previous theories. It uses the Model Papermethod of predicting relationships by examining the question of how dointerpersonal influences impact employee engagement and psychologicalcontract?. It is vital to track the effects of interpersonal influences on PCand EE, acknowledging that the employer can either empower or disengage ourhuman capital.
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Recent years have seen an emergence of network modeling applied to moods,attitudes, and problems in the realm of psychology. In this framework,psychological variables are understood to directly affect each other ratherthan being caused by an unobserved latent entity. In this tutorial, weintroduce the reader to estimating the most popular network model forpsychological data: the partial correlation network. We describe howregularization techniques can be used to efficiently estimate a parsimoniousand interpretable network structure in psychological data. We show how toperform these analyses in R and demonstrate the method in an empirical exampleon post-traumatic stress disorder data. In addition, we discuss the effect ofthe hyperparameter that needs to be manually set by the researcher, how tohandle non-normal data, how to determine the required sample size for a networkanalysis, and provide a checklist with potential solutions for problems thatcan arise when estimating regularized partial correlation networks.
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Until now mean-field-type game theory was not focused oncognitively-plausible models of choices in humans, animals, machines, robots,software-defined and mobile devices strategic interactions. This work presentssome effects of users' psychology in mean-field-type games. In addition to thetraditional "material" payoff modelling, psychological patterns are introducedin order to better capture and understand behaviors that are observed inengineering practice or in experimental settings. The psychological payoffvalue depends upon choices, mean-field states, mean-field actions, empathy andbeliefs. It is shown that the affective empathy enforces mean-field equilibriumpayoff equity and improves fairness between the players. It establishesequilibrium systems for such interactive decision-making problems. Basicempathy concepts are illustrated in several important problems in engineeringincluding resource sharing, packet collision minimization, energy markets, andforwarding in Device-to-Device communications. The work conducts also anexperiment with 47 people who have to decide whether to cooperate or not. Thebasic Interpersonal Reactivity Index of empathy metrics were used to measurethe empathy distribution of each participant. Android app called Empathizer isdeveloped to analyze systematically the data obtained from the participants.The experimental results reveal that the dominated strategies of the classicalgame theory are not dominated any more when users' psychology is involved, anda significant level of cooperation is observed among the users who arepositively partially empathetic.
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In this paper, the influence of fan-shaped buffer zone on the performance ofthe toll plaza is researched. A two-dimensional traffic flow model and acomprehensive evaluation model based on mechanical model and psychologicalfield are established. The traffic flow model is simulated by creatingcoordinate system.  We first establish queue theory model to analyze vehicles when entering tollplaza. Then, a two-dimensional steadily car-following model is establishedbased on psychological field for the analysis of vehicles when leaving tollplaza. According to psychological field theory, we analyze the force conditionof each vehicle. The force of each vehicle is contributed by the vehicles inits observation area and obstacles. By projecting these vehicles and obstaclesvia the equipotential line in the psychological field, the influence on thevalue and direction acceleration of following vehicles is obtained.Consequently, the changes of each vehicle's speed and position are obtained aswell. Next, we establish simulation based on the states of vehicles and makethe rules of vehicle state-changing. By simulating the system, we obtain thethroughput of the toll plaza's input and output. Then we obtained the bearingpressure on the road by the max throughput and the demand of the roads. Usingthe number of cars in per unit area as the safety factor. Then a comprehensiveevaluation model is established based on bearing pressure on the road, cost andsafety factor.
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Users in Online Social Networks (OSN) leaves traces that reflect theirpersonality characteristics. The study of these traces is important for anumber of fields, such as a social science, psychology, OSN, marketing, andothers. Despite a marked increase on research in personality prediction onbased on online behavior the focus has been heavily on individual personalitytraits largely neglecting relational facets of personality. This study aims toaddress this gap by providing a prediction model for a holistic personalityprofiling in OSNs that included socio-relational traits (attachmentorientations) in combination with standard personality traits. Specifically, wefirst designed a feature engineering methodology that extracts a wide range offeatures (accounting for behavior, language, and emotions) from OSN accounts ofusers. Then, we designed a machine learning model that predicts scores for thepsychological traits of the users based on the extracted features. The proposedmodel architecture is inspired by characteristics embedded in psychologicaltheory, i.e, utilizing interrelations among personality facets, and leads toincreased accuracy in comparison with the state of the art approaches. Todemonstrate the usefulness of this approach, we applied our model to twodatasets, one of random OSN users and one of organizational leaders, andcompared their psychological profiles. Our findings demonstrate that the twogroups can be clearly separated by only using their psychological profiles,which opens a promising direction for future research on OSN usercharacterization and classification.
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A conceptual system with rich connotation is key to improving the performanceof knowledge-based artificial intelligence systems. While a conceptual system,which has abundant concepts and rich semantic relationships, and isdevelopable, evolvable, and adaptable to multi-task environments, its actualconstruction is not only one of the major challenges of knowledge engineering,but also the fundamental goal of research on knowledge and conceptualization.Finding a new method to represent concepts and construct a conceptual systemwill therefore greatly improve the performance of many intelligent systems.Fortunately the core of human cognition is a system with relatively completeconcepts and a mechanism that ensures the establishment and development of thesystem. The human conceptual system can not be achieved immediately, but rathermust develop gradually. Developmental psychology carefully observes the processof concept acquisition in humans at the behavioral level, and along withcognitive psychology has proposed some rough explanations of thoseobservations. However, due to the lack of research in aspects such asrepresentation, systematic models, algorithm details and realization, many ofthe results of developmental psychology have not been applied directly to thebuilding of artificial conceptual systems. For example, Karmiloff-Smith'sRepresentation Redescription (RR) supposition reflects a concept-acquisitionprocess that re-describes a lower level representation of a concept to a higherone. This paper is inspired by this developmental psychology viewpoint. We usean object-oriented approach to re-explain and materialize RR supposition fromthe formal semantic perspective, because the OO paradigm is a natural way todescribe the outside world, and it also has strict grammar regulations.
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Support agents that help users in their daily lives need to take into accountnot only the user's characteristics, but also the social situation of the user.Existing work on including social context uses some type of situation cue as aninput to information processing techniques in order to assess the expectedbehavior of the user. However, research shows that it is important to alsodetermine the meaning of a situation, a step which we refer to as socialsituation comprehension. We propose using psychological characteristics ofsituations, which have been proposed in social science for ascribing meaning tosituations, as the basis for social situation comprehension. Using data fromuser studies, we evaluate this proposal from two perspectives. First, from atechnical perspective, we show that psychological characteristics of situationscan be used as input to predict the priority of social situations, and thatpsychological characteristics of situations can be predicted from the featuresof a social situation. Second, we investigate the role of the comprehensionstep in human-machine meaning making. We show that psychologicalcharacteristics can be successfully used as a basis for explanations given tousers about the decisions of an agenda management personal assistant agent.
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The simplest field theory description of the multivariate statistics offorward rate variations over time and maturities, involves a quadratic actioncontaining a gradient squared rigidity term. However, this choice leads to aspurious kink (infinite curvature) of the normalized correlation function forcoinciding maturities. Motivated by empirical results, we consider an extendedaction that contains a squared Laplacian term, which describes the bendingstiffness of the FRC. With the extra ingredient of a `psychological' futuretime, describing how the perceived time between events depends on the time inthe future, our theory accounts extremely well for the phenomenology ofinterest rate dynamics.
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This paper constructs a tree structure for the music rhythm using theL-system. It models the structure as an automata and derives its complexity. Italso solves the complexity for the L-system. This complexity can resolve thesimilarity between trees. This complexity serves as a measure of psychologicalcomplexity for rhythms. It resolves the music complexity of variouscompositions including the Mozart effect K488.  Keyword: music perception, psychological complexity, rhythm, L-system,automata, temporal associative memory, inverse problem, rewriting rule,bracketed string, tree similarity
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A non-technical argument is presented that there is a link between the mindand the physical world in modern physics. Special relativity, generalrelativity, quantum mechanics, statistical mechanics, and other areas ofphysics are explored. Each area is found to support the existence of a linkbetween the mind and the physical world. Research from psychology is alsopresented indicating that there is a role for the discipline of psychology indelineating the nature of this link.
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We develop a purely ordinal model for aggregation functionals for latticevalued functions, comprising as special cases quantiles, the Ky Fan metric andthe Sugeno integral. For modeling findings of psychological experiments likethe reflection effect in decision behaviour under risk or uncertainty, weintroduce reflection lattices. These are complete linear lattices endowed withan order reversing bijection like the reflection at 0 on the real interval$[-1,1]$. Mathematically we investigate the lattice of non-void intervals in acomplete linear lattice, then the class of monotone interval-valued functionsand their inner product.
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We argue that in Universes where future and past differ only by the entropycontent a psychological arrow of time pointing in the direction of entropyincrease can arise from natural selection in biological evolution. We show thatthis effect can be demonstrated in very simple toy computer simulations ofevolution in an entropy increasing or decreasing environment.
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More and more scientific research shows that there is a close correlationbetween the Internet and brain science. This paper presents the idea ofestablishing the Internet neurology, which means to make a cross-contrastbetween the two in terms of physiology and psychology, so that a completeinfrastructure system of the Internet is established, predicting thedevelopment trend of the Internet in the future as well as the brain structureand operation mechanism, and providing theoretical support for the generationprinciple of intelligence, cognition and emotion. It also proposes theviewpoint that the Internet can be divided into Internet neurophysiology,Internet neuropsychology, Brain Internet physiology, Brain Internet psychologyand the Internet in cognitive science.
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A demandance is a psychological "pull" exerted by a stimulus. It is closelyrelated to the theory of "affordance". I introduce the theory of demandance,offer some motivating examples, briefly explore its psychological basis, andexamine some implications of the theory. I exemplify some of the positive andnegative implications of demandances for design, with special attention toyoung children and the design of educational products and practices. I suggestthat demandance offers an approach to one of the persistent mysteries of thetheory of affordance, specifically: Given that there may be many affordances inany particular setting, how do we choose which to actually act upon?
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There has been a growing interest, both in physics and psychology, inunderstanding contextuality in experimentally observed quantities. Differentapproaches have been proposed to deal with contextual systems, and a promisingone is contextuality-by-default, put forth by Dzhafarov and Kujala. The goal ofthis paper is to present a tutorial on a different approach: negativeprobabilities. We do so by presenting the overall theory of negativeprobabilities in a way that is consistent with contextuality-by-default and byexamining with this theory some simple examples where contextuality appears,both in physics and psychology.
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The aim of this investigation was to establish the personality profile ofBrazilian software engineering students according to the MBTI. This study alsoshows that the software engineering field attracts students of some types morethan other types, for instance: Is, Ps, IPs, TPs, and INs are significantlyrepresented in that group as opposed to E, Js, EJs, TJs, ENs.
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We study the set of no-signalling empirical models on a measurement scenario,and show that the combinatorial structure of the no-signalling polytope iscompletely determined by the possibilistic information given by the support ofthe models. This is a special case of a general result which applies to allpolytopes presented in a standard form, given by linear equations together withnon-negativity constraints on the variables.
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In this paper we propose a hypothesis about how different uses of maintainingdragging, either as a physical tool in a dynamic geometry environment or as apsychological tool for generating conjectures can influence subsequentprocesses of proving. Through two examples we support the hypothesis that usingmaintaining dragging as a physical tool may foster cognitive rupture betweenthe conjecturing phase and the proof, while using it as a psychological toolmay foster cognitive unity between them.
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We show that standard Bayesian games cannot represent the full spectrum ofbelief-dependent preferences. However, by introducing a fundamental distinctionbetween intended and actual strategies, we remove this limitation. We defineBayesian games with intentions, generalizing both Bayesian games andpsychological games, and prove that Nash equilibria in psychological gamescorrespond to a special class of equilibria as defined in our setting.
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The association between light and psychological states has a long history andpermeates our language. LIVEIA (Light-based Immersive Visualization Environmentfor Imaginative Actualization) is a new immersive, interactive technology thatuses physical light as a metaphor for visualizing peoples' inner lives andrelationships. This paper outlines its educational value, as a tool forunderstanding and explaining aspects of how people think and interact, and itspotential therapeutic value as a form of art therapy in which the artwork hasstraightforwardly interpretable symbolic meanings.
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We present a formal measure of argument strength, which combines the ideasthat conclusions of strong arguments are (i) highly probable and (ii) theiruncertainty is relatively precise. Likewise, arguments are weak when theirconclusion probability is low or when it is highly imprecise. We show how theproposed measure provides a new model of the Ellsberg paradox. Moreover, wefurther substantiate the psychological plausibility of our approach by anexperiment (N = 60). The data show that the proposed measure predicts humaninferences in the original Ellsberg task and in corresponding argument strengthtasks. Finally, we report qualitative data taken from structured interviews onfolk psychological conceptions on what argument strength means.
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The thermodynamic arrow-of-time problem is thought to be resolved by theobservation that our universe initially was---and still is---far fromequilibrium. The psychological arrow-of-time problem is often attributed thesame resolution, but the connection has not been thoroughly established. Iargue that a compelling explanation of the psychological arrow requires anunderstanding of the physical conditions necessary for life to emerge from aprebiotic environment. A simple calculation illustrates how life-sustainingenergy fluxes from the Sun and the Earth's interior bias the development oflife in the direction of the arrow inherited from cosmic initial conditions.
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Understanding a narrative requires reading between the lines and reasoningabout the unspoken but obvious implications about events and people's mentalstates - a capability that is trivial for humans but remarkably hard formachines. To facilitate research addressing this challenge, we introduce a newannotation framework to explain naive psychology of story characters asfully-specified chains of mental states with respect to motivations andemotional reactions. Our work presents a new large-scale dataset with richlow-level annotations and establishes baseline performance on several newtasks, suggesting avenues for future research.
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The complexity of dynamics in AI techniques is already approaching that ofcomplex adaptive systems, thus curtailing the feasibility of formalcontrollability and reachability analysis in the context of AI safety. Itfollows that the envisioned instances of Artificial General Intelligence (AGI)will also suffer from challenges of complexity. To tackle such issues, wepropose the modeling of deleterious behaviors in AI and AGI as psychologicaldisorders, thereby enabling the employment of psychopathological approaches toanalysis and control of misbehaviors. Accordingly, we present a discussion onthe feasibility of the psychopathological approaches to AI safety, and proposegeneral directions for research on modeling, diagnosis, and treatment ofpsychological disorders in AGI.
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Recently Dzhafarov and Kon published the paper advertising the possibility touse the coupling technique of classical probability theory to modelincompatible observables in quantum physics and quantum-like models ofpsychology. Here I present comments on this paper by stressing advantages anddisadvantages.
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In high dimensional settings, density estimation algorithms rely crucially ontheir inductive bias. Despite recent empirical success, the inductive bias ofdeep generative models is not well understood. In this paper we propose aframework to systematically investigate bias and generalization in deepgenerative models of images. Inspired by experimental methods from cognitivepsychology, we probe each learning algorithm with carefully designed trainingdatasets to characterize when and how existing models generate novel attributesand their combinations. We identify similarities to human psychology and verifythat these patterns are consistent across commonly used models andarchitectures.
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The behavior coordinate system and the ideal individual model are presented.The behavior state of an ideal individual is assumed to be represented by abehavior state function. Based on the ideal individual model, the behaviorcoordinate system and the quantum probability, a novel quantum theory ofpsychology is offered here in a different way. It can give some enlighteningviewpoints through which some phenomena can be discussed from a differentperspective.
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This paper introduces and evaluates a novel training method for neuralnetworks: Dual Variable Learning Rates (DVLR). Building on insights frombehavioral psychology, the dual learning rates are used to emphasize correctand incorrect responses differently, thereby making the feedback to the networkmore specific. Further, the learning rates are varied as a function of thenetwork's performance, thereby making it more efficient. DVLR was implementedon three types of networks: feedforward, convolutional, and residual, and twodomains: MNIST and CIFAR-10. The results suggest a consistently improvedaccuracy, demonstrating that DVLR is a promising, psychologically motivatedtechnique for training neural network models.
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With the progressive digitalisation of a majority of services to communitiesand individuals, humankind is facing new challenges. While energy sources arerapidly dwindling and rigorous choices have to be made to ensure thesustainability of our environment, there is increasing concern in science andsociety about the safety of connected products and technology for theindividual user. This essay provides a first basis for further inquiry into therisks in terms of potentially negative, short and long-term, effects ofconnected technologies and massive digitalisation on the psychological and/orphysical abilities and well-being of users or consumers.
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On their way to an academic career in physics, Ph.D. students have toovercome difficulties at many levels. Beyond the intellectual challenge, thereare also psychological, social and economic barriers. We studied thedifficulties experienced by physics Ph.D. students in the Israeli universities,with special attention to gender-related issues. Among the hurdles that aremuch more significant for women than for men -- that we call ``the glasshurdles" -- we find gender-related discrimination, sexual harassment,physiological and psychological health issues, and challenges related topregnancy and parenthood. We make recommendations for ways to confront andremove these barriers in order to provide female physicists with an equalopportunity to succeed.
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What is a visualization? There is limited utility in trifling withdefinitions, except insofar as one serves as a tool for communicating andconceptualizing our subject matter; a statement of identity for a community. Toestablish Visualization Psychology as a viable inter-disciplinary researchprogramme, we must first define the object(s) of our collective inquiry. Ipropose that while we might refer to the study of "visualization" for theterm's colloquial accessibility and pragmatic alignment with other fields, weshould consider for exploration a class of artifacts and correspondingprocesses more expansive and profound: external representations. What followsis an argument for the study of external representation as the foundation for anew interdisciplinary endeavor, and approach to mapping the correspondingproblem space.
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As visualization researchers evaluate the impact of visualization design ondecision-making, they often hold a one-dimensional perspective on the cognitiveprocesses behind making a decision. Several psychological and economicalresearchers have shown that to make decisions, people rely on quantitativereasoning as well as gist-based intuition -- two systems that operate inparallel. In this position paper, we discuss decision theories and providesuggestions to bridge the gap between the evaluation of decision-making invisualization and psychology research. The goal is to question the limits ofour knowledge and to advocate for a more nuanced understanding ofdecision-making with visualization.
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In this paper we extend the analysis of an agent-based model for adaptivetrading, called asynchronous stochastic price pump (ASPP) introduced byPerepelitsa and Timofeyev (2019), to the model with heterogeneous distributionof psychological parameters of speculative optimism and pessimism across thepopulation of traders. We show that the new model has a range of qualitativelydifferent dynamics when the correlation between those factors ranges from lownegative to large positive values. A statistical parameter estimation suggestsa heterogeneous ASPP with negative correlation as a model of price variationsof Bitcoin.
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In this paper we explore whether the fundamental tool of experimentalpsychology, the behavioral experiment, has the power to generate insight notonly into humans and animals, but artificial systems too. We apply thetechniques of experimental psychology to investigating catastrophic forgettingin neural networks. We present a series of controlled experiments withtwo-layer ReLU networks, and exploratory results revealing a new understandingof the behavior of catastrophic forgetting. Alongside our empirical findings,we demonstrate an alternative, behavior-first approach to investigating neuralnetwork phenomena.
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The idea that memory behavior relies on a gradually-changing internal statehas a long history in mathematical psychology. This chapter traces this line ofthought from statistical learning theory in the 1950s, through distributedmemory models in the latter part of the 20th century and early part of the 21stcentury through to modern models based on a scale-invariant temporal history.We discuss the neural phenomena consistent with this form of representation andsketch the kinds of cognitive models that can be constructed using it andconnections with formal models of various memory tasks.
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The feeling of anxiety and loneliness among aging population has beenrecently amplified by the COVID-19 related lockdowns. Emotion-aware multimodalbot application combining voice and visual interface was developed to addressthe problem in the group of older citizens. The application is novel as itcombines three main modules: information, emotion selection and psychologicalintervention, with the aim of improving human well-being. The preliminary studywith target group confirmed that multimodality improves usability and that theinformation module is essential for participating in a psychologicalintervention. The solution is universal and can also be applied to areas notdirectly related to COVID-19 pandemic.
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This paper provides a detailed overview of a case study of applying ContinualLearning (CL) to a single-session Human-Robot Interaction (HRI) session (avg.31 +- 10 minutes), where a robotic mental well-being coach conducted PositivePsychology (PP) exercises with (n = 20) participants. We present the results ofa Thematic Analysis (TA) of data recorded from brief semi-structured interviewsthat were conducted with participants after the interaction sessions, as wellas an analysis of statistical results demonstrating how participants'personalities may affect how they perceive the robot and its interactions.
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Third-person fictional narrative text is composed not only of passages thatobjectively narrate events, but also of passages that present characters'thoughts, perceptions, and inner states. Such passages take a character's``psychological point of view''. A language understander must determine thecurrent psychological point of view in order to distinguish the beliefs of thecharacters from the facts of the story, to correctly attribute beliefs andother attitudes to their sources, and to understand the discourse relationsamong sentences. Tracking the psychological point of view is not a trivialproblem, because many sentences are not explicitly marked for point of view,and whether the point of view of a sentence is objective or that of a character(and if the latter, which character it is) often depends on the context inwhich the sentence appears. Tracking the psychological point of view is theproblem addressed in this work. The approach is to seek, by extensiveexaminations of naturally-occurring narrative, regularities in the ways thatauthors manipulate point of view, and to develop an algorithm that tracks pointof view on the basis of the regularities found. This paper presents thisalgorithm, gives demonstrations of an implemented system, and describes theresults of some preliminary empirical studies, which lend support to thealgorithm.
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The cognitive frame in which most neuropsychological research on the neuralbasis of behavior is conducted contains the assumption that brain mechanismsper se fully suffice to explain all psychologically described phenomena. Thisassumption stems from the idea that the brain is made up entirely of materialparticles and fields, and that all causal mechanisms must therefore beformulated solely in terms of properties of these elements. One consequence ofthis stance is that psychological terms having intrinsic mentalistic and/orexperiential content (terms such as "feeling," "knowing," and "effort") havenot been included as primary causal factors in neuropsychological research:insofar as properties are not described in material terms they are deemedirrelevant to the causal mechanisms underlying brain function. However, theorigin of this demand that experiential realities be excluded from the causalbase is a theory of nature that has been known for more that three quarters ofa century to be fundamentally incorrect. It is explained here why it isconsequently scientifically unwarranted to assume that material factors alonecan in principle explain all causal mechanisms relevant to neuroscience. Moreimportantly, it is explained how a key quantum effect can be introduced intobrain dynamics in a simple and practical way that provides a rationallycoherent, causally formulated, physics-based way of understanding and using thepsychological and physical data derived from the growing set of studies of thecapacity of directed attention and mental effort to systematically alter brainfunction.
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We prove a theorem which shows that a collection of experimental data ofprobabilistic weights related to decisions with respect to situations and theirdisjunction cannot be modeled within a classical probabilistic weight structurein case the experimental data contain the effect referred to as the'disjunction effect' in psychology. We identify different experimentalsituations in psychology, more specifically in concept theory and in decisiontheory, and in economics (namely situations where Savage's Sure-Thing Principleis violated) where the disjunction effect appears and we point out the commonnature of the effect. We analyze how our theorem constitutes a no-go theoremfor classical probabilistic weight structures for common experimental data whenthe disjunction effect is affecting the values of these data. We put forward asimple geometric criterion that reveals the non classicality of the consideredprobabilistic weights and we illustrate our geometrical criterion by means ofexperimentally measured membership weights of items with respect to pairs ofconcepts and their disjunctions. The violation of the classical probabilisticweight structure is very analogous to the violation of the well-known Bellinequalities studied in quantum mechanics. The no-go theorem we prove in thepresent article with respect to the collection of experimental data we considerhas a status analogous to the well known no-go theorems for hidden variabletheories in quantum mechanics with respect to experimental data obtained inquantum laboratories. For this reason our analysis puts forward a strongargument in favor of the validity of using a quantum formalism for modeling theconsidered psychological experimental data as considered in this paper.
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We present scientometric results about world-wide centers of excellence inpsychology. Based on Web of Science data, domain-specific excellence can beidentified for cities where highly cited papers are published. Data refer toall psychology articles published in 2007 which are documented in the SocialScience Citation Index and to their citation frequencies from 2007 to May 2011.Visualized are 214 cities with an article output of at least 50 in 2007.Statistical z tests are used for the evaluation of the degree to which anobserved number of top-cited papers (top-10%) for a city differs from thenumber expected on the basis of randomness in the selection of papers. Mapvisualizing city ratios on significant differences between observed andexpected numbers of highly-cited papers point at excellence centers in citiesat the East and West Coast of the United States as well as in Great Britain,Germany, the Netherlands, Ireland, Belgium, Sweden, Finland, Australia, andTaiwan. Furthermore, positive but non-significant differences in favor of highcitation rates are documented for some cities in the United States, GreatBritain, the Netherlands, the Scandinavian and the German-speaking countries,Belgium, France, Spain, Israel, South Korea, and China. Scientometric resultsshow convincingly that highly-cited psychological research articles come fromthe Anglo-American countries and some of the non-English European countries inwhich the number of English-language publications has increased during the lastdecades.
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Recently, in a letter to Nature, del Rio et al.8 exploited the quantumviewpoint of the old but well-known thought experiment of Maxwell's demon, atiny "man-machine" that processes only a single unit of information. In theirwork, they showed that the thermodynamic cost for Maxwell's demon to erasequantum information decreases as the amount it "knows" increases. Indeed, asthe authors themselves concluded, that finding has the ability to strengthenthe link between information theory and statistical physics. However, thefactual link between information theory and psychology remains unknown. Theremay be no better way to investigate to this issue than to subject this dualnatured creature to psychological treatment! In this work, we propose anAusubel-inspired ansatz to map the thermodynamic mind of Maxwell's demon,addressing information processing from a cognitive perspective9-12. The maincalculation presented in this short report shows that the Ausubelianassimilation theory13-15 leads to a Shannon-Hartley-like model1,2 that, inturn, converges exactly to the Landauer limit16-18 when one single informationis discarded from the demon's memory. This result indicates that both athermodynamic device and an intelligent being "think" in the same way when onebit of information is processed. Consequently, this finding links informationtheory to the "psychological features" of the thermodynamic engine through theLandauer limit, which opens a new path towards the conception of a multi-bitreasoning machine.
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For more than 30 years, it has been claimed that a way to improve softwaredevelopers' productivity and software quality is to focus on people and toprovide incentives to make developers satisfied and happy. This claim hasrarely been verified in software engineering research, which faces anadditional challenge in comparison to more traditional engineering fields:software development is an intellectual activity and is dominated byoften-neglected human aspects. Among the skills required for softwaredevelopment, developers must possess high analytical problem-solving skills andcreativity for the software construction process. According to psychologyresearch, affects-emotions and moods-deeply influence the cognitive processingabilities and performance of workers, including creativity and analyticalproblem solving. Nonetheless, little research has investigated the correlationbetween the affective states, creativity, and analytical problem-solvingperformance of programmers. This article echoes the call to employpsychological measurements in software engineering research. We report a studywith 42 participants to investigate the relationship between the affectivestates, creativity, and analytical problem-solving skills of softwaredevelopers. The results offer support for the claim that happy developers areindeed better problem solvers in terms of their analytical abilities. Thefollowing contributions are made by this study: (1) providing a betterunderstanding of the impact of affective states on the creativity andanalytical problem-solving capacities of developers, (2) introducing andvalidating psychological measurements, theories, and concepts of affectivestates, creativity, and analytical-problem-solving skills in empirical softwareengineering, and (3) raising the need for studying the human factors ofsoftware engineering by employing a multidisciplinary viewpoint.
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A general tension-reduction (GTR) model was recently considered to derivequantum probabilities as (universal) averages over all possible forms ofnon-uniform fluctuations, and explain their considerable success in describingexperimental situations also outside of the domain of physics, for instance inthe ambit of quantum models of cognition and decision. Yet, this result alsohighlighted the possibility of observing violations of the predictions of theBorn rule, in those situations where the averaging would not be large enough,or would be altered because of the combination of multiple measurements. Inthis article we show that this is indeed the case in typical psychologicalmeasurements exhibiting question order effects, by showing that theirstatistics of outcomes are inherently non-Hilbertian, and require the largerframework of the GTR-model to receive an exact mathematical description. Wealso consider another unsolved problem of quantum cognition: responsereplicability. It is has been observed that when question order effects andresponse replicability occur together, the situation cannot be handled anymoreby quantum theory. However, we show that it can be easily and naturallydescribed in the GTR-model. Based on these findings, we motivate the adoptionin cognitive science of a hidden-measurements interpretation of the quantumformalism, and of its GTR-model generalization, as the natural interpretationalframework explaining the data of psychological measurements on conceptualentities.
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Deep neural networks (DNNs) have achieved unprecedented performance on a widerange of complex tasks, rapidly outpacing our understanding of the nature oftheir solutions. This has caused a recent surge of interest in methods forrendering modern neural systems more interpretable. In this work, we propose toaddress the interpretability problem in modern DNNs using the rich history ofproblem descriptions, theories and experimental methods developed by cognitivepsychologists to study the human mind. To explore the potential value of thesetools, we chose a well-established analysis from developmental psychology thatexplains how children learn word labels for objects, and applied that analysisto DNNs. Using datasets of stimuli inspired by the original cognitivepsychology experiments, we find that state-of-the-art one shot learning modelstrained on ImageNet exhibit a similar bias to that observed in humans: theyprefer to categorize objects according to shape rather than color. Themagnitude of this shape bias varies greatly among architecturally identical,but differently seeded models, and even fluctuates within seeds throughouttraining, despite nearly equivalent classification performance. These resultsdemonstrate the capability of tools from cognitive psychology for exposinghidden computational properties of DNNs, while concurrently providing us with acomputational model for human word learning.
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The present communication addresses a set of observations, obeying bothdeterministic as well as statistical formal requirements, and serving tooperate within the framework of the dynamical systems theory, with a certainemphasis placed on initial data. It is argued that statistical approaches canmanifest themselves non unequivocally, leading to certain virtual discrepanciesin psychological and/or cognitive data analyses, termed sometimes in literatureas, questionable research practices. This communication points to the demandfor a deep awareness of the data origins, which can indicate whether theexponential (Malthus type) or the algebraic (Pareto type) statisticaldistribution ought to be effectively considered in practical interpretation.This is also related to the question of how frequently patients behave in aspecific way, and the significance of these behaviors in determining apatient's progression or regression, involving a certain memory effect. In thisperspective, it is discussed how a sensitively applied hazardous or triggeringfactor can be helpful for well-controlled psychological strategic treatments,also those attributable to obsessive/compulsive disorders or evenself-injurious behaviors, with their both criticality and complexity exploitingrelations between a therapist and a patient.
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The problem of the electric field of a uniformly accelerating charge is alongstanding one that has led to several issues. We resolve these issues usingtechniques from linguistics, cognitive psychology, and the mathematics ofpartial differential equations.
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The Internet and, in particular, Online Social Networks have changed the waythat terrorist and extremist groups can influence and radicalise individuals.Recent reports show that the mode of operation of these groups starts byexposing a wide audience to extremist material online, before migrating them toless open online platforms for further radicalization. Thus, identifyingradical content online is crucial to limit the reach and spread of theextremist narrative. In this paper, our aim is to identify measures toautomatically detect radical content in social media. We identify severalsignals, including textual, psychological and behavioural, that together allowfor the classification of radical messages. Our contribution is three-fold: (1)we analyze propaganda material published by extremist groups and create acontextual text-based model of radical content, (2) we build a model ofpsychological properties inferred from these material, and (3) we evaluatethese models on Twitter to determine the extent to which it is possible toautomatically identify online radical tweets. Our results show that radicalusers do exhibit distinguishable textual, psychological, and behaviouralproperties. We find that the psychological properties are among the mostdistinguishing features. Additionally, our results show that textual modelsusing vector embedding features significantly improves the detection overTF-IDF features. We validate our approach on two experiments achieving highaccuracy. Our findings can be utilized as signals for detecting onlineradicalization activities.
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This paper evaluates the effects of being an only child in a family onpsychological health, leveraging data on the One-Child Policy in China. We usean instrumental variable approach to address the potential unmeasuredconfounding between the fertility decision and psychological health, where theinstrumental variable is an index on the intensity of the implementation of theOne-Child Policy. We establish an analytical link between the localinstrumental variable approach and principal stratification to accommodate thecontinuous instrumental variable. Within the principal stratificationframework, we postulate a Bayesian hierarchical model to infer various causalestimands of policy interest while adjusting for the clustering data structure.We apply the method to the data from the China Family Panel Studies and findsmall but statistically significant negative effects of being an only child onself-reported psychological health for some subpopulations. Our analysisreveals treatment effect heterogeneity with respect to both observed andunobserved characteristics. In particular, urban males suffer the most frombeing only children, and the negative effect has larger magnitude if thefamilies were more resistant to the One-Child Policy. We also conductsensitivity analysis to assess the key instrumental variable assumption.
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Intensive Longitudinal Data (ILD) is increasingly available to social andbehavioral scientists. With this increased availability come new opportunitiesfor modeling and predicting complex biological, behavioral, and physiologicalphenomena. Despite these new opportunities psychological researchers have nottaken full advantage of promising opportunities inherent to this data, thepotential to forecast psychological processes at the individual level. Toaddress this gap in the literature we present a novel modeling framework thataddresses a number of topical challenges and open questions in thepsychological literature on modeling dynamic processes. First, how can we modeland forecast ILD when the length of individual time series and the number ofvariables collected are roughly equivalent, or when time series lengths areshorter than what is typically required for time series analyses? Second, howcan we best take advantage of the cross-sectional (between-person) informationinherent to most ILD scenarios while acknowledging individuals differ bothquantitatively (e.g. in parameter magnitude) and qualitatively (e.g. instructural dynamics)? Despite the acknowledged between-person heterogeneity inmany psychological processes is it possible to leverage group-level informationto support improved forecasting at the individual level? In the remainder ofthe manuscript, we attempt to address these and other pressing questionsrelevant to the forecasting of multiple-subject ILD.
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As of July 31, 2020, the COVID-19 pandemic has over 17 million reportedcases, causing more than 667,000 deaths. Countries irrespective of economicstatus have succumbed to this pandemic. Many aspects of the lives, includinghealth, economy, freedom of movement have been negatively affected by thecoronavirus outbreak. Numerous strategies have been taken in order to preventthe outbreak. Some countries took severe resections in the form of full-scalelockdown, while others took a moderate approach of dealing with the pandemics,for example, mass testing, prohibiting large-scale public gatherings,restricting international travels. South America adopted primarily the lockdownstrategies due to inadequate economy and health care support. Since the socialinteractions between the people are primarily affected by the lockdown,psychological distress, e.g. anxiety, stress, fear are supposedly affecting theSouth American population in a severe way. This paper aims to explore theimpact of lockdown over the psychological aspect of the people of all theSpanish speaking South American capitals. We have utilized infodemiologyapproach by employing large-scale Twitter data-set over 33 million feeds inorder to understand people's interaction over the months of this on-goingcoronavirus pandemic. Our result is surprising: at the beginning of thepandemic, people demonstrated strong emotions (i.e. anxiety, worry, fear) whichdeclined over time even though the actual pandemic is worsening by having morepositive cases, and inflicting more deaths. This leads us to speculate that theSouth American population is adapting to this pandemic thus improving theoverall psychological distress.
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A significant number of college students suffer from mental health issuesthat impact their physical, social, and occupational outcomes. Various scalabletechnologies have been proposed in order to mitigate the negative impact ofmental health disorders. However, the evaluation for these technologies, ifdone at all, often reports mixed results on improving users' mental health. Weneed to better understand the factors that align a user's attributes and needswith technology-based interventions for positive outcomes. In psychotherapytheory, therapeutic alliance and rapport between a therapist and a client isregarded as the basis for therapeutic success. In prior works, social robotshave shown the potential to build rapport and a working alliance with users invarious settings. In this work, we explore the use of a social robot coach todeliver positive psychology interventions to college students living inon-campus dormitories. We recruited 35 college students to participate in ourstudy and deployed a social robot coach in their room. The robot delivereddaily positive psychology sessions among other useful skills like deliveringthe weather forecast, scheduling reminders, etc. We found a statisticallysignificant improvement in participants' psychological wellbeing, mood, andreadiness to change behavior for improved wellbeing after they completed thestudy. Furthermore, students' personality traits were found to have asignificant association with intervention efficacy. Analysis of the post-studyinterview revealed students' appreciation of the robot's companionship andtheir concerns for privacy.
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A biological understanding is key for managing medical conditions, yetpsychological and social aspects matter too. The main problem is that these twoaspects are hard to quantify and inherently difficult to communicate. Toquantify psychological aspects, this work mined around half a million Redditposts in the sub-communities specialised in 14 medical conditions, and it didso with a new deep-learning framework. In so doing, it was able to associatementions of medical conditions with those of emotions. To then quantify socialaspects, this work designed a probabilistic approach that mines openprescription data from the National Health Service in England to compute theprevalence of drug prescriptions, and to relate such a prevalence to censusdata. To finally visually communicate each medical condition's biological,psychological, and social aspects through storytelling, we designed anarrative-style layered Martini Glass visualization. In a user study involving52 participants, after interacting with our visualization, a considerablenumber of them changed their mind on previously held opinions: 10% gave moreimportance to the psychological aspects of medical conditions, and 27% weremore favourable to the use of social media data in healthcare, suggesting theimportance of persuasive elements in interactive visualizations.
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Active research pertaining to the affective phenomenon of empathy anddistress is invaluable for improving human-machine interaction. Predictingintensities of such complex emotions from textual data is difficult, as theseconstructs are deeply rooted in the psychological theory. Consequently, forbetter prediction, it becomes imperative to take into account ancillary factorssuch as the psychological test scores, demographic features, underlying latentprimitive emotions, along with the text's undertone and its psychologicalcomplexity. This paper proffers team PVG's solution to the WASSA 2021 SharedTask on Predicting Empathy and Emotion in Reaction to News Stories. Leveragingthe textual data, demographic features, psychological test score, and theintrinsic interdependencies of primitive emotions and empathy, we propose amulti-input, multi-task framework for the task of empathy score prediction.Here, the empathy score prediction is considered the primary task, whileemotion and empathy classification are considered secondary auxiliary tasks.For the distress score prediction task, the system is further boosted by theaddition of lexical features. Our submission ranked 1$^{st}$ based on theaverage correlation (0.545) as well as the distress correlation (0.574), and2$^{nd}$ for the empathy Pearson correlation (0.517).
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Relating explicit psychological mechanisms and observable behaviours is acentral aim of psychological and behavioural science. We implemented theprinciples of the Projective Consciousness Model into artificial agentsembodied as virtual humans, as a proof-of-concept for a methodologicalframework aimed at simulating behaviours and assessing underlying psychologicalparameters, in the context of experiments in virtual reality. We focus onsimulating the role of Theory of Mind (ToM) in the choice of strategicbehaviours of approach and avoidance to optimise the satisfaction of agents'preferences. We designed an experiment in a virtual environment that could beused with real humans, allowing us to classify behaviours as a function oforder of ToM, up to the second order. We show that our agents demonstrateexpected behaviours with consistent parameters of ToM in this experiment. Wealso show that the agents can be used to estimate correctly each other order ofToM. A similar approach could be used with real humans in virtual realityexperiments not only to enable human participants to interact with parametric,virtual humans as stimuli, but also as a mean of inference to derivemodel-based psychological assessments of the participants.
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This is a chapter for the third volume of the New Handbook of MathematicalPsychology. It presented mathematical foundations of Fechnerian Scaling, amethod of metrizing stimulus spaces based on subjective measures ofdissimilarity.
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Abstract: This book consists of a selection of articles divided into threemain themes: Statistics, Quantitative Trading, Psychology. These threearguments are indispensable for the development of a quantitative tradingsystem. The order of the articles was chosen so as to constitute a singlelogical reasoning that develops progressively.
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Counterfactual explanations (CFEs) highlight what changes to a model's inputwould have changed its prediction in a particular way. CFEs have gainedconsiderable traction as a psychologically grounded solution for explainableartificial intelligence (XAI). Recent innovations introduce the notion ofcomputational plausibility for automatically generated CFEs, enhancing theirrobustness by exclusively creating plausible explanations. However, practicalbenefits of such a constraint on user experience and behavior is yet unclear.In this study, we evaluate objective and subjective usability ofcomputationally plausible CFEs in an iterative learning design targeting noviceusers. We rely on a novel, game-like experimental design, revolving around anabstract scenario. Our results show that novice users actually benefit lessfrom receiving computationally plausible rather than closest CFEs that produceminimal changes leading to the desired outcome. Responses in a post-game surveyreveal no differences in terms of subjective user experience between bothgroups. Following the view of psychological plausibility as comparativesimilarity, this may be explained by the fact that users in the closestcondition experience their CFEs as more psychologically plausible than thecomputationally plausible counterpart. In sum, our work highlights alittle-considered divergence of definitions of computational plausibility andpsychological plausibility, critically confirming the need to incorporate humanbehavior, preferences and mental models already at the design stages of XAIapproaches. In the interest of reproducible research, all source code, acquireduser data, and evaluation scripts of the current study are available:https://github.com/ukuhl/PlausibleAlienZoo
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The personal spatial structure of an observer is introduced as a centralelement in the positioning of objects in space. The link between a referenceframe used by an observer and his personal spatial structure is discussed.Research on inversion or reversal of incoming light in psychology indicatesthat the personal spatial structure of an individual, as well as a referenceframe that he uses, depends on the internal coordination of sensory stimuli andthat the position of objects in space depends in part on psychological factorsthat affect one's personal spatial structure. Other research in psychologydemonstrating the flexibility for an observer in determining the direction ofup-down relative to a particular figure, and indeed relative to the entiresurround, is also noted and supports the thesis that the observer plays a rolein the positioning of objects in space.
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We try to perform geometrization of psychology by representing mental states,<<ideas>>, by points of a metric space, <<mental space>>. Evolution of ideas isdescribed by dynamical systems in metric mental space. We apply the mentalspace approach for modeling of flows of unconscious and conscious informationin the human brain. In a series of models, Models 1-4, we consider cognitivesystems with increasing complexity of psychological behavior determined bystructure of flows of ideas. Since our models are in fact models of theAI-type, one immediately recognizes that they can be used for creation ofAI-systems, which we call psycho-robots, exhibiting important elements of humanpsyche. Creation of such psycho-robots may be useful improvement of domesticrobots. At the moment domestic robots are merely simple working devices (e.g.vacuum cleaners or lawn mowers) . However, in future one can expect demand insystems which be able not only perform simple work tasks, but would haveelements of human self-developing psyche. Such AI-psyche could play animportant role both in relations between psycho-robots and their owners as wellas between psycho-robots. Since the presence of a huge numbers ofpsycho-complexes is an essential characteristic of human psychology, it wouldbe interesting to model them in the AI-framework.
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Statistical mechanics has proven to be able to capture the fundamental rulesunderlying phenomena of social aggregation and opinion dynamics, well studiedin disciplines like sociology and psychology. This approach is based on theunderlying paradigm that the interesting dynamics of multi-agent systems emergefrom the correct definition of few parameters governing the evolution of eachindividual. Into this context, we propose a new model of opinion dynamics basedon the psychological construct named "cognitive dissonance". Our system is madeof interacting individuals, the agents, each bearing only two dynamicalvariables (respectively "opinion" and "affinity") self-consistently adjustedduring time evolution. We also define two special classes of interactingentities, both acting for a peace mediation process but via different course ofaction: "diplomats" and "auctoritates". The behaviour of the system with andwithout peace mediators (PMs) is investigated and discussed with reference tocorresponding psychological and social implications.
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Thermodynamics have been applied to astronomy, biology, psychology, somesocial systems and so on. But, various evolutions from astronomy to biology andsocial systems cannot be only increase of entropy. When fluctuations aremagnified due to internal interactions, the statistical independence and thesecond law of the thermodynamics are not hold. The existence of internalinteractions is necessary condition of decrease of entropy in isolated system.We calculate quantitatively the entropy of plasma. Then we discuss thethermodynamics of biology, and obtain a mathematical expression on moderatedegree of input negative entropy flow, which is a universal scientific law.Further, the thermodynamics of physiology and psychology, and the thought fieldare introduced. Qigong and various religious practices are related to thesestates of order, in which decrease of entropy is shown due to internalinteractions of the isolated systems. Finally we discuss possible decrease ofentropy in some social systems.
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Emotional disorders and psychological flourishing are the result of complexinteractions between positive and negative affects that depend on externalevents and the subject's internal representations. Based on psychological data,we mathematically model the dynamical balance between positive and negativeaffects as a function of the response to external positive and negative events.This modeling allows the investigation of the relative impact of two leadingforms of therapy on affect balance. The model uses a delay differentialequation to analytically study the complete bifurcation diagram of the system.We compare the results of the model to psychological data on a single,recurrently depressed patient that was administered the two types of therapiesconsidered (viz., coping-focused vs. affect-focused). The model leads to theprediction that stabilization at a normal state may rely on evaluating one'semotional state through an historical ongoing emotional state rather than in anarrow present window. The simple mathematical model proposed here offers atheoretically grounded quantitative framework for investigating the temporalprocess of change and parameters of resilience to relapse.
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Similar formalisms have been independently developed in psychology, to dealwith the issue of selective influences (deciding which of several experimentalmanipulations selectively influences each of several, generallynon-independent, response variables), and in quantum mechanics (QM), to dealwith the EPR entanglement phenomena (deciding whether an EPR experiment allowsfor a "classical" account). The parallels between these problems areestablished by observing that any two noncommuting measurements in QM aremutually exclusive and can therefore be treated as analogs of different valuesof one and the same input. Both problems reduce to that of the existence of ajointly distributed system of random variables, one variable for every value ofevery input (in psychology) or every measurement on every particle involved (inan EPR experiment). We overview three classes of necessary conditions (some ofthem also sufficient under additional constraints) for the existence of suchjoint distributions.
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This work lies in the fusion of experimental economics and data mining. Itcontinues author's previous work on mining behaviour rules of human subjectsfrom experimental data, where game-theoretic predictions partially fail towork. Game-theoretic predictions aka equilibria only tend to success withexperienced subjects on specific games, what is rarely given. Apart from gametheory, contemporary experimental economics offers a number of alternativemodels. In relevant literature, these models are always biased by psychologicaland near-psychological theories and are claimed to be proven by the data. Thiswork introduces a data mining approach to the problem without using vastpsychological background. Apart from determinism, no other biases are regarded.Two datasets from different human subject experiments are taken for evaluation.The first one is a repeated mixed strategy zero sum game and the second -repeated ultimatum game. As result, the way of mining deterministicregularities in human strategic behaviour is described and evaluated. As futurework, the design of a new representation formalism is discussed.
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In computational cognitive science, the cognitive architecture ACT-R is verypopular. It describes a model of cognition that is amenable to computerimplementation, paving the way for computational psychology. Its underlyingpsychological theory has been investigated in many psychological experiments,but ACT-R lacks a formal definition of its underlying concepts from amathematical-computational point of view. Although the canonical implementationof ACT-R is now modularized, this production rule system is still hard to adaptand extend in central components like the conflict resolution mechanism (whichdecides which of the applicable rules to apply next).  In this work, we present a concise implementation of ACT-R based onConstraint Handling Rules which has been derived from a formalization in priorwork. To show the adaptability of our approach, we implement several differentconflict resolution mechanisms discussed in the ACT-R literature. This resultsin the first implementation of one such mechanism. For the other mechanisms, weempirically evaluate if our implementation matches the results of referenceimplementations of ACT-R.
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Economies are instances of complex socio-technical systems that are shaped bythe interactions of large numbers of individuals. The individual behavior anddecision-making of consumer agents is determined by complex psychologicaldynamics that include their own assessment of present and future economicconditions as well as those of others, potentially leading to feedback loopsthat affect the macroscopic state of the economic system. We propose that thelarge-scale interactions of a nation's citizens with its online resources canreveal the complex dynamics of their collective psychology, including theirassessment of future system states. Here we introduce a behavioral index ofChinese Consumer Confidence (C3I) that computationally relates large-scaleonline search behavior recorded by Google Trends data to the macroscopicvariable of consumer confidence. Our results indicate that such computationalindices may reveal the components and complex dynamics of consumer psychologyas a collective socio-economic phenomenon, potentially leading to improved andmore refined economic forecasting.
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Heart rate (HR) and its variability (HRV) has been proposed as a marker fordepressive symptoms and other aspects of mental health. However, the realcorrelation between them is presently uncertain, as previous studies havegenerally been conducted on the basis of small samples. In a sample of 113adult male prisoners, we analyzed correlations between five measures of HR/HRVand five psychological measures of mental health aspects (depression, state andtrait anxiety, and social relationships). We used Nadaraya-Watsonnon-parametric regression in both directions and age-stratified Spearmancorrelation to detect possible relations. Despite strong correlations amongHR/HRV measures and among psychological measures, correlations between HR/HRVand psychological measures were low and non-significant for the overall sample.However, we found an age dependency, suggesting some correlations in youngerpeople (HR with STAI-State, r = 0.39; with HADS-Anxiety, r = 0.52; both p <.005). Overall, the general utility of HR/HRV as a marker for mental healthacross populations remains unclear. Future research should address age andother potential confounders more consistently.
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With the arrival of the R packages nlme and lme4, linear mixed models (LMMs)have come to be widely used in experimentally-driven areas like psychology,linguistics, and cognitive science. This tutorial provides a practicalintroduction to fitting LMMs in a Bayesian framework using the probabilisticprogramming language Stan. We choose Stan (rather than WinBUGS or JAGS) becauseit provides an elegant and scalable framework for fitting models in most of thestandard applications of LMMs. We ease the reader into fitting increasinglycomplex LMMs, first using a two-condition repeated measures self-paced readingstudy, followed by a more complex $2\times 2$ repeated measures factorialdesign that can be generalized to much more complex designs.
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Affects---emotions and moods---have an impact on cognitive processingactivities and the working performance of individuals. It has been establishedthat software development tasks are undertaken through cognitive processingactivities. Therefore, we have proposed to employ psychology theory andmeasurements in software engineering (SE) research. We have called it"psychoempirical software engineering". However, we found out that existing SEresearch has often fallen into misconceptions about the affect of developers,lacking in background theory and how to successfully employ psychologicalmeasurements in studies. The contribution of this paper is threefold. (1) Ithighlights the challenges to conduct proper affect-related studies withpsychology; (2) it provides a comprehensive literature review in affect theory;and (3) it proposes guidelines for conducting psychoempirical softwareengineering.
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Borderline personality disorder and narcissistic personality disorder areimportant nosographic entities and have been subject of intensiveinvestigations. The currently prevailing psychodynamic theory for mentaldisorders is based on the repertoire of defense mechanisms employed. Anotherline of research is concerned with the study of psychological traumas anddissociation as a defensive response. Both theories can be used to shed lighton some aspects of pathological mental functioning, and have many points ofcontact. This work merges these two psychological theories, and builds a modelof mental function in a relational context called Quadripolar Relational Model.The model, which is enriched with ideas borrowed from the field of computerscience, leads to a new therapeutic proposal for psychological traumas andpersonality disorders.
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Statistical mechanics has proven to be able to capture the fundamental rulesunderlying phenomena of social aggregation and opinion dynamics, well studiedin disciplines like sociology and psychology. This approach is based on theunderlying paradigm that the interesting dynamics of multi-agent systems emergefrom the correct definition of few parameters governing the evolution of eachindividual. Into this context, we propose a particular model of opiniondynamics based on the psychological construct named "cognitive dissonance". Oursystem is made of interacting individuals, the agents, each bearing only twodynamical variables (respectively "opinion" and "affinity") self-consistentlyadjusted during time evolution. We also define two special classes ofinteracting entities, both acting for a peace mediation process but viadifferent course of action: "diplomats" and "auctoritates". The behavior of thesystem with and without peace mediators (PMs) is investigated and discussedwith reference to corresponding psychological and social implications.
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Similarity is a core notion that is used in psychology and two branches oflinguistics: theoretical and computational. The similarity datasets that comefrom the two fields differ in design: psychological datasets are focused arounda certain topic such as fruit names, while linguistic datasets contain wordsfrom various categories. The later makes humans assign low similarity scores tothe words that have nothing in common and to the words that have contrast inmeaning, making similarity scores ambiguous. In this work we discuss thesimilarity collection procedure for a multi-category dataset that avoids scoreambiguity and suggest changes to the evaluation procedure to reflect theinsights of psychological literature for word, phrase and sentence similarity.We suggest to ask humans to provide a list of commonalities and differencesinstead of numerical similarity scores and employ the structure of humanjudgements beyond pairwise similarity for model evaluation. We believe that theproposed approach will give rise to datasets that test meaning representationmodels more thoroughly with respect to the human treatment of similarity.
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Humans have employed an incredible variety of plant-derived substances overthe millennia in order to alter consciousness and perception. Among theinnumerable narcotics, analgesics, 'ordeal' drugs, and other psychoactivesubstances discovered and used in ritualistic contexts by cultures around theworld, one class in particular stands out not only for its radicalpsychological effects, but also for the highly charged political and legalatmosphere that has surrounded it since its widespread adoption about 50 yearsago: so-called psychedelic substances. We review functional neuroimaginginvestigations of the neural correlates of the psychedelic experience, andhighlight relationships with the psychological and neural bases of creativity,daydreaming, and dreaming.
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In online social networks, users tend to select information that adhere totheir system of beliefs and to form polarized groups of like minded people.Polarization as well as its effects on online social interactions have beenextensively investigated. Still, the relation between group formation andpersonality traits remains unclear. A better understanding of the cognitive andpsychological determinants of online social dynamics might help to design moreefficient communication strategies and to challenge the digital misinformationthreat. In this work, we focus on users commenting posts published by USFacebook pages supporting scientific and conspiracy-like narratives, and weclassify the personality traits of those users according to their onlinebehavior. We show that different and conflicting communities are populated byusers showing similar psychological profiles, and that the dominant personalitymodel is the same in both scientific and conspiracy echo chambers. Moreover, weobserve that the permanence within echo chambers slightly shapes users'psychological profiles. Our results suggest that the presence of specificpersonality traits in individuals lead to their considerable involvement insupporting narratives inside virtual echo chambers.
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This paper focuses on the problem of explaining predictions of psychologicalattributes such as attractiveness, happiness, confidence and intelligence fromface photographs using deep neural networks. Since psychological attributedatasets typically suffer from small sample sizes, we apply transfer learningwith two base models to avoid overfitting. These models were trained on an ageand gender prediction task, respectively. Using a novel explanation method weextract heatmaps that highlight the parts of the image most responsible for theprediction. We further observe that the explanation method provides importantinsights into the nature of features of the base model, which allow one toassess the aptitude of the base model for a given transfer learning task.Finally, we observe that the multiclass model is more feature rich than itsbinary counterpart. The experimental evaluation is performed on the 2222 imagesfrom the 10k US faces dataset containing psychological attribute labels as wellas on a subset of KDEF images.
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Deep neural networks have become increasingly successful at solving classicperception problems such as object recognition, semantic segmentation, andscene understanding, often reaching or surpassing human-level accuracy. Thissuccess is due in part to the ability of DNNs to learn useful representationsof high-dimensional inputs, a problem that humans must also solve. We examinethe relationship between the representations learned by these networks andhuman psychological representations recovered from similarity judgments. Wefind that deep features learned in service of object classification account fora significant amount of the variance in human similarity judgments for a set ofanimal images. However, these features do not capture some qualitativedistinctions that are a key part of human representations. To remedy this, wedevelop a method for adapting deep features to align with human similarityjudgments, resulting in image representations that can potentially be used toextend the scope of psychological experiments.
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Psychological traumas are thought to be present in a wide range ofconditions, including post-traumatic stress disorder, disorganised attachment,personality disorders, dissociative identity disorder and psychosis. This workpresents a new psychotherapy for psychological traumas, based on a functionalmodel of the mind, built with elements borrowed from the fields of computerscience, artificial intelligence and neural networks. The model revolves aroundthe concept of hierarchical value and explains the emergence of dissociationand splitting in response to emotional pain. The key intuition is that traumasare caused by too strong negative emotions, which are in turn made possible bya low-value self, which is in turn determined by low-value self-associatedideas. The therapeutic method compiles a list of patient's traumas, identifiesfor each trauma a list of low-value self-associated ideas, and provides foreach idea a list of counterexamples, to raise the self value and solve thetrauma. Since the psychotherapy proposed has not been clinically tested,statements on its effectiveness are premature. However, since the conceptualbasis is solid and traumas are hypothesised to be present in many psychologicaldisorders, the potential gain may be substantial.
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We present an introduction to a novel model of an individual and groupopinion dynamics, taking into account different ways in which different sourcesof information are filtered due to cognitive biases. The agent based model,using Bayesian updating of the individual belief distribution, is based on therecent psychology work by Dan Kahan. Open nature of the model allows to studythe effects of both static and time-dependent biases and information processingfilters. In particular, the paper compares the effects of two importantpsychological mechanisms: the confirmation bias and the politically motivatedreasoning. Depending on the effectiveness of the information filtering (agentbias), the agents confronted with an objective information source may eitherreach a consensus based on the truth, or remain divided despite the evidence.In general, the model might provide an understanding into the increasinglypolarized modern societies, especially as it allows mixing of different typesof filters: psychological, social, and algorithmic.
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With the advances in robotic technology, research in human-robotcollaboration (HRC) has gained in importance. For robots to interact withhumans autonomously they need active decision making that takes human partnersinto account. However, state-of-the-art research in HRC does often assume aleader-follower division, in which one agent leads the interaction. We believethat this is caused by the lack of a reliable representation of the human andthe environment to allow autonomous decision making. This problem can beovercome by an embodied approach to HRC which is inspired by psychologicalstudies of human-human interaction (HHI). In this survey, we reviewneuroscientific and psychological findings of the sensorimotor patterns thatgovern HHI and view them in a robotics context. Additionally, we study theadvances made by the robotic community into the direction of embodied HRC. Wefocus on the mechanisms that are required for active, physical human-robotcollaboration. Finally, we discuss the similarities and differences in the twofields of study which pinpoint directions of future research.
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Decades of psychological research have been aimed at modeling how peoplelearn features and categories. The empirical validation of these theories isoften based on artificial stimuli with simple representations. Recently, deepneural networks have reached or surpassed human accuracy on tasks such asidentifying objects in natural images. These networks learn representations ofreal-world stimuli that can potentially be leveraged to capture psychologicalrepresentations. We find that state-of-the-art object classification networksprovide surprisingly accurate predictions of human similarity judgments fornatural images, but fail to capture some of the structure represented bypeople. We show that a simple transformation that corrects these discrepanciescan be obtained through convex optimization. We use the resultingrepresentations to predict the difficulty of learning novel categories ofnatural images. Our results extend the scope of psychological experiments andcomputational modeling by enabling tractable use of large natural stimulussets.
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There has been a recent resurgence in the area of explainable artificialintelligence as researchers and practitioners seek to make their algorithmsmore understandable. Much of this research is focused on explicitly explainingdecisions or actions to a human observer, and it should not be controversial tosay that looking at how humans explain to each other can serve as a usefulstarting point for explanation in artificial intelligence. However, it is fairto say that most work in explainable artificial intelligence uses only theresearchers' intuition of what constitutes a `good' explanation. There existsvast and valuable bodies of research in philosophy, psychology, and cognitivescience of how people define, generate, select, evaluate, and presentexplanations, which argues that people employ certain cognitive biases andsocial expectations towards the explanation process. This paper argues that thefield of explainable artificial intelligence should build on this existingresearch, and reviews relevant papers from philosophy, cognitivepsychology/science, and social psychology, which study these topics. It drawsout some important findings, and discusses ways that these can be infused withwork on explainable artificial intelligence.
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This paper draws a parallel between similarity-based categorisation modelsdeveloped in cognitive psychology and the nearest neighbour classifier (1-NN)in machine learning. Conceived as a result of the historical rivalry betweenprototype theories (abstraction) and exemplar theories (memorisation), recentmodels of human categorisation seek a compromise in-between. Regarding thestimuli (entities to be categorised) as points in a metric space, machinelearning offers a large collection of methods to select a small, representativeand discriminative point set. These methods are known under various names:instance selection, data editing, prototype selection, prototype generation orprototype replacement. The nearest neighbour classifier is used with theselected reference set. Such a set can be interpreted as a data-drivencategorisation model. We juxtapose the models from the two fields to enablecross-referencing. We believe that both machine learning and cognitivepsychology can draw inspiration from the comparison and enrich their repertoireof similarity-based models.
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The relationship between sensory consonance and Western harmony is animportant topic in music theory and psychology. We introduce new methods foranalysing this relationship, and apply them to large corpora representing threeprominent genres of Western music: classical, popular, and jazz music. Thesemethods centre on a generative sequence model with an exponential-familyenergy-based form that predicts chord sequences from continuous features. Weuse this model to investigate one aspect of instantaneous consonance(harmonicity) and two aspects of sequential consonance (spectral distance andvoice-leading distance). Applied to our three musical genres, the resultsgenerally support the relationship between sensory consonance and harmony, butlead us to question the high importance attributed to spectral distance in thepsychological literature. We anticipate that our methods will provide a usefulplatform for future work linking music psychology to music theory.
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Researchers often divide symbolic music corpora into contiguous sequences ofn events (called n-grams) for the purposes of pattern discovery, key finding,classification, and prediction. What is more, several studies have reportedimproved task performance when using psychologically motivated weightingfunctions, which adjust the count to privilege n-grams featuring more salientor memorable events (e.g., Krumhansl, 1990). However, these functions have yetto appear in harmonic pattern discovery algorithms, which attempt to discoverthe most recurrent chord progressions in complex polyphonic corpora. This studyexamines whether psychologically-motivated weighting functions can improveharmonic pattern discovery algorithms. Models using various n-gram selectionmethods, weighting functions, and ranking algorithms attempt to discover themost conventional closing harmonic progression in the common-practice period,ii6-"I64"-V7-I, with the progression's mean reciprocal rank serving as anevaluation metric for model comparison.
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Background. There are some publications in software engineering research thataim at guiding researchers in assessing validity threats to their studies.Still, many researchers fail to address many aspects of validity that areessential to quantitative research on human factors. Goal. This paper has thegoal of triggering a change of mindset in what types of studies are the mostvaluable to the behavioral software engineering field, and also provide moredetails of what construct validity is. Method. The approach is based onpsychological test theory and draws upon methods used in psychology in relationto construct validity. Results. In this paper, I suggest a different approachto validity threats than what is commonplace in behavioral software engineeringresearch. Conclusions. While this paper focuses on behavioral softwareengineering, I believe other types of software engineering research might alsobenefit from an increased focus on construct validity.
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This paper explores the expressive capabilities of a swarm of miniaturemobile robots within the context of inter-robot interactions and their mappingto the so-called fundamental emotions. In particular, we investigate how motionand shape descriptors that are psychologically associated with differentemotions can be incorporated into different swarm behaviors for the purpose ofartistic expositions. Based on these characterizations from social psychology,a set of swarm behaviors is created, where each behavior corresponds to afundamental emotion. The effectiveness of these behaviors is evaluated in asurvey in which the participants are asked to associate different swarmbehaviors with the fundamental emotions. The results of the survey show thatmost of the research participants assigned to each video the emotion intendedto be portrayed by design. These results confirm that abstract descriptorsassociated with the different fundamental emotions in social psychology provideuseful motion characterizations that can be effectively transformed intoexpressive behaviors for a swarm of simple ground mobile robots.
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Personalized Active Learner (PAL) is a wearable system for real-time,personalized, and context-aware health and cognition support. PAL's systemconsists of a wearable device, mobile app, cloud database, data visualizationweb app, and machine learning server. PAL's wearable device uses multi-modalsensors (camera, microphone, heart-rate) with on-device machine learning andopen-ear audio output to provide real-time and context-aware cognitive,behavioral and psychological interventions. PAL also allows users to track thelong-term correlations between their activities and physiological states tomake well-informed lifestyle decisions. In this paper, we present andopen-source PAL's system so that people can use it for health and cognitionsupport applications. We also open-source three fully-developed exampleapplications using PAL for face-based memory augmentation, contextual languagelearning, and heart-rate-based psychological support. PAL's flexible, modularand extensible platform combines trends in data-driven medicine, mobilepsychology, and cognitive enhancement to support data-driven and empoweringhealth and cognition applications.
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Psychology research focuses on interactions, and this has deep implicationsfor inference from non-representative samples. For the goal of estimatingaverage treatment effects, we propose to fit a model allowing treatment tointeract with background variables and then average over the distribution ofthese variables in the population. This can be seen as an extension ofmultilevel regression and poststratification (MRP), a method used in politicalscience and other areas of survey research, where researchers wish togeneralize from a sparse and possibly non-representative sample to the generalpopulation. In this paper, we discuss areas where this method can be used inthe psychological sciences. We use our method to estimate the normingdistribution for the Big Five Personality Scale using open source data. Weargue that large open data sources like this and other collaborative datasources can be combined with MRP to help resolve current challenges ofgeneralizability and replication in psychology.
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Recent advances in the fields of machine learning and neurofinance haveyielded new exciting research perspectives in practical inference ofbehavioural economy in financial markets and microstructure study. We herepresent the latest results from a recently published stock market simulatorbuilt around a multi-agent system architecture, in which each agent is anautonomous investor trading stocks by reinforcement learning (RL) via acentralised double-auction limit order book. The RL framework allows for theimplementation of specific behavioural and cognitive traits known to traderpsychology, and thus to study the impact of these traits on the whole stockmarket at the mesoscale. More precisely, we narrowed our agent design to threesuch psychological biases known to have a direct correspondence with RL theory,namely delay discounting, greed, and fear. We compared ensuing simulated datato real stock market data over the past decade or so, and find that marketstability benefits from larger populations of agents prone to delay discountingand most astonishingly, to greed.
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Children and adolescents interact in peer groups, which are known toinfluence a range of psychological and behavioral outcomes. In developmentalpsychology and related disciplines, social cognitive mapping (SCM), asimplemented with the SCM 4.0 software, is the most commonly used method foridentifying peer groups from peer report data. However, in a series of fourstudies, we demonstrate that SCM has an unacceptably high risk of falsepositives. Specifically, we show that SCM will identify peer groups even whenapplied to random data. We introduce backbone extraction and communitydetection as one promising alternative to SCM, and offer severalrecommendations for researchers seeking to identify peer groups from peerreport data.
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There is a growing interest and literature on intrinsic motivations andopen-ended learning in both cognitive robotics and machine learning on oneside, and in psychology and neuroscience on the other. This paper aims toreview some relevant contributions from the two literature threads and to drawlinks between them. To this purpose, the paper starts by defining intrinsicmotivations and by presenting a computationally-driven theoretical taxonomy oftheir different types. Then it presents relevant contributions from thepsychological and neuroscientific literature related to intrinsic motivations,interpreting them based on the grid, and elucidates the mechanisms andfunctions they play in animals and humans. Endowed with such concepts and theirbiological underpinnings, the paper next presents a selection of models fromcognitive robotics and machine learning that computationally operationalise theconcepts of intrinsic motivations and links them to biology concepts. Thecontribution finally presents some of the open challenges of the field fromboth the psychological/neuroscientific and computational perspectives.
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In this study, we reported our exploration of Text-To-Speech without Text(TTS without T) in the Zero Resource Speech Challenge 2020, in whichparticipants proposed an end-to-end, unsupervised system that learned speechrecognition and TTS together. We addressed the challenge usingbiologically/psychologically motivated modules of Artificial Neural Networks(ANN), with a particular interest in unsupervised learning of human language asa biological/psychological problem. The system first processes Mel FrequencyCepstral Coefficient (MFCC) frames with an Echo-State Network (ESN), andsimulates computations in cortical microcircuits. The outcome is discretized byour original Variational Autoencoder (VAE) that implements the Dirichlet-basedBayesian clustering widely accepted in computational linguistics and cognitivescience. The discretized signal is then reverted into sound waveform via aneural-network implementation of the source-filter model for speech production.
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Simulation models of pedestrian dynamics have become an invaluable tool forevacuation planning. Typically crowds are assumed to stream unidirectionallytowards a safe area. Simulated agents avoid collisions through mechanisms thatbelong to each individual, such as being repelled from each other by imaginaryforces. But classic locomotion models fail when collective cooperation iscalled for, notably when an agent, say a first-aid attendant, needs to forge apath through a densely packed group. We present a controlled experiment toobserve what happens when humans pass through a dense static crowd. Weformulate and test hypothesis on salient phenomena. We discuss our observationsin a psychological framework. We derive a model that incorporates: agents'perception and cognitive processing of a situation that needs cooperation;selection from a portfolio of behaviours, such as being cooperative; and asuitable action, such as swapping places. Agents' ability to successfully getthrough a dense crowd emerges as an effect of the psychological model.
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Social engineering cyberattacks are a major threat because they often preludesophisticated and devastating cyberattacks. Social engineering cyberattacks area kind of psychological attack that exploits weaknesses in human cognitivefunctions. Adequate defense against social engineering cyberattacks requires adeeper understanding of what aspects of human cognition are exploited by thesecyberattacks, why humans are susceptible to these cyberattacks, and how we canminimize or at least mitigate their damage. These questions have received someamount of attention but the state-of-the-art understanding is superficial andscattered in the literature. In this paper, we review human cognition throughthe lens of social engineering cyberattacks. Then, we propose an extendedframework of human cognitive functions to accommodate social engineeringcyberattacks. We cast existing studies on various aspects of social engineeringcyberattacks into the extended framework, while drawing a number of insightsthat represent the current understanding and shed light on future researchdirections. The extended framework might inspire future research endeavorstowards a new sub-field that can be called Cybersecurity Cognitive Psychology,which tailors or adapts principles of Cognitive Psychology to the cybersecuritydomain while embracing new notions and concepts that are unique to thecybersecurity domain.
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Life events can dramatically affect our psychological state and workperformance. Stress, for example, has been linked to professionaldissatisfaction, increased anxiety, and workplace burnout. We explore theimpact of positive and negative life events on a number of psychologicalconstructs through a multi-month longitudinal study of hospital and aerospaceworkers. Through causal inference, we demonstrate that positive life eventsincrease positive affect, while negative events increase stress, anxiety andnegative affect. While most events have a transient effect on psychologicalstates, major negative events, like illness or attending a funeral, can reducepositive affect for multiple days. Next, we assess whether these events can bedetected through wearable sensors, which can cheaply and unobtrusively monitorhealth-related factors. We show that these sensors paired with embedding-basedlearning models can be used ``in the wild'' to capture atypical life events inhundreds of workers across both datasets. Overall our results suggest thatautomated interventions based on physiological sensing may be feasible to helpworkers regulate the negative effects of life events.
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Machines have achieved a broad and growing set of linguistic competencies,thanks to recent progress in Natural Language Processing (NLP). Psychologistshave shown increasing interest in such models, comparing their output topsychological judgments such as similarity, association, priming, andcomprehension, raising the question of whether the models could serve aspsychological theories. In this article, we compare how humans and machinesrepresent the meaning of words. We argue that contemporary NLP systems arefairly successful models of human word similarity, but they fall short in manyother respects. Current models are too strongly linked to the text-basedpatterns in large corpora, and too weakly linked to the desires, goals, andbeliefs that people express through words. Word meanings must also be groundedin perception and action and be capable of flexible combinations in ways thatcurrent systems are not. We discuss more promising approaches to grounding NLPsystems and argue that they will be more successful with a more human-like,conceptual basis for word meaning.
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Modern biomedical, behavioral and psychological inference about cause-effectrelationships respects an ergodic assumption, that is, that mean response ofrepresentative samples allow predictions about individual members of thosesamples. Recent empirical evidence in all of the same fields indicatessystematic violations of the ergodic assumption. Indeed, violation ofergodicity in biomedical, behavioral and psychological causes is precisely theinspiration behind our research inquiry. Here, we review the long term costs toscientific progress in these domains and a practical way forward. Specifically,we advocate the use of statistical measures that can themselves encode thedegree and type of non-ergodicity in measurements. Taking such steps will leadto a paradigm shift, allowing researchers to investigate the nonstationary,far-from-equilibrium processes that characterize the creativity and emergenceof biological and psychological behavior.
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The task of aesthetic quality assessment is complicated due to itssubjectivity. In recent years, the target representation of image aestheticquality has changed from a one-dimensional binary classification label ornumerical score to a multi-dimensional score distribution. According to currentmethods, the ground truth score distributions are straightforwardly regressed.However, the subjectivity of aesthetics is not taken into account, that is tosay, the psychological processes of human beings are not taken intoconsideration, which limits the performance of the task. In this paper, wepropose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predictaesthetic score distribution from images. The DDD model can describe thepsychological process of aesthetic perception instead of traditional modelingof the results of assessment. We use deep convolution neural networks toregress the parameters of the drift-diffusion model. The experimental resultsin large scale aesthetic image datasets reveal that our novel DDD model issimple but efficient, which outperforms the state-of-the-art methods inaesthetic score distribution prediction. Besides, different psychologicalprocesses can also be predicted by our model.
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Research instruments play significant roles in the construction of scientificknowledge, even though we have only acquired very limited knowledge about theirlifecycles from quantitative studies. This paper aims to address this gap byquantitatively examining the citation contexts of an exemplary researchinstrument, the Diagnostic and Statistical Manual of Mental Disorders (DSM), infull-text psychological publications. We investigated the relationship betweenthe citation contexts of the DSM and its status as a valid instrument beingused and described by psychological researchers. We specifically focused on howthis relationship has changed over the DSM's citation histories, especiallythrough the temporal framework of its versions. We found that a new version ofthe DSM is increasingly regarded as a valid instrument after its publication;this is reflected in various key citation contexts, such as the use of hedges,attention markers, and the verb profile in sentences where the DSM is cited. Wecall this process the re-instrumentalization of the DSM in the space ofscientific publications. Our findings bridge an important gap betweenquantitative and qualitative science studies and shed light on an aspect of thesocial process of scientific instrument development that is not addressed bythe current qualitative literature.
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Human safety is the most important demand for human robot interaction andcollaboration (HRIC), which not only refers to physical safety, but alsoincludes psychological safety. Although many robots with differentconfigurations have entered our living and working environments, the humansafety problem is still an ongoing research problem in human-robot coexistencescenarios. This paper addresses the human safety issue by covering both thephysical safety and psychological safety aspects. First, we introduce anadaptive robot velocity control and step size adjustment method according tohuman facial expressions, such that the robot can adjust its movement to keepsafety when the human emotion is unusual. Second, we predict the human motionby detecting the suddenly changes of human head pose and gaze direction, suchthat the robot can infer whether the human attention is distracted, predict thenext move of human and rebuild a repulsive force to avoid potential collision.Finally, we demonstrate our idea using a 7 DOF TIAGo robot in a dynamic HRICenvironment, which shows that the robot becomes sense motive, and responds tohuman action and emotion changes quickly and efficiently.
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Machine learning has the potential to aid in mitigating the human effects ofclimate change. Previous applications of machine learning to tackle the humaneffects in climate change include approaches like informing individuals oftheir carbon footprint and strategies to reduce it. For these methods to be themost effective they must consider relevant social-psychological factors foreach individual. Of social-psychological factors at play in climate change,affect has been previously identified as a key element in perceptions andwillingness to engage in mitigative behaviours. In this work, we propose aninvestigation into how affect could be incorporated to enhance machine learningbased interventions for climate change. We propose using affective agent-basedmodelling for climate change as well as the use of a simulated climate changesocial dilemma to explore the potential benefits of affective machine learninginterventions. Behavioural and informational interventions can be a powerfultool in helping humans adopt mitigative behaviours. We expect that utilizingaffective ML can make interventions an even more powerful tool and helpmitigative behaviours become widely adopted.
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The COVID-19 pandemic has caused international social tension and unrest.Besides the crisis itself, there are growing signs of rising conflict potentialof societies around the world. Indicators of global mood changes are hard todetect and direct questionnaires suffer from social desirability biases.However, so-called implicit methods can reveal humans intrinsic desires frome.g. social media texts. We present psychologically validated social unrestpredictors and replicate scalable and automated predictions, setting a newstate of the art on a recent German shared task dataset. We employ this modelto investigate a change of language towards social unrest during the COVID-19pandemic by comparing established psychological predictors on samples of tweetsfrom spring 2019 with spring 2020. The results show a significant increase ofthe conflict indicating psychometrics. With this work, we demonstrate theapplicability of automated NLP-based approaches to quantitative psychologicalresearch.
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To facilitate effective human-robot interaction (HRI), trust-aware HRI hasbeen proposed, wherein the robotic agent explicitly considers the human's trustduring its planning and decision making. The success of trust-aware HRI dependson the specification of a trust dynamics model and a trust-behavior model. Inthis study, we proposed one novel trust-behavior model, namely the reversepsychology model, and compared it against the commonly used disuse model. Weexamined how the two models affect the robot's optimal policy and thehuman-robot team performance. Results indicate that the robot will deliberately"manipulate" the human's trust under the reverse psychology model. To correctthis "manipulative" behavior, we proposed a trust-seeking reward function thatfacilitates trust establishment without significantly sacrificing the teamperformance.
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This study reviews research on social emotions in robotics. In robotics, thestudy of emotions has been pursued for a long time, including the study oftheir recognition, expression, and computational modeling of the basicmechanisms which underlie them. Research has advanced according to well-knownpsychological findings, such as category and dimension theories. Many studieshave been based on these basic theories, addressing only basic emotions.However, social emotions, also referred to as higher-level emotions, have beenstudied in psychology. We believe that these higher-level emotions are worthpursuing in robotics for next-generation, socially aware robots. In this reviewpaper, we summarize the findings on social emotions in psychology andneuroscience, along with a survey of the studies on social emotions in roboticsthat have been conducted to date. Thereafter, research directions toward theimplementation of social emotions in robots are discussed.
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In this work, we provide an extensive part-of-speech analysis of thediscourse of social media users with depression. Research in psychologyrevealed that depressed users tend to be self-focused, more preoccupied withthemselves and ruminate more about their lives and emotions. Our work aims tomake use of large-scale datasets and computational methods for a quantitativeexploration of discourse. We use the publicly available depression dataset fromthe Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extractpart-of-speech features and several indices based on them. Our results revealstatistically significant differences between the depressed and non-depressedindividuals confirming findings from the existing psychology literature. Ourwork provides insights regarding the way in which depressed individuals areexpressing themselves on social media platforms, allowing for better-informedcomputational models to help monitor and prevent mental illnesses.
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Meetings are the fuel of organizations' productivity. At times, however, theyare perceived as wasteful vaccums that deplete employee morale andproductivity. Current meeting tools, to a great extent, have simplified andaugmented the ways meetings are conducted by enabling participants to ``getthings done'' and experience a comfortable physical environment. However, animportant yet less explored element of these tools' design space is that ofpsychological safety -- the extent to which participants feel listened to, ormotivated to be part of a meeting. We argue that an interdisciplinary approachwould benefit the creation of new tools designed for retrofitting meetings forpsychological safety. This approach comes with not only research opportunities-- ranging from sensing to modeling to user interface design -- but alsochallenges -- ranging from privacy to workplace surveillance.
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Shape information is crucial for human perception and cognition, and shouldtherefore also play a role in cognitive AI systems. We employ theinterdisciplinary framework of conceptual spaces, which proposes a geometricrepresentation of conceptual knowledge through low-dimensional interpretablesimilarity spaces. These similarity spaces are often based on psychologicaldissimilarity ratings for a small set of stimuli, which are then transformedinto a spatial representation by a technique called multidimensional scaling.Unfortunately, this approach is incapable of generalizing to novel stimuli. Inthis paper, we use convolutional neural networks to learn a generalizablemapping between perceptual inputs (pixels of grayscale line drawings) and arecently proposed psychological similarity space for the shape domain. Weinvestigate different network architectures (classification network vs.autoencoder) and different training regimes (transfer learning vs. multi-tasklearning). Our results indicate that a classification-based multi-task learningscenario yields the best results, but that its performance is relativelysensitive to the dimensionality of the similarity space.
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Searcher struggle is important feedback to Web search engines. Existing Websearch struggle detection methods rely on effort-based features to identify thestruggling moments. Their underlying assumption is that the more effort a userspends, the more struggling the user may be. However, recent studies havesuggested this simple association might be incorrect. This paper proposes a newfeature modulation method for struggle detection and refers to the reversaltheory in psychology. The reversal theory (RT) points out that instead ofhaving a static personality trait, people constantly switch between oppositepsychological states, complicating the relationship between the efforts theyspend and the level of frustration they feel. Supported by the theory, ourmethod modulates the effort-based features based on RT's bi-modal arousalmodel. Evaluations on week-long Web search logs confirm that the proposedmethod can statistically significantly improve state-of-the-art struggledetection methods.
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When writing software code, developers typically prioritise functionalityover security, either consciously or unconsciously through biases andheuristics. This is often attributed to tangible pressures such as clientrequirements, but little is understood about the psychological dimensionsaffecting security behaviours. There is an increasing demand for understandinghow psychological skills affect secure software development and to understandhow these skills themselves are developed during the learning process.  This doctoral research explores this research space, with aims to identifyimportant workplace-based skills for software developers; to identify andempirically investigate the soft skills behind these workplace skills in orderto understand how soft skills can influence security behaviours; and, toidentify ways to introduce and teach soft skills to computer science studentsto prepare the future generation of software developers.  The motivations behind this research are presented alongside the work plan.Three distinct phases are introduced, along with planned analyses. Phase one iscurrently in the data collection stage, with the second phase in planning.Prior relevant work is highlighted, and the paper concludes with a presentationof preliminary results and the planned next steps.
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We study GPT-3, a recent large language model, using tools from cognitivepsychology. More specifically, we assess GPT-3's decision-making, informationsearch, deliberation, and causal reasoning abilities on a battery of canonicalexperiments from the literature. We find that much of GPT-3's behavior isimpressive: it solves vignette-based tasks similarly or better than humansubjects, is able to make decent decisions from descriptions, outperformshumans in a multi-armed bandit task, and shows signatures of model-basedreinforcement learning. Yet we also find that small perturbations tovignette-based tasks can lead GPT-3 vastly astray, that it shows no signaturesof directed exploration, and that it fails miserably in a causal reasoningtask. These results enrich our understanding of current large language modelsand pave the way for future investigations using tools from cognitivepsychology to study increasingly capable and opaque artificial agents.
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Conversational data is essential in psychology because it can helpresearchers understand individuals cognitive processes, emotions, andbehaviors. Utterance labelling is a common strategy for analyzing this type ofdata. The development of NLP algorithms allows researchers to automate thistask. However, psychological conversational data present some challenges to NLPresearchers, including multilabel classification, a large number of classes,and limited available data. This study explored how automated labels generatedby NLP methods are comparable to human labels in the context of conversationson adulthood transition. We proposed strategies to handle three commonchallenges raised in psychological studies. Our findings showed that the deeplearning method with domain adaptation (RoBERTa-CON) outperformed all othermachine learning methods; and the hierarchical labelling system that weproposed was shown to help researchers strategically analyze conversationaldata. Our Python code and NLP model are available athttps://github.com/mlaricheva/automated_labeling.
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In lieu of an abstract here is the first paragraph: No other species remotelyapproaches the human capacity for the cultural evolution of novelty that isaccumulative, adaptive, and open-ended (i.e., with no a priori limit on thesize or scope of possibilities). By culture we mean extrasomaticadaptations--including behavior and technology--that are socially rather thansexually transmitted. This chapter synthesizes research from anthropology,psychology, archaeology, and agent-based modeling into a speculative yetcoherent account of two fundamental cognitive transitions underlying humancultural evolution that is consistent with contemporary psychology. While thechapter overlaps with a more technical paper on this topic (Gabora & Smith2018), it incorporates new research and elaborates a genetic component to ouroverall argument. The ideas in this chapter grew out of a non-Darwinianframework for cultural evolution, referred to as the Self-other Reorganization(SOR) theory of cultural evolution (Gabora, 2013, in press; Smith, 2013), whichwas inspired by research on the origin and earliest stage in the evolution oflife (Cornish-Bowden & C\'ardenas 2017; Goldenfeld, Biancalani, & Jafarpour,2017, Vetsigian, Woese, & Goldenfeld 2006; Woese, 2002). SOR bridgespsychological research on fundamental aspects of our human nature such ascreativity and our proclivity to reflect on ideas from different perspectives,with the literature on evolutionary approaches to cultural evolution thataspire to synthesize the behavioral sciences much as has been done for thebiological scientists. The current chapter is complementary to this effort, butless abstract; it attempts to ground the theory of cultural evolution in termsof cognitive transitions as suggested by archaeological evidence.
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In this work we consider the Peter principle and anti-Peter principle as thediscrete logistic and discrete inverse logistic equation. Especially we discussimprecisely estimated (by hierarchical control mechanism) carrying capacity,i.e. boundary (in)competence level of a hierarchy member. It implies that Peterprinciple holds two sub-principles. In the first one objective boundarycompetence level is increased for estimation error. In the second one objectiveboundary competence level is decreased for estimation error. Similarly,anti-Peter principle holds two sub-principles too. All this implies thatparadoxical situations that follow from Peter and anti-Peter principle can besimply removed by decrease of the error of hierarchical (social) control. Alsowe discuss cases by Peter principle when error of the boundary competence levelby estimation grows up. (Then, in fact, there is no estimation error butstimulation of the boundary level by control mechanism.) By first Petersub-principle it implies anarchy in the social structure or, correspondingly,cancer in the biology and medicine, schizophrenia in the psychology andinflation in the economy. By second Peter sub-principle it implies a totalitarysocial structure (dictature or caste regime) or multiplex sclerosis and otherautoimmune diseases in biology and medicine, servile mentality or low valuecomplex in psychology and depression by hyperactive political influences ineconomy. Finally, monotonus changes of the stimulated part of boundary levelcause corresponding phase transitions discussed on the example of theintrospection in the psychology.
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Psychological and social systems provide us with a natural domain for thestudy of anticipations because these systems are based on and operate in termsof intentionality. Psychological systems can be expected to contain a model ofthemselves and their environments social systems can be strongly anticipatoryand therefore co-construct their environments, for example, in techno-economic(co-)evolutions. Using Duboi's hyper-incursive and incursive formulations ofthe logistic equation, these two types of systems and their couplings can besimulated. In addition to their structural coupling, psychological and socialsystems are also coupled by providing meaning reflexively to each other'smeaning-processing. Luhmann's distinctions among (1) interactions betweenintentions at the micro-level, (2) organization at the meso-level, and (3)self-organization of the fluxes of meaningful communication at the global levelcan be modeled and simulated using three hyper-incursive equations. The globallevel of self-organizing interactions among fluxes of communication is retainedat the meso-level of organization. In a knowledge-based economy, these twolevels of anticipatory structuration can be expected to propel each other atthe supra-individual level.
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In recent years, WSNs are garnering lot of interest from research communitybecause of their unique characteristics and potential for enormous range ofapplications. Envision for new class of applications are being emerged such ashuman augmentation, enhancing social interaction etc. Misunderstanding ormisinterpretation of behaviors from individuals leads to social conflicts.There are various theories that classify people into different personalitytypes. Most of the existing theories rely on questionnaires, which is highlyunreliable. Anyone can lead such theories in practice to incorrectclassification intentionally or unintentionally. The objective of this researchis to investigate existing solutions and propose a basic infrastructure for anautomated context-aware psychological classification based on differentparameters. The idea is to use wearable sensors to sense and measure varioushuman body parameters (i.e. body temperature, blood pressure, perspiration,brain impulses etc) that coerce human psychological condition. The datacollected from these parameters is transformed in to information, to determinepersonality type, mood and psychological condition of interacting parties. Thisinformation is shared among counterparts to better understand each other inorder to avoid potential conflicting situations. We believe that it will helppeoples understand each other, improve their quality of life and minimizepossible conflicting situations.
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Background: software engineering research (SE) lacks theory and methodologiesfor addressing human aspects in software development. Development tasks areundertaken through cognitive processing activities. Affects (emotions, moods,feelings) have a linkage to cognitive processing activities and theproductivity of individuals. SE research needs to incorporate affectmeasurements to valorize human factors and to enhance management styles.  Objective: analyze the affects dimensions of valence, arousal, and dominanceof software developers and their real-time correlation with their self-assessedproductivity (sPR).  Method: repeated measurements design with 8 participants (4 students, 4professionals), conveniently sampled and studied individually over 90 minutesof programming. The analysis was performed by fitting a linear mixed- effects(LME) model.  Results: valence and dominance are positively correlated with the sPR. Themodel was able to express about 38% of deviance from the sPR. Many lessons werelearned when employing psychological measurements in SE and for fitting LME.  Conclusion: this article demonstrates the value of applying psychologicaltests in SE and echoes a call to valorize the human, individualized aspects ofsoftware developers. It reports a body of knowledge about affects, theirclassification, their measurement, and the best practices to performpsychological measurements in SE with LME models.
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Probabilistic graphical models such as Bayesian Networks are one of the mostpowerful structures known by the Computer Science community for derivingprobabilistic inferences. However, modern cognitive psychology has revealedthat human decisions could not follow the rules of classical probabilitytheory, because humans cannot process large amounts of data in order to makejudgements. Consequently, the inferences performed are based on limited datacoupled with several heuristics, leading to violations of the law of totalprobability. This means that probabilistic graphical models based on classicalprobability theory are too limited to fully simulate and explain variousaspects of human decision making.  Quantum probability theory was developed in order to accommodate theparadoxical findings that the classical theory could not explain. Recentfindings in cognitive psychology revealed that quantum probability can fullydescribe human decisions in an elegant framework. Their findings suggest that,before taking a decision, human thoughts are seen as superposed waves that caninterfere with each other, influencing the final decision.  In this work, we propose a new Bayesian Network based on the psychologicalfindings of cognitive scientists. We made experiments with two very well knownBayesian Networks from the literature. The results obtained revealed that thequantum like Bayesian Network can affect drastically the probabilisticinferences, specially when the levels of uncertainty of the network are veryhigh (no pieces of evidence observed). When the levels of uncertainty are verylow, then the proposed quantum like network collapses to its classicalcounterpart.
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Technology and collaboration enable dramatic increases in the size ofpsychological and psychiatric data collections, but finding structure in theselarge data sets with many collected variables is challenging. Decision treeensembles like random forests (Strobl, Malley, and Tutz, 2009) are a usefultool for finding structure, but are difficult to interpret with multipleoutcome variables which are often of interest in psychology. To find andinterpret structure in data sets with multiple outcomes and many predictors(possibly exceeding the sample size), we introduce a multivariate extension toa decision tree ensemble method called Gradient Boosted Regression Trees(Friedman, 2001). Our method, multivariate tree boosting, can be used foridentifying important predictors, detecting predictors with non-linear effectsand interactions without specification of such effects, and for identifyingpredictors that cause two or more outcome variables to covary withoutparametric assumptions. We provide the R package 'mvtboost' to estimate, tune,and interpret the resulting model, which extends the implementation ofunivariate boosting in the R package 'gbm' (Ridgeway, 2013) to continuous,multivariate outcomes. To illustrate the approach, we analyze predictors ofpsychological well-being (Ryff and Keyes, 1995). Simulations verify that ourapproach identifies predictors with non-linear effects and achieves highprediction accuracy, exceeding or matching the performance of (penalized)multivariate multiple regression and multivariate decision trees over a widerange of conditions.
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Social scientists have criticised computer models of pedestrian streams fortheir treatment of psychological crowds as mere aggregations of individuals.Indeed most models for evacuation dynamics use analogies from physics wherepedestrians are considered as particles. Although this ensures that the resultsof the simulation match important physical phenomena, such as the decelerationof the crowd with increasing density, social phenomena such as group processesare ignored. In particular, people in a crowd have social identities and sharethose social identities with the others in the crowd. The process of selfcategorisation determines norms within the crowd and influences how people willbehave in evacuation situations. We formulate the application of socialidentity in pedestrian simulation algorithmically. The goal is to examinewhether it is possible to carry over the psychological model to computer modelsof pedestrian motion so that simulation results correspond to observations fromcrowd psychology. That is, we quantify and formalise empirical research on andverbal descriptions of the effect of group identity on behaviour. We useuncertainty quantification to analyse the model's behaviour when we varycrucial model parameters. In this first approach we restrict ourselves to aspecific scenario that was thoroughly investigated by crowd psychologists andwhere some quantitative data is available: the bombing and subsequentevacuation of a London underground tube carriage on July 7th 2005.
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