Computational Modeling of Behavior as a Window on Psychological Traits and States

Dopamine D2/3 receptor signaling is critical for flexible adaptive behavior; however, it is unclear whether D2, D3, or both receptor subtypes modulate precise signals of feedback and reward history that underlie optimal decision making. Here, PET with the radioligand [(11)C]-(+)-PHNO was used to quantify individual differences in putative D3 receptor availability in rodents trained on a novel three-choice spatial acquisition and reversal-learning task with probabilistic reinforcement. Binding of [(11)C]-(+)-PHNO in the midbrain was negatively related to the ability of rats to adapt to changes in rewarded locations, but not to the initial learning. Computational modeling of choice behavior in the reversal phase indicated that [(11)C]-(+)-PHNO binding in the midbrain was related to the learning rate and sensitivity to positive, but not negative, feedback. Administration of a D3-preferring agonist likewise impaired reversal performance by reducing the learning rate and sensitivity to positive feedback. These results demonstrate a previously unrecognized role for D3 receptors in select aspects of reinforcement learning and suggest that individual variation in midbrain D3 receptors influences flexible behavior. Our combined neuroimaging, behavioral, pharmacological, and computational approach implicates the dopamine D3 receptor in decision-making processes that are altered in psychiatric disorders. Flexible decision-making behavior is dependent upon dopamine D2/3 signaling in corticostriatal brain regions. However, the role of D3 receptors in adaptive, goal-directed behavior has not been thoroughly investigated. By combining PET imaging with the D3-preferring radioligand [(11)C]-(+)-PHNO, pharmacology, a novel three-choice probabilistic discrimination and reversal task and computational modeling of behavior in rats, we report that naturally occurring variation in [(11)C]-(+)-PHNO receptor availability relates to specific aspects of flexible decision making. We confirm these relationships using a D3-preferring agonist, thus identifying a unique role of midbrain D3 receptors in decision-making processes.

Characterizing the dynamics of behavior across multiple modalities and individuals is a vital component of computational behavior analysis. This is especially important in certain applications, such as psychotherapy, where individualized tracking of behavior patterns can provide valuable information about the patient’s mental state. Conventional methods that rely on aggregate statistics and correlational metrics may not always suffice, as they are often unable to capture causal relationships or evaluate the true probability of identified patterns. To address these challenges, we present a novel approach to learning multimodal and interpersonal representations of behavior dynamics during one-on-one interaction. Our approach is enabled by the introduction of a multiview extension of latent change score models, which facilitates the concurrent capture of both inter-modal and interpersonal behavior dynamics and the identification of directional relationships between them. A core advantage of our approach is its high level of interpretability while simultaneously achieving strong predictive performance. We evaluate our approach within the domain of therapist-client interactions, with the objective of gaining a deeper understanding about the collaborative relationship between the two, a crucial element of the therapeutic process. Our results demonstrate improved performance over conventional approaches that rely upon summary statistics or correlational metrics. Furthermore, since our multiview approach includes the explicit modeling of uncertainty, it naturally lends itself to integration with probabilistic classifiers, such as Gaussian process models. We demonstrate that this integration leads to even further improved performance, all the while maintaining highly interpretable qualities. Our analysis provides compelling motivation for further exploration of stochastic systems within computational models of behavior.

Orthodontic tooth movement (OTM) is associated with an inflammatory response, tooth pain (i.e. orthodontic pain) and changes in dental occlusion. Clinical realms and research evidence suggest that the sensory and jaw motor responses to OTM vary significantly among individuals. While some adjust well to orthodontic procedures, others may not and can experience significant pain or not adjust to occlusal changes. This is of concern, as clinicians cannot anticipate an individual's sensorimotor response to OTM. Converging evidence shows that some psychological states and traits significantly affect the sensorimotor response to OTM and may considerably affect an individual's adaptation to orthodontic or other dental procedures. We performed a topical review to synthesize the available knowledge about the behavioural mechanisms regulating the sensorimotor response to OTM, with the intent of informing orthodontic practitioners and researchers about specific psychological states and traits that should be considered while planning orthodontic treatment. We report on studies focusing on the role of anxiety, pain catastrophising, and somatosensory amplification (i.e. bodily hypervigilance), on sensory and jaw motor responses. Psychological states and traits can significantly affect sensory and jaw motor responses and a patient's adaptation to orthodontic procedures, although large interindividual variability exists. Clinicians can use validated instruments (checklists or questionnaires) to collect information about patients' psychological traits, which can assist in identifying those individuals who may not adjust well to orthodontic procedures. The information included in this manuscript also assists researchers investigating the effect of orthodontic procedures and or/appliances on orthodontic pain.

Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.

Introduction to Computational Modelling in Organizations - the Good Modelling Does, Charles L. Hulin and Daniel R. Ilgen Virtual Organizations, Steven T. Seitz The Impact of Organizational Interventions on Behaviours - an Examination of Different Models of Withdrawal, Kathleen A. Hanisch Comparing Different Models of Withdrawal Using a Computational Model, Mark Fichman Examining the Fit Between Empirical Data and Theoretical Models, Liberty J. Munson and Charles L. Hulin Modelling Withdrawal - Theoretical, Empirical and Methodological Implications, Nigel Nicholson Modelling Faking on Personality Tests, Michael J. Zickar Computational Models of Personality and Faking, Richard P. DeShon Simulating Effects of Pay for Performance Systems on Pay-Performance Relationships, Donald P. Schwab and Craig A. Olson Consequences of Organizational Reward Systems, John R. Hollenbeck Information Distribution, Participation and Group Decision - Explorations with the DISCUSS and SPEAK Models, Garold Stasses The DISCUSS and SPEAK Models - Lessons on the Value of Linking Theory, Empirical Research and Computer Simulation, M. Anjali Sastry Computational Modelling with Petri Nets - Solutions for Individual and Team Systems, Michael D. Coovert and David W. Dorsey Getting Entangled in One's Own (Petri) Net - On the Promises and Perils of Computational Modelling, Norbert L. Kerr.

Predicting the effects of in-car interface use on driver performance: an integrated model approach

Background: Self-evaluation, as an expression of self-concept, becomes increasingly complex in adolescence. Parental bonding, personality traits, and psychological status were the three main determining factors of adolescents’ self-evaluation. We aimed to tested associations through a systematic approach. Methods: Data were from the 2014 Chinese Family Panel Studies (CFPS). The association of parental bonding (measured by the Parental Bonding Instrument), personality traits (the Responsibility Scale), and psychological status (the Kessler Psychological Distress Scale) with self-evaluation measured in three domains (the Rosenberg Self-Esteem Scale, the Nowicki- Strickland Locus of Control Scale for children and the Self-Discipline Scale) were tested by the structural equation modeling (SEM) analysis. Results: A total of 892 Chinese adolescents were included. Significant positive correlations were found among self-evaluation, parental bonding, personality traits, and psychological status. The overall (direct and indirect) effect of parental bonding on adolescents’ self-evaluation was 0.49. Personality traits and psychological status were directly associated with adolescents’ self-evaluation. Conclusion: The use of SEM offered a detailed analysis of the correlations between parental bonding, personality traits, and psychological status and a systematic approach to investigate their direct and indirect effects on adolescents’ self-evaluation. The three factors directly associated with adolescents’ self-evaluation and parental bonding indirectly affected adolescents’ self-evaluation that was mediated by both personality traits and psychological status.

Decomposing conditioned avoidance performance with computational models

The common approach in morphological analysis of dendritic spines of mammalian neuronal cells is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia shaped. The corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate the synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist. We propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests. We compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify the differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. The source code of our software is freely available for non-commercial use1. Contact: d.plewczynski@cent.uw.edu.pl.

Breaking human social decision making into multiple components and then putting them together again

Chapter 11 - Computational Models of Developmental Mechanisms

Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.

For humans, meals are significant, not only to intake nutrients or feel satisfaction but also to feel connected with family and society through interpersonal communication. This study aimed to estimate and examine the psychological states and traits of texts describing eating experiences using BERT. Texts about positive, negative, and neutral eating experiences were collected from 877 crowd workers along with their psychological traits (loneliness and depression). The accuracy of the 6-label classification of the three psychological states × two eating situations (co-eating or eating alone) was 72%. Although the accuracies of the binary classification of loneliness and depression are approximately 55%, they are comparable with those of crowd workers. These results suggest that estimating psychological traits is more difficult than estimating psychological states from a single text per crowd worker. Further analyses revealed that the fine-tuned BERT classifiers of psychological traits use language features different from those of human raters.

Prior research suggests that cortisol responses to acute stress induction paradigms (i.e., "cortisol stress reactivity") likely arise from complex interactions between experiment-level, biological, and psychological factors. Here, we investigated the extent to which cortisol stress reactivity can be explained by psychological state and trait data alone when biological and experiment-level factors are strictly controlled. In a pooled dataset comprising five studies (total n = 443), healthy participants with no history of mental disorders were exposed to either the Maastricht Acute Stress Test experimental (MASTEXP; n = 297) or the no-stress control (MASTPLC; n = 146) condition. We calculated common cortisol stress reactivity metrics (peak reactivity, total cortisol turnover, and two binary responder classifications) and investigated associations with stress-induced changes in psychological state (momentary affect; PANAS), psychological traits (approach and avoidance tendencies; BIS/BAS), vital signs, and other contextual variables using LASSO generalized linear and logistic regression. Stress-induced changes in momentary states, particularly decreases in positive affect, were modestly associated with cortisol reactivity metrics, although the robustness of these associations was dependent on the metric employed. In contrast, negative affect was significantly associated with only one cortisol reactivity metric, while trait-level approach and avoidance tendencies showed no significant associations. Our results underscore the subtle nature of cortisol stress reactivity-psychological state/ trait data associations, lending further credence to the idea that cortisol stress reactivity, in reality, may likely arise from small contributions across diverse (experimental, biological, psychological) variables, rather than a single dominant psychological factor.

The trial-unique nonmatching to location (TUNL) touchscreen task shows promise as a translational assay of working memory (WM) deficits in rodent models of autism, ADHD, and schizophrenia. However, the low-level neurocognitive processes that drive behavior in the TUNL task have not been fully elucidated. In particular, it is commonly assumed that the TUNL task predominantly measures spatial WM dependent on hippocampal pattern separation, but this proposition has not previously been tested. In this project, we tested this question using computational modeling of behavior from male and female mice performing the TUNL task (N = 163 across three datasets; 158,843 trials). Using this approach, we empirically tested whether TUNL behavior solely measured retrospective WM, or whether it was possible to deconstruct behavior into additional neurocognitive subprocesses. Overall, contrary to common assumptions, modeling analyses revealed that behavior on the TUNL task did not primarily reflect retrospective spatial WM. Instead, behavior was best explained as a mixture of response strategies, including both retrospective WM (remembering the spatial location of a previous stimulus) and prospective WM (remembering an anticipated future behavioral response) as well as animal-specific response biases. These results suggest that retrospective spatial WM is just one of a number of cognitive subprocesses that contribute to choice behavior on the TUNL task. We suggest that findings can be understood within a resource-rational framework, and use computational model simulations to propose several task-design principles that we predict will maximize spatial WM and minimize alternative behavioral strategies in the TUNL task.SIGNIFICANCE STATEMENT Touchscreen tasks represent a paradigm shift for assessment of cognition in nonhuman animals by automating large-scale behavioral data collection. Their main relevance, however, depends on the assumption of functional equivalence to cognitive domains in humans. The trial-unique, delayed nonmatching to location (TUNL) touchscreen task has revolutionized the study of rodent spatial working memory. However, its assumption of functional equivalence to human spatial working memory is untested. We leveraged previously untapped single-trial TUNL data to uncover a novel set of hierarchically ordered cognitive processes that underlie mouse behavior on this task. The strategies used demonstrate multiple cognitive approaches to a single behavioral outcome and the requirement for more precise task design and sophisticated data analysis in interpreting rodent spatial working memory.