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 computa...
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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.
The exponential growth of IoT gadgets has enabled previously unimaginable levels of connectedness and convenience, but it has also introduced a significant new difficulty: battery life. In this study, we investigate h...
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The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performanc...
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The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.
An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically test...
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An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.
A vicious cycle through which fear and pain maintain each other explains the development and maintenance of disorders involving fear conditioning. While the behavioral processes and neurobiological circuits of fear co...
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A vicious cycle through which fear and pain maintain each other explains the development and maintenance of disorders involving fear conditioning. While the behavioral processes and neurobiological circuits of fear conditioning have been extensively studied, the effects of fear learning on pain remain poorly understood. The objectives of this thesis were to examine the effects of fear conditioning on the neuropsychophysiology of pain, and the factors that could moderate these effects. The effects of fear learning on pain were examined in Study 1 in 47 human participants during a delay Pavlovian classical fear conditioning task. Conditioned stimuli were abstract visual cues that co-terminated with a painful electric shock on 50% of trials. Pain ratings and the spinal nociceptive flexion reflex were recorded in response to each US, and anticipatory skin conductance responses were recorded to each CS. A computational model of reinforcementlearning was fitted to anticipatory SCRs and used to estimate fear learning parameters of expected shock probabilities and associability (uncertainty) to each CS+ paired. Both fear learning parameters positively predicted pain responses. These effects operated in part directly on pain ratings, and in part indirectly by facilitating ascending spinal nociceptive activity. The results also showed that the mediation of the effects of fear learning on pain by spinal nociception was enhanced for individuals reporting more trait harm vigilance, and decreased for individuals reporting more emotional detachment. In Study 2, we investigated the role of long term mindfulness meditation experience on the effects of fear learning on pain. Eleven experienced meditators (>1000 hours of experience) were tested using the same experimental and analysis protocol as in Study1, and were compared with the meditation-naive participants from Study1. Compared to controls, experienced meditators showed an overall reduction in pain ratings during fear learning
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