Prospect Valence Learning Research Articles

Differential impairments underlying decision making in anorexia nervosa and bulimia nervosa: A cognitive modeling analysis

This study examined the underlying processes of decision-making impairments in individuals with anorexia nervosa (AN) and bulimia nervosa (BN). We deconstructed their performance on the widely used decision task, the Iowa Gambling Task (IGT) into cognitive, motivational, and response processes using cognitive modeling analysis. We hypothesized that IGT performance would be characterized by impaired memory functions and heightened punishment sensitivity in AN, and by elevated sensitivity to reward as opposed to punishment in BN. We analyzed trial-by-trial data of IGT obtained from 224 individuals: 94 individuals with AN, 63 with BN, and 67 healthy comparison individuals (HC). The prospect valence learning model was used to assess cognitive, motivational, and response processes underlying IGT performance. Individuals with AN showed marginally impaired IGT performance compared to HC. Their performance was characterized by impairments in memory functions. Individuals with BN showed significantly impaired IGT performance compared to HC. They showed greater relative sensitivity to gains as opposed to losses than HC. Memory functions in AN were positively correlated with body mass index. This study identified differential impairments underlying IGT performance in AN and BN. Findings suggest that impaired decision making in AN might involve impaired memory functions. Impaired decision making in BN might involve altered reward and punishment sensitivity.

A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning

The Iowa gambling task (IGT) is one of the most popular tasks used to study decisionmaking deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we present a comparison of three models—the EV and PVL models, and a combination of these models (EV-PU)—based on the method of parameter space partitioning. This method allows us to assess the choice patterns predicted by the models across their entire parameter space. Our results show that the EV model is unable to account for a frequency-of-losses effect, whereas the PVL and EV-PU models are unable to account for a pronounced preference for the bad decks with many switches. All three models underrepresent pronounced choice patterns that are frequently seen in experiments. Overall, our results suggest that the search of an appropriate IGT model has not yet come to an end.

Validating the PVL-Delta model for the Iowa gambling task

Decision-making deficits in clinical populations are often assessed with the Iowa gambling task (IGT). Performance on this task is driven by latent psychological processes, the assessment of which requires an analysis using cognitive models. Two popular examples of such models are the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models. These models have recently been subjected to sophisticated procedures of model checking, spawning a hybrid version of the EV and PVL models—the PVL-Delta model. In order to test the validity of the PVL-Delta model we present a parameter space partitioning (PSP) study and a test of selective influence. The PSP study allows one to assess the choice patterns that the PVL-Delta model generates across its entire parameter space. The PSP study revealed that the model accounts for empirical choice patterns featuring a preference for the good decks or the decks with infrequent losses; however, the model fails to account for empirical choice patterns featuring a preference for the bad decks. The test of selective influence investigates the effectiveness of experimental manipulations designed to target only a single model parameter. This test showed that the manipulations were successful for all but one parameter. To conclude, despite a few shortcomings, the PVL-Delta model seems to be a better IGT model than the popular EV and PVL models.

Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models

The Iowa gambling task (IGT) has been used in numerous studies, often to examine decision-making performance in different clinical populations. Reinforcement learning (RL) models such as the expectancy valence (EV) model have often been used to characterize choice behavior in this work, and accordingly, parameter differences from these models have been used to examine differences in decision-making processes between different populations. These RL models assume a strategy whereby participants incrementally update the expected rewards for each option and probabilistically select options with higher expected rewards. Here we show that a formal model that assumes a win-stay/lose-shift (WSLS) strategy--which is sensitive only to the outcome of the previous choice--provides the best fit to IGT data from about half of our sample of healthy young adults, and that a prospect valence learning (PVL) model that utilizes a decay reinforcement learning rule provides the best fit to the other half of the data. Further analyses suggested that the better fits of the WSLS model to many participants' data were not due to an enhanced ability of the WSLS model to mimic the RL strategy assumed by the PVL and EV models. These results suggest that WSLS is a common strategy in the IGT and that both heuristic-based and RL-based models should be used to inform decision-making behavior in the IGT and similar choice tasks.

Unconfounding of reward frequency and magnitude in the Iowa Gambling Task: A comparison between the Iowa and Soochow gambling tasks in opiate users

Event Abstract Back to Event Unconfounding of reward frequency and magnitude in the Iowa Gambling Task: A comparison between the Iowa and Soochow gambling tasks in opiate users Julie Stout1, 2*, Rebecca Kerestes3, Dan Upton1, Junyi Dai2 and Jerome Busemeyer2 1 Monash University, School of Psychology and Psychiatry, Australia 2 Indiana University, Psychological and Brain Sciences, Australia 3 University of Melbourne, Melbourne Neuropsychiatry Centre, United States Poor performance on the Iowa Gambling Task (IGT) is routinely attributed to an inability to be guided by future consequences, yet the IGT design confounds the frequency and magnitudes of rewards and losses. The Soochow Gambling Task (SGT) was designed to have similar overall task structure, but to alter payoffs to unconfound reward frequency and long-term outcomes. In this study, we compared the IGT and the SGT in 26 opiate users and 27 healthy controls to determine the influence of reward frequency on choice behavior. In the IGT, the opiate user group performed more poorly than controls, whereas in the SGT, both groups performed poorly and did not differ. We found that across both tasks, subjects appeared to prefer alternatives that won most frequently. Examination of the data further reveal that the decrement in performance seen in opiate users appears to relate their inability to use gain-loss frequencies to overcome their initial attraction alternatives with low frequency losses. Current modeling analyses using the Prospect Valence Learning Model are investigating whether levels of loss aversion significantly predict performance in the IGT and SGT. Acknowledgements This project was funded in part by Australian Research Council Discovery Project grant DP110100696. References Upton, D.J., Kerestes, R., Stout, J.C. (2012). Comparing the Iowa and Soochow gambling tasks in opiate users. Frontiers in Neuroscience: Decision Neuroscience 6, 1-8. Keywords: Decision Making, Cognitive Modeling, opiate users, Addiction, Risky choice Conference: ACNS-2012 Australasian Cognitive Neuroscience Conference, Brisbane, Australia, 29 Nov - 2 Dec, 2012. Presentation Type: Poster Presentation Topic: Executive Processes Citation: Stout J, Kerestes R, Upton D, Dai J and Busemeyer J (2012). Unconfounding of reward frequency and magnitude in the Iowa Gambling Task: A comparison between the Iowa and Soochow gambling tasks in opiate users. Conference Abstract: ACNS-2012 Australasian Cognitive Neuroscience Conference. doi: 10.3389/conf.fnhum.2012.208.00089 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Oct 2012; Published Online: 07 Nov 2012. * Correspondence: Prof. Julie Stout, Monash University, School of Psychology and Psychiatry, Clayton, Vic, 3800, Australia, julie.stout@monash.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Julie Stout Rebecca Kerestes Dan Upton Junyi Dai Jerome Busemeyer Google Julie Stout Rebecca Kerestes Dan Upton Junyi Dai Jerome Busemeyer Google Scholar Julie Stout Rebecca Kerestes Dan Upton Junyi Dai Jerome Busemeyer PubMed Julie Stout Rebecca Kerestes Dan Upton Junyi Dai Jerome Busemeyer Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Cognitive mechanisms underlying risky decision-making in chronic cannabis users

Chronic cannabis users are known to be impaired on a test of decision-making, the Iowa Gambling Task (IGT). Computational models of the psychological processes underlying this impairment have the potential to provide a rich description of the psychological characteristics of poor performers within particular clinical groups. We used two computational models of IGT performance, the Expectancy Valence Learning model (EVL) and the Prospect Valence Learning model (PVL), to assess motivational, memory, and response processes in 17 chronic cannabis abusers and 15 control participants. Model comparison and simulation methods revealed that the PVL model explained the observed data better than the EVL model. Results indicated that cannabis abusers tended to be under-influenced by loss magnitude, treating each loss as a constant and minor negative outcome regardless of the size of the loss. In addition, they were more influenced by gains, and made decisions that were less consistent with their expectancies relative to non-using controls.

S.23.05 Neurotransmitter challenges in nicotine addiction

Our everyday-life comprises a multitude of decisions that we take whilst trying to maximize advantageous outcomes, limit risks and update current needs. The cognitive processes that guide decision making as well as the brain circuits they are based on are only poorly understood. Numerous studies point to a potential role of dopamine and nicotine in decision making but less is known about their interactions. Here, 26 healthy male subjects performed the Iowa Gambling Task (IGT) in two sessions following the administration of either nicotine or placebo. Striatal dopamine transporter (DAT) binding was measured by single-photon emission computed tomography (SPECT). Results indicate that lower DAT levels were associated with better performance in the IGT (p=0.0004). Cognitive modelling analysis using the prospect valence learning (PVL) model indicated that low DAT subjects’ performance deteriorated following nicotine administration as indicated by an increased learning rate and a decreased response consistency. Our results shed light on the neurochemistry underlying reward-based decision making in humans by demonstrating a significant interaction between nicotine and the DAT. The observed interaction is consistent with the hypothesized associations between DAT expression and extracellular dopamine levels, suggestive of an inverted U-shape relationship between baseline dopamine and magnitude in response to a pro-dopaminergic compound. Our findings are of particular interest in the context of psychiatric disorders where aberrant decision making represents a part of the core symptomatology, such as addiction, schizophrenia or depression.