scholarly journals Modeling decision-making under uncertainty: a direct comparison study between human and mouse gambling data

2019 ◽  
Author(s):  
Lidia Cabeza ◽  
Julie Giustiniani ◽  
Thibault Chabin ◽  
Bahrie Ramadan ◽  
Coralie Joucla ◽  
...  
Keyword(s):  
Decision Making ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Evolutionary Process ◽  
Cost Benefit ◽  
Optimal Choice ◽  
Face Validity ◽  
Decision Making Under Uncertainty ◽  
Gambling Task ◽  
Good Decision

AbstractDecision-making is a conserved evolutionary process enabling to choose one option among several alternatives, and relying on reward and cognitive control systems. The Iowa Gambling Task allows to assess human decision-making under uncertainty by presenting four cards decks with various cost-benefit probabilities. Participants seek to maximize their monetary gains by developing long-term optimal choice strategies. Animal versions have been adapted with nutritional rewards but interspecies data comparisons are still scarce. Our study directly compared physiological decision-making performances between humans and wild-type C57BL/6 mice. Human subjects fulfilled an electronic Iowa Gambling Task version while mice performed a maze-based adaptation with four arms baited in a probabilistic way. Our data show closely matching performances among species with similar patterns of choice behaviors. Moreover, both populations clustered into good, intermediate, and poor decision-making categories with similar proportions. Remarkably, mice good decision-makers behaved as humans of the same category, but slight differences among species have been evidenced for the other two subpopulations. Overall, our direct comparative study confirms the good face validity of the rodent gambling task. Extended behavioral characterization and pathological animal models should help strengthen its construct validity and disentangle determinants of decision-making in animals and humans.

scholarly journals Predicting human decision making in psychological tasks with recurrent neural networks

2021 ◽  
Author(s):  
Baihan Lin ◽  
Djallel Bouneffouf ◽  
Guillermo Cecchi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prisoner's Dilemma ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Prisoner’S Dilemma ◽  
Gambling Task ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Iterated Prisoner’S Dilemma

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.

scholarly journals Neurocognitive mechanisms of decision making in Iowa Gambling Task

2016 ◽  
Vol 5 (4) ◽  
pp. 50-58
Author(s):  
O.O. Zinchenko
Keyword(s):  
Decision Making ◽  
Iowa Gambling Task ◽  
Empirical Models ◽  
Ecological Approach ◽  
Decision Making Under Uncertainty ◽  
Gambling Task ◽  
Neural Basis

The article is devoted to the problem of understanding the decision making under uncertainty. The promising way of investigating the mechanisms of decision making is to use ecologically valid empirical models of decision-making situations. Iowa Gambling Task has been developed to allow research in ecological approach. One of the most prominent questions is to determine neural basis involved in holistic decision making. The article provides an overview of foreign publications and studies on the issue of decision making under uncertainty in case of Iowa Gambling Task.

scholarly journals Motivational Influence on Decision-Making under Uncertainty during the Iowa Gambling Task: A Review of the Literature

2017 ◽  
Vol 4 (4) ◽  
Author(s):  
Julie Giustiniani ◽  
Djamila Bennabi ◽  
Djamila Bennabi ◽  
Benoit Trojak ◽  
Emmanuel Haffen
Keyword(s):  
Decision Making ◽  
Iowa Gambling Task ◽  
Point Of View ◽  
Anterior Cingulate ◽  
Decision Making Under Uncertainty ◽  
Gambling Task ◽  
Somatic Markers ◽  
Two Factors ◽  
Motivational Influence ◽  
Future Outcomes

The aim of this article was to review the different levels of interaction between motivation and decision-making under uncertainty, based on the Iowa Gambling Task, with specific attention to their neuronal structures. The influence of motivation has been observed on many cognitive functions, and its influence appears to be important on the decision-making process under uncertainty. However, few studies treat this influence. Several structures have been found to be implied in both motivational and decision making processes. The anterior cingulate cortex is an essential interface between motivation and executive functions. In addition, the activity in the ventral striatum is correlated with motivation while this region is under the influence of somatic markers. The interaction of both structures leads to an anticipation of future outcomes. The ventromedial prefrontal cortex is under the influence of the motivational states that potentiates the somatic markers effect on the directional and activational aspect of behaviors. In this review we were able to show that the level of uncertainty and the degree of motivation are two factors that influence action selection and that this interaction is visible from a behavioral and neuronal point of view.

Differences in Reward and Persistency characterise Behavior in Chronic Pain for the Iowa Gambling Task in a Web-based Lab-in-the-field study (Preprint)

2020 ◽  
Author(s):  
Lili Zhang ◽  
Himanshu Vashisht ◽  
Alekhya Nethra ◽  
Brian Slattery ◽  
Tomas Ward
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Chronic Pain ◽  
Iowa Gambling Task ◽  
Learning Curves ◽  
Self Report ◽  
Healthy Controls ◽  
Gambling Task ◽  
Convenience Sample ◽  
Pain Experiences

BACKGROUND Chronic pain is a significant world-wide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional lab experiments to date. In such experiments researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain impacts decision-making captured via lab-in-the-field experiments. Although such settings can introduce more experimental uncertainty, it is believed that collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain. OBJECTIVE We aim to quantify decision-making differences between chronic pain individuals and healthy controls in a lab-in-the-field environment through taking advantage of internet technologies and social media. METHODS A cross-sectional design with independent groups was employed. A convenience sample of 45 participants were recruited through social media - 20 participants who self-reported living with chronic pain, and 25 people with no pain or who were living with pain for less than 6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making, i.e. the Iowa Gambling Task (IGT) in their web browser at a time and location of their choice without supervision. RESULTS Standard behavioral analysis revealed no differences in learning strategies between the two groups although qualitative differences could be observed in learning curves. However, computational modelling revealed that individuals with chronic pain were quicker to update their behavior relative to healthy controls, which reflected their increased learning rate (95% HDI from 0.66 to 0.99) when fitted with the VPP model. This result was further validated and extended on the ORL model because higher differences (95% HDI from 0.16 to 0.47) between the reward and punishment learning rates were observed when fitted on this model, indicating that chronic pain individuals were more sensitive to rewards. It was also found that they were less persistent in their choices during the IGT compared to controls, a fact reflected by their decreased outcome perseverance (95% HDI from -4.38 to -0.21) when fitted using the ORL model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. CONCLUSIONS We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our lab-in-the-field experiment. In this case study, it was demonstrated that compared to standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand and explain the differences in decision- making behavior in the context of chronic pain outside the lab.

Underlying decision making processes on Iowa Gambling Task

2019 ◽  
Vol 39 ◽  
pp. 63-69 ◽  
Author(s):  
Rajesh Kumar ◽  
Keshav Janakiprasad Kumar ◽  
Vivek Benegal
Keyword(s):  
Decision Making ◽  
Iowa Gambling Task ◽  
Gambling Task ◽  
Decision Making Processes
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