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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

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Gambling Environment Exposure Increases Temporal Discounting but Improves Model-Based Control in Regular Slot-Machine

Ben Wagner^1,2, David Mathar¹, Jan Peters¹

¹Department of Psychology, Biological Psychology, University of Cologne, Cologne, Germany.

Computational Psychiatry (Cambridge, Mass.)

|May 22, 2024

Summary

This summary is machine-generated.

Gambling disorder impacts decision-making. Exposure to gambling cues worsened temporal discounting but improved model-based learning in regular gamblers, challenging habit theories of addiction.

Keywords:

Neuroscience addiction cognitive modelling real-life environments transdiagnostic traits

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Area of Science:

Neuroscience
Psychology
Behavioral Economics

Background:

Gambling disorder is a behavioral addiction with significant negative consequences.
Computational markers like temporal discounting and model-based reinforcement learning are implicated in addiction.
Previous research suggests gambling disorder is associated with increased temporal discounting and reduced model-based learning.

Purpose of the Study:

To investigate the impact of real-life gambling environments on temporal discounting and model-based reinforcement learning.
To examine how gambling cue exposure modulates these computational markers in regular gamblers.

Main Methods:

A pre-registered study involving 30 regular gamblers (DSM-5 score range 3-9).
Participants completed temporal discounting and model-based reinforcement learning tasks in both neutral (café) and gambling-related (slot-machine venue) environments.
Data were analyzed using drift diffusion models and hierarchical Bayesian estimation.

Main Results:

Gamblers exhibited steeper temporal discounting in the gambling-related context compared to the neutral context.
This increased temporal discounting positively correlated with gambling-related cognitive distortions.
Contrary to hypotheses, model-based reinforcement learning performance improved in the gambling environment.

Conclusions:

Real-life gambling cue exposure modulates temporal discounting and model-based reinforcement learning in opposite directions.
These findings challenge habit theories of addiction.
Computational markers of psychopathology are significantly influenced by environmental context.