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Forgetting phenomena in the Iowa Gambling Task: a new computational model among diverse participants
Tiancheng Yang1, Chenghan Xie2, Xuehe Wang1
1School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China.
Introduction:
The Iowa Gambling Task (IGT) is a widely used paradigm for evaluating decision-making and executive functioning, yet existing computational models seldom account for the phenomenon of forgetting, which is critical to understanding dynamic decision processes.
Methods:
We developed the Exploitation and Exploration with Forgetting (EEF) model, which integrates a dynamic forgetting parameter (λ) and participants' first-choice priors into a unified reinforcement-learning framework. The EEF model was fitted to choice data from 504 healthy individuals performing the standard 100-trial IGT. Model performance was assessed via goodness-of-fit comparisons (BIC/AIC/Free Energy), parameter- and model-recovery simulations, and behavioral validation.
Results:
Across multiple cohorts, the EEF model achieved superior fit relative to five established models. We introduce two novel metrics-Sequential Exploration Decay (SED) and Forgetting Interval (FI)-to quantify how forgetting shapes exploratory behavior. The EEF model's SED and FI values closely matched empirical data, and further analyses revealed systematic effects of age and gambling frequency on forgetting and decision strategies.
Discussion:
Our findings underscore the fundamental role of forgetting in complex decision-making environments. By explicitly modeling information decay, the EEF framework offers novel insights into cognitive dynamics across the lifespan and behavioral contexts, and provides a parsimonious yet powerful tool for future computational and empirical research.
