Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task

✍ Scribed by Vincent Valton; Alain Marchand; Francoise Dellu-Hagedorn; Peggy Seriès

• Publisher: BioMed Central
• Year: 2011
• Tongue: English
• Weight: 139 KB
• Volume: 12
• Category: Article
• ISSN: 1471-2202
• DOI: 10.1186/1471-2202-12-s1-p294

✦ Synopsis

Deficits in decision-making have been repeatedly observed in various psychiatric disorders (i.e. ADHD, Pathological Gambling, Mania, OCD and Substance Abuse) as well as in frontal lobe patients. Such decision-making deficits are often assessed using the Iowa Gambling task (IGT) [1]. The IGT represents a realistic decision-making task where subjects are asked to choose between targets associated with rewards and penalties of varying likelihood and amplitude. Previous studies have shown that when healthy participants take the IGT, around a third of these perform poorly, similar to psychiatric patients [1].

Recently, these behavioral findings were successfully translated to animal research in a rodent version of the IGT, the Rat Gambling Task (RGT). In common with human studies, it was found that a third of a healthy population of rats exhibited poor decision-making performances [2]. The rats were tested in other tasks aiming at characterizing behavioral traits such as impulsivity, sensitivity to reward, cognitive inflexibility and risk seeking. Poor decision makers were always characterized by high scores for a combination of these behavioral traits.

Here we use a model of learning and decision-making in the RGT to answer the following questions: (1) how do the behavioral traits described above influence learning; (2) how is this manifested in terms of their decisionmaking performance?

In order to model the learning and decision process of the RGT, we used a TD-learning algorithm [3]. The model agent experiences the environment by learning the values of rewards and penalties for each state using trial and error sampling. As the agent gets a more accurate representation of the environment, it takes more appropriate decisions, using a 'softmax' action selection process. The