Topic: reinforcement learning models

From picking dinner to college courses, the choices we make reflect our past experiences and momentary preferences. The value of a choice is inherently subjective, internal to a decision maker, yet we need to identify objective measures to study its neural basis. According to learning theory, value can be inferred from reinforcement history through an algorithmic computation (‘model-inferred value’). In contrast, in behavioral economics, appropriately designed tasks can elicit choice patterns that reveal subjective preferences (‘revealed choice value’). However, the relationship between these

Adaptive behavior in a dynamically changing environment requires learning the value of actions, and updating these values in response to changes in the environment. Several studies have investigated the role of the striatum, and its key neuromodulators, dopamine (DA) and acetylcholine (Ach), in this process. However, these studies usually investigated these neuromodulatory signals in animals which had undergone extensive pre-training, and were thus experts at flexibly adapting to new action values. In addition, most of these studies did not explore the dynamics of these signals simultaneously,