Topic: goal-directed learning

The sensory cortex is widely considered to be specialized for perception by interpreting complex sensory patterns while also exhibiting structured forms of representational plasticity of behaviorally-relevant stimuli. Recent evidence, however, suggests that sensory cortical neurons directly encode non-sensory variables such as movement and valence. Conjoint representations of sensory and non-sensory variables in the same network could further hone perception or, alternatively, subserve more integrative cognitive processes. Here, we ask the extent to which the sensory cortex is an associative e

While simple value-based learning efficiently predicts an outcome in a stable environment, goal-directed learning can deal with dynamic and uncertain environments1. It is this complex nature that poses a challenge for experimental design to investigate such learning processes. For example, it is hard to predict how task manipulations affect the latent process of goal-directed learning. Here we present a computational framework of goal-directed task control to guide goal-directed learning. The proposed framework is based on an asymmetric two-player game setting: while a computational model of h

Behavioral performance during goal-directed learning is typically measured in the presence of reinforcement. In this context, learning has been described as a slow process with high inter-subject variability. Exploration of the neural mechanisms, therefore, has focused on identifying dynamics concomitant with these slow performance improvements. Recent work, however, has shown that task acquisition is much faster and more stereotyped than previously thought1. Performance was evaluated daily in reinforced and non-reinforced (‘probe’) trials. These probe trials revealed a rapid and stereotyped a