Fronto-Striatal Circuits Optimize Feature-based Attention and Learning

Our new publication (Oemisch et al. (2018) Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning) provides the first 4-brain-area survey of how prediction error information in the anterior cingulate – ventral striatum and lateral prefrontal – caudate fronto-striatal loops relate to feature-based attention and learning. We found prediction errors that encode the specific stimulus feature that was reward relevant. This coding took place with stimuli having multiple feature dimensions. Reporting that neurons track the specific reward relevant feature suggests an attractive solution of credit assignment through a distributed feature-specific eligibility trace enabling ‘goal-directed’ synaptic plasticity changes across the entire fronto-striatal network.

Related News

Adaptive Learning needs Attention, Meta-learning and Working Memory

We tested which model mechanisms best explain how six animals learn attention sets and found a common set of most-important behavioral mechanisms that account for learning success.When learning attention sets is easy value based reinforcement learning and working memory are powerful, but when learning problems are more complex learning is more efficient with attention and […]

Computational Properties of Prefrontal Cortex – Workshop

We hosted a superb 2018 CPPC (Computational Properties of Prefrontal Cortex) Workshop at Vanderbilt. The workshop attracted more than 60 emerging and established (neuro-)scientists about how the prefrontal cortex works – See the program and more at the www link CPPC2018. This year had special sessions on value-based decision making and uncertainty, social cognition, functional […]

New 3D-object type with controllable feature space published with open-sourced code

The lab has a new publication showcasing and describing details of Quaddles: A multidimensional 3D object set with parametrically-controlled and customizable features. Quaddles have 5+ feature dimensions, each with multiple possible feature values that can be parametrically morphed, making it possible to generate a near arbitrary number of unique objects. Thanks to Marcus and Milad […]