Compact representations of the environment allow humans to behave efficiently in a
complex world. Reinforcement learning models capture many behavioral and neural effects …

The central theme of this review is the dynamic interaction between information selection
and learning. We pose a fundamental question about this interaction: How do we learn what …

Quantitative finance has had a long tradition of a bottom-up approach to complex systems
inference via multi-agent systems (MAS). These statistical tools are based on modelling …

Statistical learning enables learners to extract the environmental regularities necessary to
piece together the structure of their worlds. The capacity for statistical learning and its …

Social learning is complex, but people often seem to navigate social environments with
ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that …

People can use abstract rules to flexibly configure and select actions for specific situations,
yet how exactly rules shape actions toward specific sensory and/or motor requirements …

Highlights•Optogenetic activation and inactivation of dopamine neurons mimic TD error
signals as predicted from reinforcement learning models.•When the current state is …

Animals rely on learned associations to make decisions. Associations can be based on
relationships between object features (eg, the three leaflets of poison ivy leaves) and …

In making decisions, we often choose from among options with multiple value-relevant
attributes. Neuroeconomic models propose that the value associated with each attribute is …

The striatum and medial temporal lobes (MTL) exhibit dissociable roles during learning.
Whereas the striatum and its network of thalamic relays and cortical nodes are necessary for …