Topic: learning rate

Humans are capable of generalising local pairwise relations to transitively infer global relationships between items, states or events. Recent research has shown that asymmetric learning, where beliefs about only the winners (or losers) of local comparisons are updated, is well-suited to allow human and artificial agents to infer relational structures from sparse feedback. However, less is known about how already-acquired knowledge and subsequent learning policies are impacted by experiences that violate the inferred monotonic ordering of items, such as when agents learn that A is greater than

Animals and humans have multiple reinforcement learning (RL) systems, especially a hard-wired Pavlovian system that learns state-outcome associations and promotes an approach to rewards and avoidance of punishment; and an instrumental system that learns values based on state-action-outcome associations. Conflict between the two systems sometimes leads to suboptimal decisions including pathological behaviors but factors that affect the conflict remain largely unclear. Meanwhile, previous findings suggest that taxing cognitive resources alters several decision-making processes and a balance betw

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how animals learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large arena. Combining core features of quantitative models of foraging and decision-making, we developed a novel reinforcement learning (