Topic: Bayesian learning

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation during exploration. Understanding the role of sleep in this process has been challenging because of the inaccessibility of place fields when animals are not actively exploring a maze. Here, we devised a novel Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings to track dynamically the spatial tuning of individual neurons during offline states in freely moving rats. Measuring these dynamic tunings, we found spatial representations within

Reinforcement Learning studies how animals and humans adapt to their environment by learning from experience which actions maximize reward acquisition. Extant research has focused on situations where learners receive veridical reward-feedback. However, as social beings, much of the feedback we receive is provided by others. Critically, such feedback may be misleading because others might manipulate us based on their own –even if well-intended– interests. For example, a colleague might spare your feelings, expressing enthusiasm regarding your imminent Cosyne submission when, they actually find

Aims: Social cognition hinges on the fundamental capability to perceive the presence of others. Research indicates that the mere presence of conspecifics can enhance task performance, particularly in simpler tasks, and is closely associated with the activity of context-dependent neural subpopulations. While attentional modulation (attentional-conflict theory) has long been posited as the primary mechanism driving social facilitation, the literature on the neurobiological underpinnings of this modulation remains scant at best. Understanding these mechanisms could yield critical insights into so

Adaptively navigating an uncertain social world often requires individuals to infer other people’s moral character based on their observed behavior. Recently, this inferential process has been elucidated through the lens of Bayesian learning, wherein beliefs about others’ character are dynamically updated in proportion to the uncertainty of those beliefs. This theoretical framework suggests that stress, a psychophysiological state intricately tied to uncertainty, may influence moral inference. Specifically, we hypothesized that acute stress would increase belief uncertainty and, consequently,