Learning efficient representations of environmental priors in working memory.

ABSTRACT: Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer's estimate of the environmental prior. For instance, when retaining an estimate of an object's features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human

SUBMITTER: Eissa TL 

PROVIDER: S-EPMC10662764 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature