Learning efficient representations of environmental priors in working memory

Tahra L Eissa and Zachary P Kilpatrick

PLOS Computational Biology, 2023, vol. 19, issue 11, 1-28

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 response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects’ response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies.Author summary: Working memory is known to play an important role in cognition, allowing us to maintain information in our memory for short periods without a constant stimuli. However, humans display limitations in working memory, such as recalling certain stimuli more frequently and accurately than others. We propose that these recall biases are based on our experience of common stimuli in our environment and driven by goal of efficiently reducing error by remembering common stimuli with more accuracy than rare stimuli. Here, we develop a model that updates an observer’s beliefs about the statistics of stimuli in an environment based on experience, biasing working memory recall such that common stimuli are remembered better. We then show that most human subjects’ responses from a previously published working memory task are better matched to a model that learns in an experience-dependent way compared to models with fixed biases. Finally, we identify a plausible neural mechanism for environmental experience-updating to show how the brain could implement this efficient strategy.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011622

DOI: 10.1371/journal.pcbi.1011622

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