Stochastic attractor models of visual working memory

Abstract

This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.

Summary

Penny's study explores stochastic attractor models of visual working memory, proposing that memory traces evolve according to stochastic attractor dynamics. The study adapts these models to account for multiple items within a single trial, offering a better explanation of behavioral data from continuous-report tasks. The models suggest that swap-errors occur when memory traces diffuse and are captured by the attractors of other items. Additionally, they indicate that empirical memory performance is characterized by a stochastic process with error-correcting dynamics.