Attractors for action selection in working memory

A central feature of WM is its ablility to not only store information, but also utilise it. Most models of WM don't specify how information is read out and used. This shifts the burden to other systems. But recent work suggests that WM is task dependent, dynamic, and itself involves manipulation. We propose that WM representations should themselves be able to make decisions and select actions. WM should be effectual. Accordingly, one recent perspective suggests that WM holds a set of rules for transforming stimuli to responses. For example in change detection tasks, WM may function as a "matched filter", and in graded judgements, as a decision-making circuit. Can we model this? We propose that this functionality corresponds to the ability to set up multiple new attractor states. We show that a minimal array of Hebbian units can rapidly carve out an attractor that binds the features of an item. A matching input can trigger competition between these attractors, with the winner potentially triggering an action. The dynamics can "gate" items in and out of memory, but without needing a gatekeeper. We think of this as a primitive but concrete implementation of a multiple demand network, holding pointers that bind features in sensory stores. Competition between plastic attractors replicates Hick's law of decisions, trial history effects, and some conflict effects previously explained by "event files". We were also able to apply plastic attractors to a simple situation where WM gets put to immediate use: Visual search. The template that is being searched for is encoded into WM, and the search display acts as a memory probe, triggering attention to select the matching item. The model also extends to continuous feature domains, where it generates predictions similar to the interference model. We hope this work encourages a view where WM encodes information in terms of active effects or responses, making representations inherently potent.