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Blaire Dube, Stephen Emrich, Naseem Al-Aidroos; More than a filter: Feature-based attention regulates the distribution of visual working memory resources. Journal of Vision 2017;17(10):206. doi: https://doi.org/10.1167/17.10.206.
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© ARVO (1962-2015); The Authors (2016-present)
How does feature-based attention regulate visual working memory (VWM) performance? The prominent filter account proposes that attention acts like a "bouncer" for VWM—the brain's "nightclub"—filtering out distracting information to ensure that access to VWM resources is reserved for relevant information. This account, however, originated from discrete-capacity models of VWM architecture, the assumptions of which have since been challenged. Across three experiments, we revisited the filter account by testing if feature-based attention plays a broader role in regulating VWM performance. Each experiment used partial report tasks in which participants memorized the colors of circle and square stimuli, and we provided a feature-based goal by manipulating the likelihood that one shape would be probed over the other across a range of probabilities. By decomposing participants' responses using mixture and variable-precision models, we estimated the contributions of guesses, non-target responses, and imprecise memory representations to their errors. Consistent with the filter account, participants were less likely to guess when the probed memory item matched the feature-based goal. Interestingly, this effect varied with the strength of the goal, even across high-probabilities where goal-matching information should always be prioritized, demonstrating strategic control over filter strength. Beyond this effect of attention on which stimuli were encoded, we also observed effects on how they were encoded: Estimates of both memory precision and non-target errors varied continuously with feature-based attention. The results demonstrate a new role for feature-based attention in dynamically regulating the distribution of resources within working memory so that the most relevant items are encoded with the greatest precision.
Meeting abstract presented at VSS 2017
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