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Timothy F. Brady, Joshua B. Tenenbaum; Hierarchical Encoding in Visual Working Memory. Journal of Vision 2010;10(7):777. doi: https://doi.org/10.1167/10.7.777.
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When remembering a real-world scene, people encode both detailed information about specific objects and higher-order information like the overall gist of the scene. However, existing formal models of visual working memory capacity (e.g., Cowan's K) generally assume that people encode individual items but do not represent the higher-order structure of the display. We present a probabilistic model of VWM that generalizes Cowan's K to encode not only specific items from a display, but also higher-order information. While higher-order information can take many forms, we begin with a simple summary representation: how likely neighboring items are to be the same color. In Experiment 1, we test this model on displays of randomly chosen colored dots (Luck & Vogel, 1997). In Experiment 2, we generalize the model to displays where the dots are purposefully arranged in patterns. In both experiments, 75 observers detected changes in each individual display, which allowed us to calculate d' for a particular change in a particular display (range: d′=0.8-3.8). Results show that observers are highly consistent about which changes are easy or difficult to detect, even in standard colored dot displays (split-half correlations=0.60-0.76). Furthermore, the correlation between observers d′ and the model d′ is r=0.45 (p<0.01) in the randomly generated displays and r=0.72 (p<0.001) in the patterned displays, suggesting the model's simple summary representation captures which changes people are likely to detect. By contrast, the simpler model of change detection typically used in calculations of VWM capacity does not predict any reliable differences in difficulty between displays. We conclude that even in simple VWM displays items are not represented independently, and that models of VWM need to be expanded to take into account this non-independence between items before we can usefully make predictions about observers' memory capacity in real-world scenes.
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