Visual ZIP files: Viewers beat capacity limits by compressing redundant features across objects.

Given a set of simple objects, visual working memory capacity drops from 3 to 4 units down to only 1 to 2 units when the display rotates. But real-world STEM experts somehow overcome these limits. Here, we study a potential domain-general mechanism that might help experts exceed these limits: compressing information based on redundant visual features. Participants briefly saw 4 colored shapes, either all distinct or with repetitions of color, shape, or paired Color + Shape (e.g., two green squares among a blue triangle and a yellow diamond), with a concurrent verbal suppression task. Participants reported potential swaps (change/no change) in a rotated view. In Experiments 1a through 1c, repeating features improved performance for color, shape, and paired Color + Shape. Critically, Experiments 2a and 2b found that the benefits of repetitions were most pronounced when the repeated objects shared both feature dimensions (i.e., two green squares). When color and shape repetitions were split across different objects (e.g., green square, green triangle, red triangle), the benefit was reduced to the level of a single redundant feature, suggesting that feature-based grouping underlies the redundancy benefit. Visual compression is an effective encoding strategy that can spatially tag features that repeat. (PsycInfo Database Record (c) 2020 APA, all rights reserved) Public Significance Statement—The ability to compare objects across a rotation is limited to extremely simple objects, yet STEM experts such as chemists appear to circumvent this limitation. Understanding the limits of visuospatial thinking and mechanisms of overcoming these limitations is crucial for developing training supporting STEM-relevant abilities. Here, we study a domain-general mechanism that might allow people to exceed known limitations—leveraging redundant feature information. Our results show that the ability to detect swaps between rotated views is higher when people leverage redundant feature information, but that this advantage is limited to a single group of objects that share the same set of redundant features.