Open AccessVisual ZIP files: Viewers beat capacity limits by compressing redundant features across objects.
Author(s) -
Hauke S. Meyerhoff,
Nicole Jardine,
Mike Stieff,
Mary Hegarty,
Steven Franconeri
Publication year - 2021
Publication title -
journal of experimental psychology. human perception and performance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.691
H-Index - 148
eISSN - 1939-1277
pISSN - 0096-1523
DOI - 10.1037/xhp0000879
Subject(s) - computer science , colored , artificial intelligence , computer vision , set (abstract data type) , feature (linguistics) , redundancy (engineering) , pattern recognition (psychology) , mathematics , linguistics , philosophy , materials science , composite material , programming language , operating system
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).