Quantifying error distributions in crowding

Abstract

When multiple objects are in close proximity, observers have difficulty identifying them individually. Two classes of theories aim to account for this crowding phenomenon: spatial pooling and spatial substitution. Variations of these accounts predict different patterns of errors in crowded displays. Here we aim to characterize the kinds of errors that people make during crowding by comparing a number of error models across three experiments in which we manipulate flanker spacing, display eccentricity, and precueing duration. We find that both spatial intrusions and individual letter confusions play a considerable role in errors. Moreover, we find no evidence that a naïve pooling model that predicts errors based on a nonadditive combination of target and flankers explains errors better than an independent intrusion model (indeed, in our data, an independent intrusion model is slightly, but significantly, better). Finally, we find that manipulating trial difficulty in any way (spacing, eccentricity, or precueing) produces homogenous changes in error distributions. Together, these results provide quantitative baselines for predictive models of crowding errors, suggest that pooling and spatial substitution models are difficult to tease apart, and imply that manipulations of crowding all influence a common mechanism that impacts subject performance.