Contrasting Predictions of Low- and High-Threshold Models for the Detection of Changing Visual Features

Abstract

Change blindness is the failure of observers to notice otherwise obvious changes to a visual scene when those changes are masked in some way (eg by blotches or a blanking of the screen). Typically, change blindness is taken as evidence that our representation of the visual world is capacity limited. The locus of this capacity limit is thought to be visual short-term memory (vSTM). The capacity of vSTM is usually estimated with a high-threshold model which assumes that each element in the stimulus array is either fully encoded or not encoded at all, and, furthermore, that false alarms can arise only by guessing, not by noise. Low-threshold models, by contrast, suggest that false alarms can arise by noise at the level of detection/discrimination and/or decision. In this study, we use a well-controlled stimulus display in which a single element changes over a blanking of the screen and contrast predictions from a popular high-threshold model of vSTM with the predictions of a low-threshold model (specifically, the sample-size model) of visual search and vSTM. The data were better predicted by the low-threshold model.