Flexible Resource Allocation for the Detection of Changing Visual Features

Failure to detect change under circumstances where visual input is interrupted or attention is distracted is indicative of the capacity limits of visual short-term memory. The current study attempts to probe the nature of these limits. In experiment 1, the appearance of single Gabor patches was altered across colour, size, or speed, and set size was manipulated by means of a visual cue. In experiment 2, performance for detecting single and multiple changes to Gabor patches was compared under the constraint that the inherent detectability of each individual change was the same. Experiment 1 yielded a particular set size (4) and a particular level of change magnitude at which performance was equivalent across change type. On the basis of these parameter values, experiment 2 revealed that the detectability of two features changing within one object was the same as the detectability of a single feature changing across two objects, and that this level of detectability could be predicted by a simple model of probability summation. Together, these results suggest that performance is determined by the magnitude of featural changes independently of the way they are distributed across objects. We suggest they are adequately explained by a flexible-resource-allocation model rather than a slot-allocation model.