Dynamic Perception of Well-Learned Perceptual Objects

Eight initially novel objects with four features were learned by three participants over about 70 sessions in a variety of present-absent search tasks. This article analyzes and models trials with a single object presented for test. The features of the object were presented simultaneously, or successively at rates fast enough that the objects appeared to be simultaneous (inter-stimulus intervals were 16, 33, or 50 ms). Classification of a test object as target or foil required a conjunction of two features. When successively presented, features diagnostic for target presence could arrive first or last, and vice versa for features diagnostic for foil presence. Two results were particularly important: (1) the order in which target-diagnostic or foil-diagnostic features appeared produced large changes in accuracy and response times; (2) simultaneous feature presentation produced lower accuracy than sequential presentation with target-diagnostic features arriving first, despite the delay in such features arriving. The results required a dynamic model for perception and decision. The model has features perceived at independent times. It accumulates evidence at each moment based on the features perceived up to that time, and the diagnosticity of those features for classifying the test object as target or foil. The model also assumes that configurations of features provide evidence as processing continues: when all four features of an object are perceived the evidence points without error to the correct response. The results and modeling support the view that perceptual and decision processes operate concurrently and interactively during identification, recognition, and classification of well-learned objects, rather than in successive stages.