AN ANALYSIS OF THE DYNAMICS OF PERCEPTION AND DECISION

Abstract

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 (ISIs 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 Hidden Markov Model model has features perceived at independent times. It accumulates evidence at each moment based on the particular features perceived up to that time, and the diagnosticities 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. How an object’s features are perceived over time, how they provide evidence, and how the evidence leads to a decision expressed with a binary response are explored using cursor movements. An object is presented on a computer monitor and a cursor controlled by a mouse is moved to one of two response regions to indicate the type of object presented. The object has two features, one 100% diagnostic of the desired response and the other 75% diagnostic. The features are presented simultaneously or sequentially. The cursor movements are analyzed with a Hidden Markov Model, a method that provides an exceptionally detailed inference about the moment-to-moment processes that govern perception and evidence change that produce a final decision: For each trial in each condition for each participant, the method provides a best guess, at each ten milliseconds from trial’s start to trial’s end, of the features that have been perceived by that time, and the evidence that has been accumulated by that time. These inferences can be analyzed in myriad ways to provide insights about the dynamic processes of cognition, and differences among conditions and individuals. A selection of such analyses is provided to illustrate the power of the approach.