Constraints on continuous models applied to binary and multi-alternative choice

Models of continuous response tasks have been gainfully applied across a variety of perceptual and preferential choice paradigms. One benefit of these approaches is that they can serve as a general case of binary and multi-alternative choice models by dividing the continuum of evidence they produce into discrete regions corresponding to separate responses. Using these more general approaches elucidates “hidden” mechanisms of binary and multiple choice models that are often built into drift rates, thresholds, or parameter variability. In this talk, I present three empirical phenomena related to these hidden mechanisms, examining the constraints that they place on continuous models being applied to multi-alternative and binary choice. First, different choice options should be able to have different stopping rules (thresholds) based on their degree of similarity to other alternatives in the choice set. Second, continuous models must contain different mechanisms for different “drift rate” manipulations such as stimulus coherence, stimulus-response match, and the discriminability (confusability) of different response options. And third, continuous models must be able to store evidence for response options that are outside the initial choice set and map it onto new response options when they appear during a trial. Each of these constraints is imposed by an empirical phenomenon: participants in three experiments showed greater accuracy and faster response times for dissimilar response alternatives in a set; diverging effects of discriminability, coherence, and match manipulations; and efficient re-mapping of evidence when new choice options were introduced partway through an experimental trial.