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

Authors
Peter Kvam
University of Florida ~ Department of Psychology
Abstract

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.

Tags

Keywords

continuous model
multi-alternative choice
evidence accumulation
similarity

Topics

Cognitive Modeling
Decision Making
Reaction Times
Model Analysis and Comparison
Accumulator/Diffusion models
Discussion
New

Thank you for your very interesting talk! the results that you presented support post-accumulation sorting models. But since there are many of these models, what kind of evidence (or experimental paradigm) could distinguish them empirically?

Dimitris Katsimpokis 1 comment

I really enjoyed your talk. If I understand correctly, you conclude that the pre-accumulation sorting model is ruled out because the RTs speed up for one of the switching options (the easiest). When you move the orange or blue response alternatives you are providing a new response option so the subject must start over in accumulating evidence for t...

Lori Mahoney 3 comments
RT effects in the circular DDM Last updated 3 months ago

Hi Peter Excellent talk and work! I have a question about the RT effects that you get when making the trial easier/harder by shifting the response options. Is the circular DDM able to predict these RT effects? As far as I understand it, in the cDDM you would just rotate the circle in correspondence to the shift (e.g., let's say you rotate both the...

Sebastian Gluth 2 comments
decoy effects Last updated 3 months ago

you mentioned decoy effects found in preferential choice research at the beginning. Can your model account for these effects?

Jerome Busemeyer 1 comment