Modeling Continuous Outcome Color Decisions With the Circular Diffusion Model: Metric and Categorical Properties

• Published in: Psychological review Vol. 127; no. 4; pp. 562 - 590
• Main Authors: Smith, Philip L., Saber, Saam, Corbett, Elaine A., Lilburn, Simon D.
• Format: Journal Article
• Language: English
• Published: United States American Psychological Association 01-07-2020
• Subjects: Adult; Bias; Chromaticity; Cognitive load; Cognitive Processing Speed; Color; Color Perception; Decision Making; Decision Making - physiology; Diffusion; Encoding; Eye Movement Measurements; Female; Human; Humans; Male; Memory; Memory, Short-Term - physiology; Models, Psychological; Noise; Nonlinear systems; Reaction Time; Short Term Memory; Simulation; Stimulus; Transformation; Visual Memory; Young Adult; continuous outcome; visual working memory; diffusion model; response time; decision making
• ISSN: 0033-295X; 1939-1471
• DOI: 10.1037/rev0000185

The circular diffusion model is extended to provide a theory of the speed and accuracy of continuous outcome color decisions and used to characterize eye-movement decisions about the hues of noisy color patches in an isoluminant, equidiscriminability color space. Heavy-tailed distributions of decision outcomes were found with high levels of chromatic noise, similar to those found in visual working memory studies with high memory loads. Decision times were longer for less accurate decisions, in agreement with the slow error property typically found in difficult 2-choice tasks. Decision times were shorter, and responses were more accurate in parts of the space corresponding to nameable color categories, although the number and locations of the categories varied among participants. We show that these findings can be predicted by a theory of across-trial variability in the quality of the evidence entering the decision process, represented mathematically by the drift rate of the diffusion process. The heavy-tailed distributions of decision outcomes and the slow-error pattern can be predicted by either of 2 models of drift rate. One model is based on encoding failures and the other is based on a nonlinear transformation of the stimulus space. Both models predict highly inaccurate stimulus representations on some trials, leading to heavy-tailed distributions and slow errors. The color-category effects were successfully modeled as stimulus biases in a similarity-choice framework, in which the drift rate is the vector sum of the encoded metric and categorical representations of the stimulus.