Biased Guessing in a Complete-Identification Visual-Working-Memory Task: Further Evidence for Mixed-State Models

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Abstract

Research is reported that provides evidence for a significant role of mixed states and guessing processes in tasks of visual working memory (VWM). Subjects engaged in a complete-identification VWM task. The stimulus set consisted of 16 colors roughly equally spaced around a color circle. On each trial, a memory-set drawn from the colors was briefly presented, followed by a location probe. Subjects attempted to reproduce the color of the probed item by clicking on the appropriate response button of a discrete color wheel. The key manipulation was to vary payoffs for alternative correct responses across trials. Analysis of the resulting matrices of individual-subject identification-confusion data provided evidence for a systematic guessing process: On trials in which subjects had no memory for the probed stimulus, they guessed with high probability using the high-payoff response. Formal modeling corroborated this interpretation. Mixed-state models that assumed that performance involved a combination of memory-based responding and biased guessing yielded accurate and easy-to-interpret accounts of the identification data; by comparison, variable-resources (VR) models without a guessing state struggled to account for the data, including versions with bias parameters for the high-payoff response. The authors argue that the work adds to recent converging sources of evidence that point to a significant role of discrete, mixed states in VWM. The authors also suggest directions for development of extended VR models with sophisticated knowledge-rich decision rules for the complete-identification task.

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