Biased guessing in a complete-identification visual-working-memory task: Further evidence for mixed-state models.
Author(s) -
Robert M. Nosofsky,
Jason M. Gold
Publication year - 2017
Publication title -
journal of experimental psychology human perception and performance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.691
H-Index - 148
eISSN - 1939-1277
pISSN - 0096-1523
DOI - 10.1037/xhp0000482
Subject(s) - working memory , psycinfo , psychology , cognitive psychology , set (abstract data type) , response bias , identification (biology) , task (project management) , stimulus (psychology) , computer science , stochastic game , short term memory , artificial intelligence , social psychology , cognition , mathematics , botany , management , medline , mathematical economics , neuroscience , political science , law , economics , biology , programming language
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. (PsycINFO Database Record
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