title: Qualitative contrast between knowledge-limited mixed-state and variable-resources models of visual change detection.
abstract: We report an experiment designed to provide a qualitative contrast between knowledge-limited versions of mixed-state and variable-resources (VR) models of visual change detection. The key data pattern is that observers often respond “same” on big-change trials, while simultaneously being able to discriminate between same and small-change trials. The mixed-state model provides a natural account of this data pattern: With some probability, the observer is in a zero-memory state and is forced to guess. Thus, even on big-change trials, there is a significant probability that the observer will respond “same.” On other trials, the observer retains memory for the probed study item, and these memory-based responses allow the observer to show above-chance discrimination between same and small-change trials. By contrast, we show that important versions of the VR models that we refer to asknowledge-limitedmodels are stymied by this simple pattern of results. In agreement with Keshvari, van den Berg, and Ma (2012, 2013), alternative knowledge-rich VR models that employ ideal-observer decision rules provide a significant improvement over the knowledge-limited VR models; however, extant versions of theknowledge-richVR models still fall short quantitatively compared to the descriptive mixed-state model. We discuss implications of the knowledge-rich assumptions that are posited in current versions of the VR models that have been used to fit change-detection data.
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University Bloomington.
authors.forename: Robert M
authors.initials: RM
authors.lastname: Nosofsky
authors.affiliation: School of Psychology, University of New South Wales.
authors.forename: Chris
authors.initials: C
authors.lastname: Donkin
mesh_terms: D006801:Humans; D008960:Models, Psychological; D010775:Photic Stimulation; D011336:Probability; D011601:Psychophysics; D017603:Signal Detection, Psychological; D014796:Visual Perception