English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 as knowledge-limited models 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 the knowledge-rich VR 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.

Qualitative contrast between knowledge-limited mixed-state and variable-resources models of visual change detection.