Can change detection succeed when change localization fails?
Bibliographic record
Abstract
Statistical summary representations (SSRs) are thought to be computed by the visual system to provide a rapid summary of the properties of sets of similar objects. Recently, it has been suggested that a change in the statistical properties of a set can be identified even when changes to the individual items comprising the set cannot. Haberman and Whitney (2011) showed that subjects were correctly able to report which of 2 consecutively presented sets of faces was, on average, happier, even when participants were unable to localize any of the items contributing to this change. In this article, we revisit this conclusion and suggest that the results supporting it may be an artifact of the paradigm used. In 4 experiments, we find little evidence to suggest that subjects can reliably detect a change in the average size or emotion of an array of faces when they are unable to localize changes to individual items. The results are well accounted for by assuming that observers are selectively attending to individual items and then inferring the direction of the overall change based on the behavior of the attended items. We suggest that this occurs because change localization requires focused attention to individual items, impeding calculation of SSRs, which requires global attention to the entire set. We conclude that there is currently little evidence that SSRs can facilitate change detection when individual change localization fails. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".