Cueing color imagery: A critical analysis of imagery-perception congruency effects.
Bibliographic record
Abstract
The relation between mental imagery and visual perception is a long debated topic in experimental psychology. In a recent study, Wantz, Borst, Mast, and Lobmaier (2015) demonstrated that color imagery could benefit color perception in a task that involved generating imagery in response to a cue prior to a forced-choice color discrimination task. Here, we scrutinized whether the method of Wantz et al. warrants strong inferences about the role of color imagery in color perception. In Experiments 1-3, we demonstrate that the imagery effect reported by Wantz et al. does replicate nicely using their method but does not occur when cue-target contingencies and a redundancy between the imagery and response dimensions are removed from their method. In Experiments 4-6, we explored cued imagery effects further using a method in which the cued imagery dimension was orthogonal to the response dimension. The results of these experiments demonstrate that a compelling endogenously cued imagery effect does not occur for lone targets but does occur for singleton color targets embedded amid homogenous color distractors. (PsycINFO Database Record (c) 2019 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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".