Does covert attention alter perceived contrast? Evidence from gender perception
Bibliographic record
Abstract
The current study evaluated the theory that attention boosts perceived contrast (Carrasco, Ling, & Read, 2004) by employing a novel measure of contrast: gender perception of ambiguous faces. Given that the apparent gender of a face has been shown to be related to contrast (Russell, 2009), we sought to use gender perception as a measure of whether perceived contrast indeed increases with attention. Participants performed a gender judgment task wherein the locus of attention was independently manipulated prior to stimulus presentation. On each trial, after 800 ms of fixation, an exogenous peripheral or neutral cue appeared for 50 ms, followed by a 50 ms presentation of the lower-region of two ambiguous faces. Participants reported which of the two faces appeared to be more female (Experiment 1) or more male (Experiment 2). Results showed that as the brightness contrast of a face increased, participants were less likely to report the face as female (Experiment 1) or more likely to report it as male (Experiment 2). While this contrasts with the demonstration by Russell (2009), a key difference is that we manipulated image contrast, whereas Russell specifically manipulated the contrast of the lips and eyes to the remaining face. Critically, the effect of attention did not consistently follow the effect of physical contrast on face perception, meaning that attention did not increase perceived contrast. Instead, attention increased the tendency to report a face as being more female (Experiment 1) or more male (Experiment 2). Our results support the hypothesis that attention does not boost perceived contrast, but instead causes an increase in the tendency to report stimuli in the attended region as being more salient (Schneider & Komlos, 2008).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".