Face and word superiority effects: Parallel effects of visual expertise
Bibliographic record
Abstract
There are several studies that compare perception for written words and faces. However, many draw conclusions from different experimental paradigms, complicating direct comparison between these stimuli. Such comparisons are of interest because of hypotheses based on neuroimaging and neuropsychological data that face and word processing may have common underlying mechanisms and neural substrates. To facilitate such comparisons, we created a novel paradigm studying face recognition that closely resembles the word-superiority test, in which a letter is more easily identified when it is embedded in a whole word than when seen in isolation or in an unpronounceable random string of letters. Forty subjects each completed both of our tests. In the traditional word-superiority test, they briefly saw a word, a pseudoword, or a nonword, then a single test letter, and were asked if the letter had been part of the initial stimulus. In the face-superiority test, they briefly saw a learned, new, or scrambled face initially, then a test facial feature in isolation, and were asked to respond whether the feature had been part of the initial stimulus. For both categories of stimuli, there were similar differences between real, pseudo-, and non-stimuli. Accuracy was lower for non-stimuli compared to pseudo- and real stimuli, which in turn did not differ from each other. Response latency was greater for non-stimuli compared to pseudo-stimuli, which in turn was greater than real stimuli. Bivariate analyses revealed significant correlations between interstimulus trials for reaction times. Our study replicated a face superiority effect utilizing a similar methodology to the word-superiority test. Additionally, response latencies follows similar patterns in the recognition of written words and faces.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".