Examining within-category discrimination of faces and objects of expertise.
Bibliographic record
Abstract
Object experts quickly and accurately discriminate objects within their domain of expertise. The current study used a novel and implicit visual discrimination paradigm coupled with electroencephalography – Fast Periodic Visual Stimulation – to examine whether within-category discrimination of face and non-face objects of expertise rely on shared visual discrimination mechanisms. Bird experts and novices were presented with sequences of the same object image of a family-level bird (Robin), species-level bird (Purple Finch), or face (Face A) at a periodic rate of six images per second (6.00 Hz), with size varying randomly at every cycle to restrict adaptation to areas sensitive to object discrimination. A different within-category "oddball" family-level bird (Finch), species-level bird (Cassin's Finch) or face (Face B) was interleaved with the base image at every 5th cycle (1.20 Hz). Thus, a differential response at 1.20 Hz is an index of within-category discriminability between the base- and oddball-objects. We reasoned that discriminability of one object domain should be correlated at the participant level with the discriminability of another object domain if they share common visual discrimination mechanisms. The results showed a robust base signal (6.00 Hz, medial-occipital channels) and discrimination signal (1.20 Hz, occipito-temporal channels) that did not differ as a function of group by object domain. At the participant level, the base signal (6.00 Hz, medial-occipital channels) for all object categories positively correlated in both experts and novices. Importantly, the discrimination signal (1.20 Hz, occipito-temporal channels) for face and birds correlated in experts, but no pattern of correlations was found in novices. Moreover, family- and species-level birds correlated in both experts and novices. This indicates that the discrimination mechanisms for faces and birds were shared in the experts, but not in the novices. Overall, this suggests that face and non-face objects of expertise share visual discrimination mechanisms. Meeting abstract presented at VSS 2018
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".