Behavioural tuning of face-selective neural populations
Bibliographic record
Abstract
Recent fMRI evidence demonstrates that face-selective mechanisms are tuned to face identity in a manner consistent with geometric face space. We evaluated the spatiotemporal properties of identity tuning for synthetic faces using a behavioural reverse correlation technique proposed by Ringach (1998). With this technique, different faces were rapidly (∼80ms) flashed on the screen and subjects were required to respond when a target face was presented as quickly as possible. Spatial and temporal tuning were assessed by correlating the probability that a particular stimulus was presented in the recent history of a subject's response. Spatiotemporal tuning plots were measured for face detection (detection of an intact face in the presence of scrambled faces) and for face identification (detection of one face identity in the presence of other identities). When asked to detect an intact face, subjects were most likely to respond on average 497.4 ms after an intact face was presented in the stimulus history. When asked to detect a specific identity, on the other hand, subjects were most likely to respond on average 587.9 ms after the target identity was presented. This temporal response difference is inconsistent with previous research demonstrating similar reaction times for face detection and identification. Moreover, subjects tended to be less likely to respond when a geometrically opposite ‘anti-face’ was presented at the same point in the stimulus history. These results suggest that inhibitory mechanisms may contribute to the tuning of face-selective mechanisms, similar in principle to opponent mechanisms that are observed in the orientation domain.
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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 | 0.001 | 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".