The neural correlates of illusory face perception: An fMRI study
Bibliographic record
Abstract
Individuals often report seeing a face in the clouds, their toast, or a tortilla. These informal observations suggest that our visual system is highly tuned to perceive faces, potentially due to the high social importance of faces or face processing expertise. Previous fMRI studies of this top-down bias to perceive faces have mainly examined the neural correlates of imagining faces or perception of ambiguous faces. However, the neural mechanisms underlying the illusory processing of faces are unclear. To address this question, in the present study, participants were instructed to detect faces (face task) and letter (letter task) in pure noise images after training in which increasingly noisy face or letter images were used. The pure noise images actually contained neither faces nor letters. Trials were classified into 4 conditions according to whether participants responded that they had "seen" a face or a letter in a pure noise image: face response, no-face response, letter response, and no-letter response. A repeated two-way ANOVA of task (face vs. letter) by detection (face or letter response vs. no response) was performed on the fMRI activities of each face-preferential area, namely the fusiform face area (FFA) and the occipital face area (OFA). Results revealed that the right FFA showed significantly greater activity for face responses than for no-face responses, whereas it showed equal responses to the letter and no-letter response. Within the left FFA and bilateral OFA, regardless of the face or letter task, the neural activity for detection responses was significantly greater than no-detection responses. Our findings suggest that the right FFA is specifically involved in the illusory processing of faces, whereas the left FFA and the bilateral OFA are involved in the illusory processing of visual objects more generally. Meeting abstract presented at VSS 2012
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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 | 0.001 |
| 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".