Decoding real-world visual recognition abilities in the human brain
Bibliographic record
Abstract
The typical human visual system is able to decipher information about the visual world with impressive efficiency and speed. But not all individuals are equally competent at recognising what is presented to their eyes. Critically, very little is known about the brain mechanisms behind variations in recognition abilities. Here, we ask if interindividual variation in face cognition can be accurately “read” from brain activity, and use computational models to characterise the underlying brain mechanisms. We recorded high-density electroencephalography (EEG) in typical (n=17) and “super-recogniser” participants (n=16; individuals in the top 2% of face-recognition ability spectrum) while they were presented with images of faces, objects, animals, and scenes. Relying on more than 100,000 trials, we trained linear classifiers to predict whether trial-by-trial brain activity belonged to an individual from the “super” or “typical” recogniser group. Significant decoding of group-membership was observed from ~85ms, peaking within the N170 window, and spreading well after stimulus offset (>500ms). Using fractional ridge regression, we extended these findings by predicting individual ability scores from EEG in similar time-windows. Both results held true when decoding from face or non-face stimuli. To better understand the brain mechanisms behind these variations, we used representational similarity analysis and computational models that characterise visual (convolutional neural networks trained on object recognition; CNNs) and semantic processing (deep averaging network trained on sentence embeddings). This computational approach uncovered two representational signatures of higher face-recognition ability: mid-level visual computations (representations within the N170 window and mid-layers of CNNs) and high-level semantic computations (representations within the P600 window and the semantic model). Altogether, our results indicate that an individual’s ability to identify faces is supported by domain-general brain mechanisms distributed across several information processing steps, from low-level feature integration to high-level semantic processing, in the brain of the beholder.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| 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".