A robust and representative lower bound on object processing speed in humans
Bibliographic record
Abstract
How early does the brain decode object categories? Addressing this question is critical to constrain the type of neuronal architecture supporting object categorization. In this context, much effort has been devoted to estimating face processing speed. With onsets estimated from 50 to 150 ms, the timing of the first face-sensitive responses in humans remains controversial. This controversy is due partially to the susceptibility of dynamic brain measurements to filtering distortions and analysis issues. Here, using distributions of single-trial event-related potentials (ERPs), causal filtering, statistical analyses at all electrodes and time points, and effective correction for multiple comparisons, we present evidence that the earliest categorical differences start around 90 ms following stimulus presentation. These results were obtained from a representative group of 120 participants, aged 18-81, who categorized images of faces and noise textures. The results were reliable across testing days, as determined by test-retest assessment in 74 of the participants. Furthermore, a control experiment showed similar ERP onsets for contrasts involving images of houses or white noise. Face onsets did not change with age, suggesting that face sensitivity occurs within 100 ms across the adult lifespan. Finally, the simplicity of the face-texture contrast, and the dominant midline distribution of the effects, suggest the face responses were evoked by relatively simple image properties and are not face specific. Our results provide a new lower benchmark for the earliest neuronal responses to complex objects in the human visual system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".