Face identity processing at 33 ms and 100 ms with 4 ms of stimulus exposure
Bibliographic record
Abstract
Measuring the processing time from presentation to identification has proved difficult: Reaction times are slowed by decision-making and motor response processes, and although advances in neural representational analysis provide new insight on the speed of neural response, the relationship between these representations (e.g., decoding accuracy) and behaviour is not well understood. We used a psychophysical approach to measure the threshold for conscious access to face identity at 4 stages of processing: the minimum exposure duration, access to low-level information, access to high-level category information, and access to high-level identity information. In Experiment 1, 4ms of exposure was sufficient for identification and increasing exposure did not improve performance. In subsequent experiments, targets were presented for 4ms and the time available for processing the target was constrained by backward-masking and varying SOA between 8-213ms. In Experiment 2, a diffeomorphic transformation of the target image that preserves basic perceptual properties but obliterates high-level properties was used to effectively mask and limit access to the low-level properties of the target (Stojanoski & Cusack, 2014). With this mask, we found a threshold of only 33ms for above-chance identification. Targets were then masked by unfamiliar faces (Experiment 3) and familiar faces (Experiment 4) to mask the high-level face category and face familiarity properties of the target, respectively. The main findings were that unfamiliar faces were as effective as familiar faces in masking target identity; moreover, the psychometric function observed when masking with a diffeomorphic scramble indicates a narrow window of time needed to process low-level perceptual properties, whereas the functions observed when masking with another face show a gradual accumulation of evidence. The data indicate that as little as 33ms of uninterrupted processing is required to extract the low-level properties and ~100ms to extract the high-level properties needed for conscious access to a familiar face identity.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".