Information sampling and processing during visual recognition
Bibliographic record
Abstract
Visual recognition is a phenomenon that seems to occur almost instantaneously. However, this is just an impression: not only does it require hundreds of milliseconds of processing, but information from the world must also be sampled during tens of milliseconds. This means that brain activity related to the recognition of an object is in fact composed of the brain responses to information sampled in different time windows. Furthermore, we can expect activity in response to different time windows to be different, partly because different features are attended and used at different moments during recognition, and because information perceived earlier must be maintained longer to be integrated with information perceived later. In this study, we aimed to decompose brain activity according to the sampling moment of information. To do so, we randomly sampled the main face features across 200ms on each trial while subjects performed a gender or expression recognition task and while their EEG activity was recorded. We then reverse correlated EEG amplitude in occipito-temporal sensors at all time points with information presented in different time windows: this allowed us to uncover the processing time course of information sampled at specific moments. We observed that processing was significantly different across presentation moments at several latencies and that the time windows leading to high activity correlated with the time windows leading to accurate responses. We also found that presentation moment modulated the durations of the P1 and P3 components. Importantly, these differences were not the same across tasks, indicating that their origin is partly top-down. In summary, we uncovered for the first time the processing of information sampled at different moments during recognition. We showed that sampling moment modulates the processing of information in more than one way, and that this modulation is partly related to top-down routines of information extraction Meeting abstract presented at VSS 2018
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".