How do we recognize objects? An intracranial study
Bibliographic record
Abstract
Abstract Purpose How the brain recognizes visual stimuli has been extensively studied using scalp surface electrodes and magnetic resonance imaging. Complex visual stimuli such as objects and faces have been shown to strongly activate the ventral visual stream in humans. The link between this pathway of activation (bottom‐up processes) and the memory (top‐down processes) still has to be described. Methods In the present experiment, intracranial electroencephalography was carried out in epileptic patients implanted with subdural electrodes to localize the epileptic focus so as to directly assess brain activations evoked by visual stimuli. Fragmented pictures of objects with eight levels of coherence were used whereby at level 1 the stimulus was unrecognizable and at level 8, it constituted the complete picture. We measured the evoked potentials at four levels of recognition: threshold (T‐0), two levels before threshold (T‐1 and T‐2) and one post threshold (T+1). Results Activations were found in V1, V2, LOC, orbito‐frontal cortex (OF) and hippocampus. In V1 and V2, the early visual response suggested a higher level of treatment when trying to integrate the information to recognize the stimulus. The higher order structures, such as LOC and OF, gave their largest responses mainly at longer latencies. Conclusion This intracranial study permits precise localization coupled with high temporal resolution, which allows for a better understanding of the different mechanisms involved in a fast and accurate visual recognition of objects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".