Exploring expert object recognition by the means of fast periodic visual stimulation
Bibliographic record
Abstract
Expert category domains are thought to be instantiated within the human ventral visual pathway. For instance, differential responses to expert domains (e.g., faces, birds, cars, novel objects) have been shown in spatially localized areas using functional magnetic resonance imaging. In the current study we used fast periodic visual stimulation (FPVS) to explore the neural sensitivity to expert categories. Electroencephalogram was recorded from bird experts and bird novices, each presented with two trials of 60s sequences containing a base-bird (A, e.g., american robin) sinusoidally contrast-modulated at a presentation rate of 6 images per second (F=6Hz) with size varying every cycle to control for low-level adaptation effects. At every 5th cycle (F/5=1.2Hz), a different oddball-bird (e.g., northern cardinal, anna’s hummingbird…) (B, C…) substituted the repeating base-bird (i.e., AAAABAAAACAAAAD…). The results showed that despite changes in low-level information (i.e., image size), the experts showed an adaptation in the right occipito-temporal channels (PO8, P8) in response to the base-bird within the first 18s of the 60s sequence, whereas the novices showed a sustained signal-to-noise ratio (SNR) throughout the entire sequence. For the oddball-birds, both experts and novices showed a significant SNR at the fundamental 1.2 Hz frequency and its harmonics that remained sustained across the entire 60s sequence and that peaked at the right occipito-temporal channels (PO8, P8). In summary, the experts but not the novices showed an adaption to the base-birds, however, within the same sequence, both the experts and novices showed a sustained response to the oddball-birds. These results indicate that the response to the base-bird and oddball-birds are dissociated in experts, but not the novices. Meeting abstract presented at VSS 2015
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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 | 0.000 | 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".