Visual mismatch negativity (vMMN): automatic detection change followed by an inhibition of the attentional switch without visual awareness
Bibliographic record
Abstract
Attentional processing in the absence of conscious vision has yet to be understood in terms of neurophysiological mechanisms. Therefore, we used the visual mismatch negativity (vMMN) to determine if automatic detection of changes can be followed by an attentional switch without visual awareness. Random moving dots changing in direction were presented in the periphery, while participants carried out an effortful Stroop test in the central visual field to fully engage their attention on this primary task. The results revealed a posterior vMMN at 200 ms that was maximal in the parietal regions, revealing an automatic detection of change in the absence of visual awareness related to a dorsal/magnocellular pathway. Moreover, a frontal and central positivity, with a more pronounced activity in the left frontal areas was found at 300 ms possibly reflecting (1) unconscious attentional switch, (2) inhibition of explicit attentional switch by the left frontal areas acting on the right frontal areas via interhemispheric connections (3) inhibition of explicit attentional switch by the frontal areas acting on the central area via top-down connections. In conclusion, our results showed that vMMN could be a useful tool to study detection of changes and attentional mechanisms in the absence of visual consciousness.
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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 | 0.000 | 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".