Electrophysiological evidence for biased competition in V1 favoring motivationally significant stimuli
Bibliographic record
Abstract
Conscious experience is selective: we are not aware of everything we see. So, how do some percepts reach conscious awareness while others do not? According to one influential model, stimuli will either be detected or go unnoticed based on the result of a competition for neural representation between multiple concurrent stimulus inputs. The winner of this competition will be selected for more detailed analyses. The goal of this study was to use event-related potentials (ERPs) to investigate whether stimuli of motivational significance “win” this competition for neural representation in visual cortex. Specifically, we took advantage of the C1 component which, due to the architecture of the calcarine fissure in V1, evokes either a negative or positive potential when stimuli are displayed in either the upper or lower hemifields respectively. When stimuli in the upper hemifield receive greater neural representation as compared to those concurrently displayed the lower hemifield, activity will summate to produce a distinct negative C1 component. In a first experiment, we contrasted a pair of task irrelevant fearful faces and their Fourier transformed derivatives, displayed in opposite hemifields, while participants engaged in a central task. Results showed that when fearful faces were displayed in the upper hemifield (evoking a negative potential) and Fourier transformed faces were displayed in the lower hemifield (evoking a positive potential), activity summated to produce a negative C1 component. Importantly, when Fourier transformed faces were presented in the upper hemifield, the C1 component was eliminated. This pattern was replicated when contrasting fearful and neutral faces, and also with fearful faces and their inverted counterparts. These findings demonstrate that (a) displays of threat competing for awareness are prioritized over other concurrent stimuli, and (b) this biased competition is resolved within 70 ms of visual processing; likely before any feed-back from higher level visual cortices occurs.
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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 | 0.001 |
| 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".