Visuocortical changes during delay and trace aversive conditioning: Evidence from steady-state visual evoked potentials.
Bibliographic record
Abstract
The visual system is biased toward sensory cues that have been associated with danger or harm through temporal co-occurrence. An outstanding question about conditioning-induced changes in visuocortical processing is the extent to which they are driven primarily by top-down factors such as expectancy or by low-level factors such as the temporal proximity between conditioned stimuli and aversive outcomes. Here, the authors examined this question using 2 different differential aversive conditioning experiments: participants learned to associate a particular grating stimulus with an aversive noise that was presented either in close temporal proximity (delay conditioning experiment) or after a prolonged stimulus-free interval (trace conditioning experiment). In both experiments, the authors probed cue-related cortical responses by recording steady-state visual evoked potentials. Although behavioral ratings indicated that all participants successfully learned to discriminate between the grating patterns that predicted the presence versus absence of the aversive noise, selective amplification of population-level responses in visual cortex for the conditioned danger signal was observed only when the grating and the noise were temporally contiguous. Our findings are in line with notions purporting that changes in the electrocortical response of visual neurons induced by aversive conditioning are a product of Hebbian associations among sensory cell assemblies rather than being driven entirely by expectancy-based, declarative processes.
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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".