GABA Predicts Time Perception
Bibliographic record
Abstract
Our perception of time constrains our experience of the world and exerts a pivotal influence over a myriad array of cognitive and motor functions. There is emerging evidence that the perceived duration of subsecond intervals is driven by sensory-specific neural activity in human and nonhuman animals, but the mechanisms underlying individual differences in time perception remain elusive. We tested the hypothesis that elevated visual cortex GABA impairs the coding of particular visual stimuli, resulting in a dampening of visual processing and concomitant positive time-order error (relative underestimation) in the perceived duration of subsecond visual intervals. Participants completed psychophysical tasks measuring visual interval discrimination and temporal reproduction and we measured in vivo resting state GABA in visual cortex using magnetic resonance spectroscopy. Time-order error selectively correlated with GABA concentrations in visual cortex, with elevated GABA associated with a rightward horizontal shift in psychometric functions, reflecting a positive time-order error (relative underestimation). These results demonstrate anatomical, neurochemical, and task specificity and suggest that visual cortex GABA contributes to individual differences in time perception.
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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 | 0.003 |
| 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 | 0.001 | 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".