Occipital GABA correlates with cognitive failures in daily life
Bibliographic record
Abstract
The brain has limited capacity, and so selective attention enhances relevant incoming information while suppressing irrelevant information. This process is not always successful, and the frequency of such cognitive failures varies to a large extent between individuals. Here we hypothesised that individual differences in cognitive failures might be reflected in inhibitory processing in the sensory cortex. To test this hypothesis, we measured GABA in human visual cortex using MR spectroscopy and found a negative correlation between occipital GABA (GABA+/Cr ratio) and cognitive failures as measured by an established cognitive failures questionnaire (CFQ). For a second site in parietal cortex, no correlation between CFQ score and GABA+/Cr ratio was found, thus establishing the regional specificity of the link between occipital GABA and cognitive failures. We further found that grey matter volume in the left superior parietal lobule (SPL) correlated with cognitive failures independently from the impact of occipital GABA and together, occipital GABA and SPL grey matter volume statistically explained around 50% of the individual variability in daily cognitive failures. We speculate that the amount of GABA in sensory areas may reflect the potential capacity to selectively suppress irrelevant information already at the sensory level, or alternatively that GABA influences the specificity of neural representations in visual cortex thus improving the effectiveness of successful attentional modulation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".