Peak frequency of induced gamma-band response to simple stimulus predicts individual switch rate for perceptual rivalry tasks.
Bibliographic record
Abstract
The peak frequency of an induced gamma-band response to a simple stimulus is known to vary across individuals (Muthukumaraswamy et al., 2010) and is thought to be shaped by differences in the extent of inhibitory connections in visual cortex (Brunel & Wang, 2003). In support of this, peak gamma frequency has been found to predict individual levels of resting state GABA in visual cortex (Edden et al., 2009; Muthukumaraswamy eta l., 2009). Also varying reliably across normal populations is the rate of alternation for perceptual rivalry tasks including binocular rivalry and monocular pattern rivalry (Carter & Pettigrew, 2003; Miller et al., 2010). Models of perceptual rivalry include mutual inhibition as a constraint on switching dynamics (e.g., Wilson 2003), but do the differences in switch rate reflect individual differences in cortical inhibition? If so, individual switch rate should be inversely correlated with peak gamma frequency, yet this prediction has not been tested. We used magnetoencephalography (MEG) to compare the peak gamma-band frequency of neuromagnetic responses of 12 healthy adults (6 female) to the onset of simple grating stimuli with their individual switch rates for binocular and monocular rivalry tasks. We computed Morlet wavelet analyses for left and right V1, V2, and MT+ of individual source data based on minimum norm estimates for each participant. Peak frequency was determined for three latency ranges: evoked (10-150ms), early induced (200-450ms) and late induced (500-800ms). Results show significant inverse correlations between peak frequency of early induced gamma in V1 and switch rate for both rivalry tasks, compatible with models that propose inhibitory connections in visual cortex are crucial for tuning gamma-band frequency as well as perceptual alternation rate. Our study suggests that subtle variations of behavior in a normal population can directly advance our understanding of visual function by encouraging models at increasingly finer scales. Meeting abstract presented at VSS 2014
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".