Decoding feature information in human auditory cortex—A comparison of auditory perception, short-term memory and imagery
Bibliographic record
Abstract
The flexible nature of auditory cortex, the complexity of real-world sounds, and limitations of the methods for neural measurement in humans have made it difficult to investigate auditory feature information processing in the human brain. Which precise features are encoded in auditory cortex and the roles they play in different cognitive tasks remained unclear. New methods for functional magnetic resonance (fMRI) provide solutions to these limitations. New acquisition sequences make less noise, improving the suitability of fMRI for auditory research. Real-time adaptive fMRI and multivariate pattern analysis methods are robust to individual differences in anatomy and exploit information in distributed neural networks. They allow assessment of which acoustic and abstracted features of simple and natural sounds are represented in human auditory regions during perception, and cognitive tasks such as change detection and imagery. The results indicate that auditory cortex is recruited for processes beyond analyzing simple feature information, playing an important role in maintaining sounds in short-term memory and encoding abstracted information during imagery. This research was supported by the Medical Research Council (UK) and The Brain and Mind Institute, University of Western Ontario.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".