Abstract Encoding of Auditory Objects in Cortical Activity Patterns
Bibliographic record
Abstract
The human brain is thought to process auditory objects along a hierarchical temporal "what" stream that progressively abstracts object information from the low-level structure (e.g., loudness) as processing proceeds along the middle-to-anterior direction. Empirical demonstrations of abstract object encoding, independent of low-level structure, have relied on speech stimuli, and non-speech studies of object-category encoding (e.g., human vocalizations) often lack a systematic assessment of low-level information (e.g., vocalizations are highly harmonic). It is currently unknown whether abstract encoding constitutes a general functional principle that operates for auditory objects other than speech. We combined multivariate analyses of functional imaging data with an accurate analysis of the low-level acoustical information to examine the abstract encoding of non-speech categories. We observed abstract encoding of the living and human-action sound categories in the fine-grained spatial distribution of activity in the middle-to-posterior temporal cortex (e.g., planum temporale). Abstract encoding of auditory objects appears to extend to non-speech biological sounds and to operate in regions other than the anterior temporal lobe. Neural processes for the abstract encoding of auditory objects might have facilitated the emergence of speech categories in our ancestors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".