“What” and “where” in the human auditory system
Bibliographic record
Abstract
The extent to which sound identification and sound localization depend on specialized auditory pathways was examined by using functional magnetic resonance imaging and event-related brain potentials. Participants performed an S1-S2 match-to-sample task in which S1 differed from S2 in its pitch and/or location. In the pitch task, participants indicated whether S2 was lower, identical, or higher in pitch than S1. In the location task, participants were asked to localize S2 relative to S1 (i.e., leftward, same, or rightward). Relative to location, pitch processing generated greater activation in auditory cortex and the inferior frontal gyrus. Conversely, identifying the location of S2 relative to S1 generated greater activation in posterior temporal cortex, parietal cortex, and the superior frontal sulcus. Differential task-related effects on event-related brain potentials (ERPs) were seen in anterior and posterior brain regions beginning at 300 ms poststimulus and lasting for several hundred milliseconds. The converging evidence from two independent measurements of dissociable brain activity during identification and localization of identical stimuli provides strong support for specialized auditory streams in the human brain. These findings are analogous to the "what" and "where" segregation of visual information processing, and suggest that a similar functional organization exists for processing information from the auditory modality.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".