The Encoding of Sound Source Elevation in the Human Auditory Cortex
Bibliographic record
Abstract
Spatial hearing is a crucial capacity of the auditory system. While the encoding of horizontal sound direction has been extensively studied, very little is known about the representation of vertical sound direction in the auditory cortex. Using high-resolution fMRI, we measured voxelwise sound elevation tuning curves in human auditory cortex and show that sound elevation is represented by broad tuning functions preferring lower elevations as well as secondary narrow tuning functions preferring individual elevation directions. We changed the ear shape of participants (male and female) with silicone molds for several days. This manipulation reduced or abolished the ability to discriminate sound elevation and flattened cortical tuning curves. Tuning curves recovered their original shape as participants adapted to the modified ears and regained elevation perception over time. These findings suggest that the elevation tuning observed in low-level auditory cortex did not arise from the physical features of the stimuli but is contingent on experience with spectral cues and covaries with the change in perception. One explanation for this observation may be that the tuning in low-level auditory cortex underlies the subjective perception of sound elevation. SIGNIFICANCE STATEMENTThis study addresses two fundamental questions about the brain representation of sensory stimuli: how the vertical spatial axis of auditory space is represented in the auditory cortex and whether low-level sensory cortex represents physical stimulus features or subjective perceptual attributes. Using high-resolution fMRI, we show that vertical sound direction is represented by broad tuning functions preferring lower elevations as well as secondary narrow tuning functions preferring individual elevation directions. In addition, we demonstrate that the shape of these tuning functions is contingent on experience with spectral cues and covaries with the change in perception, which may indicate that the tuning functions in low-level auditory cortex underlie the perceived elevation of a sound source.
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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 | 0.000 | 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) | 0.001 | 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".