Auditory Cortical Responses to the Interactive Effects of Interaural Intensity Disparities and Frequency
Bibliographic record
Abstract
Under natural conditions, stimuli reaching the two ears contain multiple acoustic components. Rarely does a stimulus containing only one component (e.g. pure tone burst) exist outside the realm of the laboratory. For example, in sound localization the simultaneous presence of multiple cues (spectral content, level, phase, etc.) serves to increase the number of available cues and provide the listener with more information, thereby helping to reduce errors in locating the sound source. The present study was designed to explore the relationship between two acoustic parameters: stimulus frequency and interaural intensity disparities (IIDs). By varying both stimulus frequency and IIDs for each cell, we hoped to gain insight into how multiple cues are processed. To this end, we examined the responses of neurons in cat primary auditory cortex (AI) to determine if their sensitivity to IIDs changed as a function of stimulus frequency. IIDs ranging from +30 to -30 dB were presented at different frequencies (frequency was always the same in the two ears). We found that approximately half of the units examined exhibited responses to IIDs that varied as a function of stimulus frequency (i.e. displayed some form of IID x Freq dependency). The remaining units displayed IID responses that were not clearly related to stimulus frequency.
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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".