Spatial relationship between two sounds in an oddball paradigm affects responses of neurons in the auditory midbrain to the two sounds
Bibliographic record
Abstract
Neural sensitivity to a novel sound (i.e., an occasionally occurring acoustic event) is important for hearing in a natural acoustic environment. An oddball paradigm is used to study this sensitivity. Such a paradigm is a quick succession of acoustic stimuli, with each stimulus being one of two qualitatively different sounds presented at respective probabilities (i.e., high probability standard sound and low probability deviant sound). We studied action potential discharges in the rat’s auditory midbrain in response to oddball paradigms presented from free-field loudspeakers. Our results indicated that many neurons generated a stronger response to a sound presented as a deviant than as a standard stimulus when two sounds in an oddball paradigm were co-localized at the frontal midline. For many neurons, the response to a sound presented at frontal midline (either as a standard or a deviant sound) was enhanced or suppressed by relocating the other sound in an oddball paradigm from frontal midline to an off-midline angle. At extreme angles of separation, the number of neurons showing suppression exceeded that showing enhancement. Thus, a spatial separation between two sounds in an oddball paradigm affects responses to both sounds. Population results suggest that co-localization of two sounds helps maintaining neural sensitivity to both sounds.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".