Exploiting Development to Evaluate Auditory Encoding of Amplitude Modulation
Bibliographic record
Abstract
During development, detection for many percepts matures gradually. This provides a natural system in which to investigate the neural mechanisms underlying performance differences: those aspects of neural activity that mature in conjunction with behavioral performance are more likely to subserve detection. In principle, the limitations on performance could be attributable to either immature sensory encoding mechanisms or an immature decoding of an already-mature sensory representation. To evaluate these alternatives in awake gerbil auditory cortex, we measured neural detection of sinusoidally amplitude-modulated (sAM) stimuli, for which behavioral detection thresholds display a prolonged maturation. A comparison of single-unit responses in juveniles and adults revealed that encoding of static tones was adult like in juveniles, but responses to sAM depth were immature. Since perceptual performance may reflect the activity of an animal's most sensitive neurons, we analyzed the d prime curves of single neurons and found an equivalent percentage with highly sensitive thresholds in juvenile and adult animals. In contrast, perceptual performance may reflect the pooling of information from neurons with a range of sensitivities. We evaluated a pooling model that assumes convergence of a population of inputs at a downstream target neuron and found poorer sAM detection thresholds for juveniles. Thus, if sAM detection is based on the most sensitive neurons, then immature behavioral performance is best explained by an immature decoding mechanism. However, if sAM detection is based on a population response, then immature detection thresholds are more likely caused by an inadequate sensory representation.
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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 | 0.002 |
| 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".