Successive-signal biasing for a learned sound sequence
Bibliographic record
Abstract
Adult rats were trained to detect the occurrence of a two-element sound sequence in a background of nine other nontarget sound pairs. Training resulted in a modest, enduring, static expansion of the cortical areas of representation of both target stimulus sounds. More importantly, once the initial stimulus A in the target A-B sequence was presented, the cortical "map" changed dynamically, specifically to exaggerate further the representation of the "anticipated" stimulus B. If B occurred, it was represented over a larger cortical area by more strongly excited, more coordinated, and more selectively responding neurons. This biasing peaked at the expected time of B onset with respect to A onset. No dynamic biasing of responses was recorded for any sound presented in a nontarget pair. Responses to nontarget frequencies flanking the representation of B were reduced in area and in response strength only after the presentation of A at the expected time of B onset. This study shows that cortical areas are not representationally static but, to the contrary, can be biased moment by moment in time as a function of behavioral context.
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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) | 0.002 | 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".