Bibliographic record
Abstract
The present review focuses on the hypothesis that norepinephrine (NE) and dopamine (DA) act as learning signals. Both NE and DA are broadly distributed in areas concerned with the representation of the world and with the conjunction of sensory inputs and motor outputs. Both are released at times of novelty and uncertainty, providing plausible signal events for updating representations and associations. These catecholamines activate intracellular machinery postulated to serve as a memory-formation cascade. Yet, despite the plausibility of an NE and DA role in vertebrate learning and memory, most evidence that they provide a learning signal is circumstantial. The major weakness of the data available is the lack of a specific description of how the neural circuit modulated by NE or DA participates in the learning being analyzed. Identifying a conditioned stimuli (CS) representation would facilitate the identification of a learning signal role for NE or DA. Describing how the CS representation comes to relate to learned behavior, either through sensory-sensory associations, in which the CS acquires the motivational significance of reward or punishment, thus driving appropriate behavior, or through direct sensory-motor associations is necessary to identify how NE and DA participate in memory creation. As described here, evidence consistent with a direct learning signal role for NE and DA is seen in the changing of sensory circuits in odor preference learning (NE), defensive conditioning (NE), and auditory cortex remodeling in adult rats (DA). Evidence that NE and DA contribute to normal learning through unspecified mechanisms is extensive, but the details of that support role are lacking.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".