Bibliographic record
Abstract
The sense of taste is a key component of the sensory machinery, enabling the evaluation of both the safety as well as forming associations regarding the nutritional value of ingestible substances. Indicative of the salience of the modality, taste conditioning can be achieved in rodents upon a single pairing of a tastant with a chemical stimulus inducing malaise. This robust associative learning paradigm has been heavily linked with activity within the insular cortex (IC), among other regions, such as the amygdala and medial prefrontal cortex. A number of studies have demonstrated taste memory formation to be dependent on protein synthesis at the IC and to correlate with the induction of signaling cascades involved in synaptic plasticity. Taste learning has been shown to require the differential involvement of dopaminergic GABAergic, glutamatergic, muscarinic neurotransmission across an extended taste learning circuit. The subsequent activation of downstream protein kinases (ERK, CaMKII), transcription factors (CREB, Elk-1) and immediate early genes (c-fos, Arc), has been implicated in the regulation of the different phases of taste learning. This review discusses the relevant neurotransmission, molecular signaling pathways and genetic markers involved in novel and aversive taste learning, with a particular focus on the IC. Imaging and other studies in humans have implicated the IC in the pathophysiology of a number of cognitive disorders. We conclude that the IC participates in circuit-wide computations that modulate the interception and encoding of sensory information, as well as the formation of subjective internal representations that control the expression of motivated behaviors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".