Bibliographic record
Abstract
The ability to anticipate physiological needs and to predict the availability of desirable resources optimizes the likelihood of survival for an organism. The neural basis of the complex behaviors associated with anticipatory responses is now being delineated. Anticipation likely involves learning and memory, reward and punishment, memory and cognition, arousal and feedback associated with changes in internal and external state, homeostatic processes and timing mechanisms. While anticipation can occur on a variety of timescales (seconds to minutes to hours to days to a year), there have been great strides made towards understanding the neural basis timing of events in the circadian realm. Anticipation of daily events, such as scheduled access to food, may serve as a useful model for a more broadly based understanding the neurobiology of anticipation. In this review we examine the historical, conceptual and experimental approaches to understanding the neural basis of anticipation with a focus on anticipation of scheduled daily meals. We also introduce the key topics represented in the papers in this issue. These papers focused on food anticipation, to explore the state of the art in the studies of the neural basis of timing and anticipatory 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 | 0.001 |
| 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 | 0.001 |
| 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".