Temporal context effects on suboptimal choice
Bibliographic record
Abstract
Choice can be driven both by rewards and stimuli that signal those rewards. Under certain conditions, pigeons will prefer options that lead to less probable reward when the reward is signaled. A recently quantified model, the Signal for Good News (SiGN) model, assumes that in the context of uncertainty, signals for a reduced delay to reward reinforce choice. The SiGN model provides an excellent fit to previous results from pigeons and the current studies are the first to test a priori quantitative predictions. Pigeons chose between a suboptimal alternative that led to signaled 20% food and an optimal alternative that led to 50% food. The duration of the choice period was manipulated across conditions in two experiments. Pigeons strongly preferred the suboptimal alternative at the shorter durations and strongly preferred the optimal alternative at the longer durations. The results from both experiments fit well with predictions from the SiGN model and show that altering the duration of the choice period has a dramatic effect in that it changes which of the two options pigeons prefer. More generally, these results suggest that the relative value of options is not fixed, but instead depends on the temporal 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".