Pigeons use distinct stop phases to control pecking
Bibliographic record
Abstract
Pecking at small targets requires accurate spatial coordination of the head. Planning of the peck has been proposed to occur in two distinct stop phases, but although this idea has now been around for a long time, the specific functional roles of these stop phases remain unsolved. Here, we investigated the characteristics of the two stop phases using high-speed motion capture and examined their functions with two experiments. In experiment 1, we tested the hypothesis that the second stop phase is used to pre-program the final approach to a target and analyzed head movements while pigeons (Columba livia) pecked at targets of different size. Our results show that the duration of both stop phases significantly increased as stimulus size decreased. We also found significant positive correlations between stimulus size and the distances of the beaks to the stimulus during both stop phases. In experiment 2, we used a two-alternative forced choice task with different levels of difficulty to test the hypothesis that the first stop phase is used to decide between targets. The results indicate that the characteristics of the stop phases do not change with an increasing difficulty between the two choices. Therefore, we conclude that the first stop phase is not exclusively used to decide upon a target to peck at, but also contributes to the function of the second stop phase, which is improving pecking accuracy and planning the final approach to the target.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".