No Sex‐Differences in Learning Trap‐Gap Problems in Zebra Finches
Bibliographic record
Abstract
), males choose and deposit the majority of the material into the nest and might therefore exhibit enhanced physical cognition. We tested this hypothesis using the trap-gap task, a modified shape-frame matching paradigm designed to evaluate how animals assess object-hole relationships. In this task birds pulled food-containing trays attached to strings through gaps in barriers. Birds were trained on either a barrier task (choosing the correct gap size to fit a tray between barriers with different gaps) or a tray task (choosing the correct tray size between barriers with the same gap), then transferred to the alternate task (called the transfer test). Contrary to predictions, males and females showed no differences in the number of trials to reach learning criteria or in the number of errors in the transfer test. Birds required more trials, on average, to learn the barrier task compared to the tray task, and the transfer test was at chance, suggesting birds relied on absolute cue-based strategies rather than learning the object-gap relationship (relative cue-based strategies). These findings align with previous research showing no sex differences in learning about material properties in zebra finches, despite males' dominant role in nest building. The lack of sex differences in performance may stem from a mismatch in spatial frames of reference: while nest building relies on an egocentric (body-centered) frame, the trap-gap tasks uses an allocentric (object-centered) frame. Our findings highlight the complexity of linking behavioral sex roles to cognitive specialization and underscore the importance of task design and ecological relevance in comparative cognition research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".