A cost-effective Raspberry Pi-based operant playback setup to evaluate auditory preferences in songbirds
Bibliographic record
Abstract
Preferences arise from the interplay of biological processes, past experiences, and environmental factors that shape decision-making and adaptive behaviour. Song preference is integral to cognitive processes and song learning in songbirds. Traditionally, measuring song preferences in the lab has required expensive commercial equipment, limiting accessibility and adaptability. The past decade, however, has seen an increase in the development and use of low-cost DIY equipment that can be used for research. Here, we built a cost-effective operant audio playback system using a Raspberry Pi Zero 2 W. We validated the operant audio playback system using a conspecific-heterospecific song preference task with black-capped chickadees (Poecilia atricapillus). Our system allows the birds to self-select acoustic stimuli through perch-hopping while automatically recording their behaviour. Birds chose between conspecific black-capped chickadee fee-bee songs vs heterospecific white-throated sparrow songs. We did not find evidence that birds prefer conspecific (predicted probability 44 %, with 90 % CI [22 %-72 %]) over heterospecific songs from our limited sample size. Several factors may have contributed to this outcome, including the testing of wild-caught birds in a laboratory environment, the novelty or acoustic similarity of the stimuli, and potential habituation over time. Preference dynamics also varied across individuals, possibly reflecting differences in age or prior experiences. Nevertheless, birds readily engaged with the operant preference testing system, validating the setup and paving the way for a wide range of future research questions. The operant preference setup is made with affordable parts and open-source software, offering similar features to commercial systems but at a lower cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".