Weakly electric fish distinguish between envelope stimuli arising from different behavioral contexts
Bibliographic record
Abstract
to stimuli arising from different contexts, by measuring changes in the electric organ discharge (EOD) frequency. Specifically, we focused on envelopes, which can arise either because of movement (i.e. motion envelopes) or because of interactions between the electric fields of three of more fish (i.e. social envelopes). Overall, we found that the animal's EOD frequency effectively tracked the detailed time course of both motion and social envelopes. In general, behavioral sensitivity (i.e. gain) decreased while phase lag increased with increasing envelope and carrier frequency. However, changes in gain and phase lag as a function of changes in carrier frequency were more prominent for motion than for social envelopes in general. Importantly, we compared behavioral responses to motion and social envelopes with similar characteristics. Although behavioral sensitivities were similar, we observed an increased response lag for social envelopes, primarily for low carrier frequencies. Thus, our results imply that the organism can, based on behavioral responses, distinguish envelope stimuli resulting from movement from those that instead result from social interactions. We discuss the implications of our results for neural coding of envelopes and propose that behavioral responses to motion and social envelopes are mediated by different neural circuits in the brain.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".