Task-Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation
Bibliographic record
Abstract
Perception and motor control traditionally are studied separately. However, motor activity can serve as a scaffold to shape the sensory flow. This tight link between motor actions and sensing is particularly evident in active sensory systems. Here, we investigate how the weakly electric mormyrid fishGnathonemus petersiiof undetermined sex structure their sensing and motor behavior while learning a perceptual task. We find systematic adjustments of the motor behavior that correlate with an increased performance. Using a model to compute the electrosensory input, we show that these behavioral adjustments improve the sensory input. As we find low neuronal detection thresholds at the level of medullary electrosensory neurons, it seems that the behavior-driven improvements of the sensory input are highly suitable to overcome the sensory limitations, thereby increasing the sensory range. Our results show that motor control is an active component of sensory learning, demonstrating that a detailed understanding of contribution of motor actions to sensing is needed to understand even seemingly simple behaviors. SIGNIFICANCE STATEMENTMotor-guided sensation and perception are intertwined, with motor behavior serving as a scaffold to shape the sensory input. We characterized how the weakly electric mormyrid fishGnathonemus petersii, as it learns a perceptual task, restructures its sensorimotor behavior. We find that systematic adjustments of the motor behavior correlate with increased performance and a shift of the sensory attention of the animal. Analyzing the afferent electrosensory input shows that a significant gain in information results from these sensorimotor adjustments. Our results show that motor control can be an active component of sensory learning. Researching the sensory corollaries of motor control thus can be crucial to understand sensory sensation and perception under naturalistic conditions.
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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) | 0.000 | 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) | 0.001 | 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".