Motivation dynamically increases noise resistance by internal feedback during movement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motivation improves performance, pushing us beyond our normal limits. One general explanation for this is that the effects of neural noise can be reduced, at a cost. If this were possible, reward would promote investment in resisting noise. But how could the effects of noise be attenuated, and why should this be costly? Negative feedback may be employed to compensate for disturbances in a neural representation. Such feedback would increase the robustness of neural representations to internal signal fluctuations, producing a stable attractor. We propose that encoding this negative feedback in neural signals would incur additional costs proportional to the strength of the feedback signal. We use eye movements to test the hypothesis that motivation by reward improves precision by increasing the strength of internal negative feedback. We find that reward simultaneously increases the amplitude, velocity and endpoint precision of saccades, indicating true improvement in oculomotor performance. Analysis of trajectories demonstrates that variation in the eye position during the course of saccades is predictive of the variation of endpoints, but this relation is reduced by reward. This indicates that motivation permits more aggressive correction of errors during the saccade, so that they no longer affect the endpoint. We suggest that such increases in internal negative feedback allow attractor stability, albeit at a cost, and therefore may explain how motivation improves cognitive as well as motor precision.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it