Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields
Bibliographic record
Abstract
We tested the efficacy of color context cues during adaptation to dynamic force fields. Four groups of human subjects performed elbow flexion/extension movements to move a cursor between targets on a monitor while encountering a resistive (Vr) or assistive (Va) viscous force field. They performed two training sets of 256 trials daily, for 10 days. The monitor background color changed (red, green) every four successful trials but provided different degrees of force field context information to each group. For the irrelevant-cue groups, the color changed every four trials, but one group encountered only the Va field and the other only the Vr field. For the reliable-cue group, the force field alternated between Va and Vr each time the monitor changed color (Vr, red; Va, green). For the unreliable-cue group, the force field changed between Va and Vr pseudorandomly at each color change. All subjects made increasingly stereotyped movements over 10 training days. Reliable-cue subjects typically learned the association between color cues and fields and began to make predictive changes in motor output at each color change during the first day. Their performance continued to improve over the remaining days. Unreliable-cue subjects also improved their performance across training days but developed a strategy of probing the nature of the field at each color change by emitting a default motor response and then adjusting their motor output in subsequent trials. These findings show that subjects can extract explicit and implicit information from color context cues during force field adaptation.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".