Putting in the effort: Explicit effort may not influence perceptuomotor decision-making
Bibliographic record
Abstract
Humans often make decisions that are enacted by the action system. For example, humans use reach-to-grasp movements when making perceptuomotor decisions between and obtaining donuts of varying perceived quality. Recent work suggests that the characteristics of each action alternative may influence the associated decision-making process – biases away from perceptuomotor alternatives associated with high effort have been reported when participants are unaware of the effort differences between responses. The present study examined if perceptuomotor decisions were influenced by explicit, as opposed to implicit, reaching effort differences. Random dot motion stimuli were presented in which most dots moved in random directions and varying percentages of remaining dots moved coherently left- or rightward. Participants reported leftward motion judgements by performing leftward (or left hand) reaching movements and rightward motion judgements by performing rightward (or right hand) reaching movements on a tabletop. A resistance band was affixed to participants’ wrists and to the edge of the tabletop in different configurations. The configurations ensured that one movement/ motion direction judgement always required stretching of the band and, therefore, relatively more effort than the alternative movement. Across four experiments, the response context (i.e., selecting directions within a limb or selecting between limbs) and the effort difference between responses was manipulated. Overall, no experiment revealed a consistent bias away from the perceptuomotor decision associated with high effort. It is concluded that explicit effort as induced by a resistance band may not influence perceptuomotor decision-making and may point to a contextual influence of action effort on perceptuomotor decision-making.
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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 | 0.002 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".