Action Costs Rapidly and Automatically Interfere with Reward-Based Decision-Making in a Reaching Task
Bibliographic record
Abstract
It is widely assumed that we select actions we value the most. While the influence of rewards on decision-making has been extensively studied, evidence regarding the influence of motor costs is scarce. Specifically, how and when motor costs are integrated in the decision process is unclear. Twenty-two right-handed human participants performed a reward-based target selection task by reaching with their right arm toward one of two visual targets. Targets were positioned in different directions according to biomechanical preference, such that one target was systematically associated with a lower motor cost than the other. Only one of the two targets was rewarded, either in a congruent or incongruent manner with respect to the associated motor cost. A timed-response paradigm was used to manipulate participants' reaction times (RT). Results showed that when the rewarded target carried the highest motor cost, movements produced at short RT (<350 ms) were deviated toward the other (i.e., non-rewarded, low-cost (LC) target). In this context participants needed an additional 150-ms delay to reach the same percentage of rewarded trials as when the LC target was rewarded. Crucially, motor costs affected the total earnings of participants. These results demonstrate a robust interference of motor costs in a simple reward-based decision-making task. They point to the rapid and automatic integration of motor costs at an early stage of processing, potentially through the direct modulation of competing action representations in parieto-frontal regions. The progressive overcoming of this bias with increasing RT is likely achieved through top-down signaling pertaining to expected rewards.
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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 | 0.002 |
| 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".