The effect of load magnitude on path choice in a decision-making task
Bibliographic record
Abstract
We are constantly faced with decisions about how to choose a path when navigating a complex movement environment. When deciding between movement paths that vary in reach distance and walking distance, previous research shows that the path which minimizes reach distance is more likely to be chosen, as reaching is ~11.2x more costly than walking (Rosenbaum et al., 2011; Rosenbaum, 2012; Cappelletto & Lyons, 2018a, 2018b). Our recent work investigates biomechanical factors (joint loading at the trunk and shoulder) during decision-making tasks and provides evidence that these functional movement costs can influence cognitive decision making when choosing between alternative movement strategies in both a two-choice and four-choice model (Cappelletto & Lyons, 2018a, 2018b). It is still unclear how functional costs are incorporated into the planning and execution of a decision-making task with decision factors in multiple domains (e.g. distance and weight). The purpose of this study is to explore how the perceived costs of multiple task variables are prioritized and integrated into action planning. Sixteen participants performed 80 trials of a bucket transfer task that varied as a function of load start location, load magnitude, and terminal target position. Our biomechanical data revealed that participants prioritized decreased reach distance over bearing an increased load, as reflected in decreased joint loading in chosen vs. unchosen paths, which suggests that bottom-up processes are influencing action planning.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".