Choosing between action alternatives in an unconstrained task environment
Bibliographic record
Abstract
To navigate our surroundings, the human motor system must make decisions about which path or route will be chosen. For example, a decision may need to be made between a path that has a large reach distance and shorter walking distance, or a path with a shorter reach and longer walking distance. Previous research suggests that path choices are made to minimize reach distance, as reaching is ~11 times more costly than walking a given distance (Rosenbaum et al., 2011; Rosenbaum, 2012; Cappelletto & Lyons, 2018). Furthermore, previous work from our lab provides evidence that biomechanical factors, such as joint loading, can drive cognitive decision making when choosing postures for action, quantifying this notion of cost (Cappelletto & Lyons, 2018). To the best of our knowledge, previous work in this area presented participants with a two-choice model, allowing only the choice between the right or the left paths. Questions remain as to how biomechanical costs and constraints are incorporated into the planning and execution of decision-making tasks with increased degrees of freedom. Sixteen female participants performed 50 trials of a bucket transfer task that varied as a function of load start position (near, middle, far, right, left), and load end position (backward, forward, right, left). Behavioural outcomes suggest that the load's start position is incorporated into the decision-making process, reflected in the participants choice of end position. Our biomechanical data also provide evidence of bottom-up processes influencing action planning, as reflected in decreased cumulative loading measures at both the shoulder and trunk.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".