Embodied decision making in athletes and other animals
Bibliographic record
Abstract
Humans and other animals continuously make embodied decisions about ongoing or pending courses of action. Examples of embodied decisions include a hunting lioness’s decision of which gazelle to chase and a soccer player’s decision of which teammate to pass the ball to. The study of embodied decisions has recently gained tractions across several fields, including cognitive psychology, neuroscience, and sports science. Here, we summarize key insights from these studies and highlight that they imply a shift of perspective from viewing decision-making as a central cognitive process largely separated from perception and action dynamics to a more integrative perspective that recognizes its embodied and situated nature. We discuss how embodied decisions can be effectively conceptualized in terms of the parallel specification and selection between available (and future) affordances, i.e., as an “affordance competition” process. We discuss studies addressing various aspects of embodied decisions, which include the selection between courses of action, the involvement of motor processes in perceptual and cognitive tasks, motivational factors and the decision of how vigorously and urgently to act. Furthermore, we highlight current controversies in the field and open directions for future work – and their implications for the advancement of our understanding of the mind and the behavior of athletes.
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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 | 0.001 | 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 | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".