Using Purposeful Tasks to Improve Motor Performance: Does Object Affordance Matter?
Bibliographic record
Abstract
Introduction: Purposeful activity is believed to yield better results than meaningless exercises during motor rehabilitation. The objective of this paper is to provide a narrative review of the literature regarding the influence of object affordance on motor performance, a factor that contributes to the purposefulness of a task. Method: Thirty-five reviewed articles were selected following an online search on PubMed, Cinahl and Google Scholar and an inspection of their references. Results of reviewed studies are discussed in relation to (1) the different approaches used to increase object affordance; (2) the influence of clinical conditions on the relationship between object affordance and motor performance; and (3) the influence of object affordance on immediate motor performance vs motor learning. Findings: The three main approaches used to increase object affordance relate to (a) the number of objects used during the task; (b) the functional information that these objects convey; and (c) the functional goals of the task. Reviewed results suggest that increasing object affordance can produce beneficial effects on immediate motor performance and motor learning, and especially support the effect of varying the number of objects. However, most evidence on object affordance has not come from high quality research. Conclusion: Clinicians should favour the use of tasks with high object affordance during both clinical assessment and therapy in order to promote optimal motor performance. More high quality research is needed to assess the influence of object affordance during therapy and on long-term motor learning and clinical outcomes.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".