The influence of affordances on hand selection in reaching in right-handed children and adults
Bibliographic record
Abstract
Handedness is defined by the preferred hand to complete a variety of unimanual tasks, where a plethora of behavioural assessments are currently in use. Traditional measures quantify handedness in terms of preference (e.g., questionnaires) performance (e.g., pegboard tasks) and performance-based preference measures. Here a tool’s position in peripersonal space influences selection of the preferred hand and the presence of a tool, in contrast to an object (e.g., a peg) with ‘no purpose,’ is known to increase selection of the preferred hand. In this study we extended the paradigm involving tools by examining whether a tool’s affordance influences hand selection in peripersonal space. It was hypothesized that use of the preferred hand would increase with age. Sixty-nine right-handers (5- to 11-year-olds and adults) were presented with four tools in three tasks: (1) hammer a nail: hammer, rock, wrench and comb; (2) tighten a screw: screwdriver, knife, dime, and crayon; and (3) dig sand in a bucket: shovel, rake, wooden block, and tweezers. Participants were asked to ‘pick the best tool to complete the task’ until all four tools were selected, where task and tool order were randomized. Children were significantly more inclined to select an object based on location; using the right-hand in right space, and the left-hand in left-space for tool selection and then transferring the tool to their preferred hand to use. In comparison, adults were more inclined to use the same hand to pick-up and use the tool, where performance becomes more adult-like as a function of age.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".