Motor representations evoked by objects under varying action intentions.
Bibliographic record
Abstract
In an extension of Gibson's (1979) concept of object affordance, it has been proposed that motor representations are automatically evoked by pictures of graspable objects. A variety of effects on left/right-handed keypress responses to the perceptual attributes of such images have been taken as evidence that features of actions, including the hand best suited to grasp an object, contribute to the effect of the handle's left/right location on response selection. We present an argument against this claim by establishing that all of these effects are based on spatial codes, including effects mistakenly interpreted to reflect the influence of limb-specific features of a grasp action. We also present 6 experiments showing that under certain task conditions, limb-specific effects on response selection are indeed automatically generated by the task-irrelevant image of a graspable object. These effects are found either when the observer makes keypress or reach-and-grasp responses to the laterality of a pictured hand superimposed on a depicted object. Both tasks recruit control processes that determine how the hand is selected and configured to grasp an object. We infer that processes implicated in the planning of a reach-and-grasp action themselves determine whether the task-irrelevant picture of an object triggers motor-based rather than spatial features. Our results have deep implications for the widely used concept of an affordance for action furnished by an object. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".