Time course of motor affordances evoked by pictured objects and words.
Bibliographic record
Abstract
Correspondence effects based on the relationship between the left/right position of a pictured object's handle and the hand used to make a response, or on the size of the object and the nature of a grip response (power/precision), have been attributed to motor affordances evoked by the object. Effects of this nature, however, are readily explained by the similarity in the abstract spatial coding of the features that define the stimulus and response, without recourse to object-based affordances. We propose that in the task context of making reach-and-grasp actions, pictured objects may evoke genuine, limb-specific action constituents. We demonstrate that when subjects make reach-and-grasp responses, there is a qualitative difference in the time course of correspondence effects induced by pictures of objects versus the names of those objects. For word primes, this time course was consistent with the abstract spatial coding account, in which effects should emerge slowly and become apparent only among longer response times. In contrast, correspondence effects attributable to object primes were apparent even among the shortest response times and were invariant across the entire response-time distribution. Using rotated versions of object primes provided evidence for a short-lived competition between canonical and depicted orientations of an object with respect to eliciting components of associated actions. These results suggest that under task conditions requiring reach-and-grasp responses, pictured objects rapidly trigger constituents of real-world actions. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".