Action Influences Object Perception
Bibliographic record
Abstract
Proximal hand posture biases activity in the magnocellular (M) visual pathway (Gozli et al., 2012; Goodhew et al., in press). We tested whether this is due to having visual stimuli in the "action space" of the hands by making use of the finding that low spatial frequency images are rapidly identified through "gist" processing arising from the M-pathway (Kveraga, 2007). In Experiment 1, we paired low and high spatial frequencies images with either a proximal or distal hand posture and had participants indicate whether the objects were larger or smaller than a prototypical shoebox. Participants responded faster to low spatial frequency images paired with a proximal hand posture. No differences were found with distal hand posture. In Experiment 2, we manipulated proximal hand postures such that hands were either action oriented with palms in (palms toward the stimuli) or non-action oriented with palms out (palms away from the stimuli). In Experiment 3 we used one proximal hand posture (palms in) only but manipulated the type of visual stimuli such that they were either action oriented (easily grasped) or non-action oriented (not easily grasped). The results of Experiments 2 and 3 demonstrated that when action was primed (whether through hand posture or stimulus type) there was an advantage for low spatial frequency images. Overall, these experiments show that rapid "gist" object perception is due to M pathway activity, and that this processing is influenced by action-based hand positions. Meeting abstract presented at VSS 2013
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".