Social modulation of object-directed but not image-directed actions
Bibliographic record
Abstract
There has recently been an increased research focus on the influence of social factors on human cognition, attention, and action. While this represents an important step towards an ecologically valid description of real-world behaviour, this work has primarily examined dyads interacting with virtual stimuli i.e. on-screen images of objects. Though differences between actions to images and real items are known, their relative sensitivity to social factors is largely unknown. We argue that because images and real items elicit different neural representations, patterns of attention, and hand actions, a direct comparison between the magnitude of social effects while interacting with images and real objects is demanded. We examined patterns of reaching as individuals performed a shape-matching game. Images and real objects were used as stimuli, and social context was manipulated via the proximity of an observer. We found that social context interacted with stimulus type to modulate behaviour. Specifically, there was a delay in reaching for distant objects when a participant was facing another individual but this social effect only occurred when the stimuli were real objects. Our data suggest that even when images and real objects are arranged to share the affordance of reachability, they differ in their sensitivity to social influences. Therefore, the measurement of social effects using on-screen stimuli may poorly predict the social effects of actions directed towards real objects. Accordingly, generalizations between these two domains should be treated with caution.
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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 | 0.003 |
| 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".