Seeing eye to “egg”: Can attention and memory be impacted by “social” objects?
Bibliographic record
Abstract
An oft-used distinction when studying the effect of social content on cognitive processes is to create a division between people/faces as social, and objects as non-social; however, this practice presumes that objects lack social value. In contrast, evidence from other research domains suggest that objects can communicate social information, such as CDs or cars which can communicate information about the owner’s identity. Thus, here we explored whether attentional and spatial memory biases exist for objects with a social versus non-social value. 84 participants (either primed to think about objects as social or not) engaged in a visual search task and a memory task that included objects belonging to one of three categories: (i) identity objects—such as a menorah—which are affiliated with an individual’s identity, (ii) situation objects—such as a board game—which are used in social situations, and (iii) neutral objects—such as a toothbrush—which are not reflective of one’s identity nor used in social situations. Participants were (i) faster to locate identity and situation objects compared to neutral objects, (ii) were the most accurate at remembering the location of the situation objects, and (iii) the social prime was not necessary to show differences in attention and memory for the three object categories. These findings demonstrate that not all objects are attended to or remembered equally, and have important implications when considering the objects used for drawing conclusions about social versus non-social processes.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".