The impact of attention on impression formation in real-world interactions
Bibliographic record
Abstract
Social interactions are fundamental to human life, as they enable our understanding of others and grow relationships needed for our day-to-day functioning. One of the key processes that is vital to social interactions is attention, which helps us focus and prioritize specific facial or body information in order to better understand and read other individuals. For example, prior studies asking participants to watch a series of short video interviews found that greater attention to the faces, and eyes of the speaker enhances how accurately we form impressions of their personality (Capozzi, Human, & Ristic, 2019). However, one question that remains is whether this relationship holds during real life scenarios. Thus, this study explored whether attention to facial features facilitates impression formation in a real-world interactions. We recruited 16 participants (7 women, 6 men, 1 non-binary, age M=23.25years, range=18-35years) to complete a 20-minute conversation task with another participant, wherein each dyad was given a desert island scenario and asked to rank items that would maximize their chances of survival. Both participants wore eye tracking glasses, which monitored their attention and eye movements during the conversation task. Each participant completed personality assessments of themselves before the conversation task and of their partner after the conversation task. We measured attention to faces using an automated algorithm that tracked how often each participant’s eye movements landed on their partner’s face during the conversation task. We measured impression accuracy by calculating the difference in personality assessments between how someone rated themselves versus how their partner rated them. Preliminary results found no significant correlation between attention to faces and impression formation accuracy (r=-.03), suggesting that attention does not enhance how we socially perceive others. As such, this study hopes to inspire further research into the contribution of attention on how we form relationships with others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".