I see what you’re saying – self-referential memory is influenced by direct eye gaze
Bibliographic record
Abstract
Eye contact is an integral factor of human communication and is used to express intentions, interests, and emotions (Emery, 2000). The direction of eye gaze is particularly relevant, as direct gaze indicates the intention to approach and interact, while averted gaze communicates social exclusion (Hietanen et al., 2008). Previous research has indicated that direct eye gaze heightens physiological arousal and becomes the focus of attention (Myllyneva, & Hietanen, 2015). Eye contact has also been shown to increase self-referential processing of information which advances recall ability (e.g., Hietanen, & Hietanen, 2017). The proposed study will conduct two experiments to examine the effect that direct eye contact has on attention and verbal memory by using a word recognition task, and recording arousal via galvanic skin response (GSR). Two participants will be asked to remember a list of words while the experimenter (live or video) either establishes direct or averted eye gaze, or no gaze at all. Participants will then be tested using a word recognition task. Experiment two will be the same but with the instructions to remember the words using self-referential or semantic encoding strategies. We hypothesize that direct eye gaze will improve memory more than averted gaze will, and significantly more so when using the self-referential encoding strategy. Faculty Mentor: Michelle Jarick Department: Psychology
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".