Mirror, Mirror on the Screen, What Does All this ASCII Mean?: A Pilot Study of Spontaneous Facial Mirroring of Emotions
Bibliographic record
Abstract
Though an ever-increasing mode of communication, computer-mediated communication (CMC) faces challenges in its lack of paralinguistic cues, such as vocal tone and facial expression. Researchers suggest that emoticons fill the gap left by facial expression (Rezabek & Cochenour, 1998; Thompson & Foulger, 1996). The fMRI research of Yuasa, Saito, and Mukawa (2011b), in contrast, finds that viewing ASCII (American Standard Code for Information Interchange) emoticons (e.g., :), :( ) does not activate the same parts of the brain as does viewing facial expressions. In the current study, an online survey was conducted to investigate the effects of emoticons on perception of ambiguous sentences and users’ beliefs about the effects of and reasons for emoticon use. In the second stage of the study, eleven undergraduate students participated in an experiment to reveal facial mimicry responses to both faces and emoticons. Overall, the students produced more smiling than frowning gestures. Emoticons were found to elicit facial mimicry to a somewhat lesser degree than photographs of faces, while male and female participants differed in response to both ASCII emoticons and distractor images (photos of non-human, non-facial subjects used to prevent participants from immediately grasping the specific goal of the study). This pilot study suggests that emoticons, though not analogous to faces, affect viewers in ways similar to facial expression whilst also triggering other unique effects.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".