How Realistic Should Avatars Be?
Bibliographic record
Abstract
Abstract. Increased interaction with characters in games and online necessitates a better understanding of how different characteristics of these agents impact media users. This paper investigates a possible neurological underpinning for a common research finding – namely, that animated characters designed to be comparatively more human, more real, and more similar to the people they represent elicit more positive self-reported evaluations. The goal of this study was to examine the extent to which these results might be due to differential processing of character features in brain networks recruited for face recognition. There is some evidence that parts of the face network may be specifically tuned for real human faces. An experiment was conducted where participants viewed photographs of faces of actual agents (humans and animals) or colored drawings of matched agents (cartoon humans and animals). Using functional magnetic resonance imaging (fMRI) to measure blood oxygen-level dependent (BOLD) activation in the whole brain and specifically in the face network, we investigated the variation in patterns of activation with human and animal faces that were more or less real. The results were consistent with previous reports that the core regions of the face network are sensitive to the humanness of faces. However, our results extended previous work by showing that regions of the core and extended regions of the face network – and some regions outside the network – were sensitive to realism, but only realism of human faces.
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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 | 0.009 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".