How Realistic Should Avatars Be?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it