The medium modulates the medusa effect: Perceived mind in analogue and digital images
Bibliographic record
Abstract
We effortlessly attribute mental states to other people. We also attribute minds to people depicted in pictures, albeit at a reduced strength. Intriguingly, this reduction in intensity continues for images of people within a photograph itself-a phenomenon known as the Medusa effect. The present study replicates the Medusa effect for images shown digitally and on paper. Crucially, we demonstrate that we can reduce the magnitude of the Medusa effect by depicting people digitally within a computer screen (e.g., as if one were interacting with a person on a Zoom call). As well as modulating the quantity of the Medusa effect, changes in pictorial medium can affect the quality of the perceived mind. Specifically, the dimension of Experience-what a depicted person can feel-reflected participants' observations that they could interact with an onscreen person embedded in a digital image. This combination of a robust Medusa effect and the ability to control it both quantitatively and qualitatively opens many avenues for its future application, such as manipulating and measuring mind in immersive media.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".