Beyond the Uncanny Valley: A Theory of Eeriness for Android Science Research
Bibliographic record
Abstract
The "Uncanny Valley" (UV) theory predicts that highly realistic human-like artifacts, e.g. robots and animated characters, sometimes elicit eeriness in human subjects. But in spite of a rapidly growing body of interdisciplinary research on the UV, a lack of consensus concerning the cause(s) of this phenomenon persists. Therefore in what follows, I undertake a conceptual "overhaul" of the UV theory in order to facilitate an account of the UV phenomenon. Drawing from philosophical and empirical research, I demonstrate that: (1) eeriness is best understood as anxiety caused by uncertainty concerning the ontological nature of the artifact; and (2), that the misfiring of cognitive and affective empathic abilities -viz. an inconsistency between a subject's perception of the artifact's apparent animacy and mentation, and her knowledge that artifacts ought not possess such attributes -is the primary causal mechanism of the UV phenomenon. Finally, I revise the UV theory accordingly.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".