Language as a window into mind perception: How mental state language differentiates body and mind, human and nonhuman, and the self from others.
Bibliographic record
Abstract
Mind perception-the attribution of mental states to humans and nonhuman entities-is an essential element of social cognition (distinct from related constructs such as perspective-taking and attribution). Despite its importance, research often captures this construct in hypothetical and atypical situations. We therefore used a novel text analysis tool-the Mind Perception Dictionary (MPD)-to measure linguistic use of mind perception (words related to "agency" and "experience") in naturalistic settings (externally valid contexts in the world unprompted by experimental demand) and test basic theoretical claims across 15 total studies (N = 7713). Initial validation studies show that the MPD reliably captures language referring to mental states when people focus more versus less on a stimulus's mental capacities. Studies 2A-5B illustrate that people use the concept of mind to distinguish friends from acquaintances (Studies 2A and 2B), human from nonhuman entities (technology in Studies 3A and 3B; nonhuman animals in Studies 4A and 4B), and the self from others (Studies 5A and 5B). Studies 6A-6C use the MPD to show meaningful differences in mind perception in naturalistic contexts (externally valid contexts in the world unprompted by experimental demand) and to reveal downstream consequences of mental state language. Together these studies show that mind perception is a generalizable psychological phenomenon that emerges in natural contexts and that systematically varies across stimuli perceived to be more or less human. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".