Exploring the relationship between anthropomorphism and Theory-of-Mind in brain and behaviour
Bibliographic record
Abstract
The process of understanding the minds of other people, such as their emotions and intentions, is mimicked when individuals try to understand an artificial mind. The assumption is that anthropomorphism, attributing human-like characteristics to non-human agents and objects, is an analogue to Theory-of-Mind, the ability to infer mental states of other people. Here, we test to what extent these two constructs formally overlap. Specifically, using a multi-method approach, we test if and how anthropomorphism is related to Theory-of-Mind using brain (Experiment 1) and behavioural (Experiment 2) measures. In a first exploratory experiment, we examine the relationship between dispositional anthropomorphism and activity within the Theory-of-Mind brain network (n = 108). Results from a Bayesian regression analysis showed no consistent relationship between dispositional anthropomorphism and activity in regions of the Theory-of-Mind network. In a follow-up, pre-registered experiment, we explored the relationship between Theory-of-Mind and situational and dispositional anthropomorphism in more depth. Participants (n = 311) watched a short movie while simultaneously completing situational anthropomorphism and Theory-of-Mind ratings, as well as measures of dispositional anthropomorphism and general Theory-of-Mind. Only situational anthropomorphism predicted the ability to understand and predict the behaviour of the film’s characters. No relationship between situational or dispositional anthropomorphism and general Theory-of-Mind was observed. Together, these results suggest that while anthropomorphism and Theory-of-Mind might share some overlap in certain situations, they are separate and possibly unrelated at the personality level. These findings point to a possible dissociation between brain and behavioural measures when considering the relationship between Theory-of-Mind and anthropomorphism.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".