Linguistic Markers of Theory of Mind in Spontaneous Speech: A Narrative Review
Bibliographic record
Abstract
The relationship between theory of mind (ToM) or mentalizing, i.e., the cognitive ability to attribute mental states to oneself and others, and language has been widely explored across disciplines. Identifying reliable linguistic markers of ToM extractable from individuals' speech provides a promising path for both research and clinical practice. In this narrative review, we aimed to synthesize findings from studies identified through a PSYCINFO search to provide an overview of speech-based markers associated with ToM abilities. Our results revealed six primary categories of relevant speech markers: mental state terms, general linguistic ability, embedded clauses, referring expressions, and pragmatic markers. Standardizing these markers could enhance the replicability and applicability of ToM assessments across diverse populations. We encourage future research to build on these findings to examine how mentalizing is expressed through language in varied social, cultural, and clinical contexts. Advancing this line of inquiry will deepen our understanding of the interplay between language and mentalizing and contribute to broader insights into language and cognition.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".