Simulating Semantics: Are Individual Differences in Motor Imagery Related to Sensorimotor Effects in Language Processing?
Bibliographic record
Abstract
In embodied theories of semantic representation, the processes and mechanisms of modal simulations that are engaged during semantic processing have tended to be under-specified. We investigated the possibility that motor imagery may be a mechanism of simulation, using an individual differences approach. In this pre-registered study, we assessed motor imagery abilities (n = 161) with implicit and explicit measures and identified two latent factors. We then examined whether those factors account for significant variations in sensorimotor effects observed in three different language tasks: a lexical decision task, syntactic classification task, and sentence-picture verification task. In the language tasks, when all participants were considered together, we replicated some previously reported sensorimotor effects (e.g., body-object interaction, BOI, effects in semantic processing, wherein words associated with more sensorimotor information were processed more quickly than words associated with less sensorimotor information) and did not replicate others (e.g., BOI effects in the LDT, congruency effects in SPVT). There were no significant relationships between imagery factor scores and sensorimotor effects. A follow-up analysis using scores from each motor imagery measure revealed a significant interaction between hand movement imagery and BOI effects in the syntactic classification task, with those higher in this imagery ability showing a larger BOI effect. This latter result may suggest that specific types of motor imagery are related to sensorimotor effects in semantic processing, however further investigation is needed. In general, our findings provide little support for the possibility that motor imagery is an underlying mechanism of sensorimotor simulation during language processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".