ArticleBehavior research methods2023
Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs.
Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Contextualized sensorimotor norms: Multi-dimensional measures of sensorimotor strength for ambiguous English words, in context.Behavior research methods · 2026Article
- Centroid analysis: Inferring concept representations from open-ended word responses.Behavior research methods · 2026Article
- Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection.Sensors (Basel, Switzerland) · 2025Article
- What we mean when we say semantic: Toward a multidisciplinary semantic glossary.Psychonomic bulletin & review · 2025Review
- Words and Worlds Both: Dynamic Effects of Distributional and Sensorimotor Information in Semantic Processing.Open mind : discoveries in cognitive science · 2025Article
- A Linguistic-Sensorimotor Model of the Basic-Level Advantage in Category Verification.Cognitive science · 2024Article
- Can large language models help augment English psycholinguistic datasets?Behavior research methods · 2024Article
- Visual Intuitions in the Absence of Visual Experience: The Role of Direct Experience in Concreteness and Imageability Judgements.Journal of cognition · 2024Article
- Bigram semantic distance as an index of continuous semantic flow in natural language: Theory, tools, and applications.Journal of experimental psychology. General · 2023Article
- Multi-dimensional sensorimotor grounding of concrete and abstract categories.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
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2 authors.
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Abstract
Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .
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