ArticleCognitive science2021
Visual and Affective Multimodal Models of Word Meaning in Language and Mind.
Article in Cognitive science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Semantic properties and categorization norms for the 260 Snodgrass and Vanderwart objects: A 45-year conceptual update to a classic set.Behavior research methods · 2026Article
- Multimodal prior knowledge determines false memory formation.Journal of experimental psychology. General · 2026Article
- Moral Association Graph: A Cognitive Model for Automated Moral Inference.Topics in cognitive science · 2025Article
- Infinite Mixture Chaining: An Efficiency-Based Framework for the Dynamic Construction of Word Meaning.Open mind : discoveries in cognitive science · 2025Article
- Pupil Size Tracks the Effects of Global Context and Semantic Ambiguity on Word-Meaning Processing.Journal of cognition · 2025Article
- A large-scale database of Mandarin Chinese word associations from the Small World of Words Project.Behavior research methods · 2024Article
- Off-camera gaze decreases evaluation scores in a simulated online job interview.Scientific reports · 2024Article
- The "Small World of Words" free association norms for Rioplatense Spanish.Behavior research methods · 2024Article
- Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.Human brain mapping · 2024Article
- The Pandemic in Words: Tracking Fast Semantic Changes via a Large-Scale Word Association Task.Open mind : discoveries in cognitive science · 2023Article
- Independency of Coding for Affective Similarities and for Word Co-occurrences in Temporal Perisylvian Neocortex.Neurobiology of language (Cambridge, Mass.) · 2023Article
- Child-oriented word associations improve models of early word learning.Behavior research methods · 2023Article
- Using Network Science to Understand the Aging Lexicon: Linking Individuals' Experience, Semantic Networks, and Cognitive Performance.Topics in cognitive science · 2022Review
- Computational Models of Readers' Apperceptive Mass.Frontiers in artificial intelligence · 2022Article
- Visual and Affective Multimodal Models of Word Meaning in Language and Mind.Cognitive science · 2021Article
- Predicting Lexical Norms: A Comparison between a Word Association Model and Text-Based Word Co-occurrence Models.Journal of cognition · 2018Article
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4 authors.
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Abstract
One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic-level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.
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