ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019
An Integrated Neural Decoder of Linguistic and Experiential Meaning.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Neural decoding of autobiographical mental image features with a general semantic model.Nature communications · 2025Article
- A Common Representational Code for Event and Object Concepts in the Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- What we mean when we say semantic: Toward a multidisciplinary semantic glossary.Psychonomic bulletin & review · 2025Review
- Neural dynamics of social verb processing: an MEG study.Social cognitive and affective neuroscience · 2025Article
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- Neural representation of phonological wordform in temporal cortex.Psychonomic bulletin & review · 2024Article
- Combined representation of visual features in the scene-selective cortex.Behavioural brain research · 2024Article
- The neural representation of body part concepts.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.Human brain mapping · 2024Article
- Family lexicon: Using language models to encode memories of personally familiar and famous people and places in the brain.PloS one · 2024Article
- Decomposing unaccusativity: a statistical modelling approach.Language, cognition and neuroscience · 2024Article
- Experientially-grounded and distributional semantic vectors uncover dissociable representations of conceptual categories.Language, cognition and neuroscience · 2024Article
- Driving and suppressing the human language network using large language models.bioRxiv : the preprint server for biology · 2023Article
- Bigram semantic distance as an index of continuous semantic flow in natural language: Theory, tools, and applications.Journal of experimental psychology. General · 2023Article
- A Distributed Network for Multimodal Experiential Representation of Concepts.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- How the Brain Dynamically Constructs Sentence-Level Meanings From Word-Level Features.Frontiers in artificial intelligence · 2022Article
- Analogy-Related Information Can Be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, Without Participants Performing any Analogy Task.Neurobiology of language (Cambridge, Mass.) · 2022Article
- Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Decoding individual identity from brain activity elicited in imagining common experiences.Nature communications · 2020Article
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8 authors.
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Abstract
The brain is thought to combine linguistic knowledge of words and nonlinguistic knowledge of their referents to encode sentence meaning. However, functional neuroimaging studies aiming at decoding language meaning from neural activity have mostly relied on distributional models of word semantics, which are based on patterns of word co-occurrence in text corpora. Here, we present initial evidence that modeling nonlinguistic "experiential" knowledge contributes to decoding neural representations of sentence meaning. We model attributes of peoples' sensory, motor, social, emotional, and cognitive experiences with words using behavioral ratings. We demonstrate that fMRI activation elicited in sentence reading is more accurately decoded when this experiential attribute model is integrated with a text-based model than when either model is applied in isolation (participants were 5 males and 9 females). Our decoding approach exploits a representation-similarity-based framework, which benefits from being parameter free, while performing at accuracy levels comparable with those from parameter fitting approaches, such as ridge regression. We find that the text-based model contributes particularly to the decoding of sentences containing linguistically oriented "abstract" words and reveal tentative evidence that the experiential model improves decoding of more concrete sentences. Finally, we introduce a cross-participant decoding method to estimate an upper bound on model-based decoding accuracy. We demonstrate that a substantial fraction of neural signal remains unexplained, and leverage this gap to pinpoint characteristics of weakly decoded sentences and hence identify model weaknesses to guide future model development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.