Building semantic memory from embodied and distributional language experience

Overview

Charles P. Davis, Eiling Yee

Published Online: Feb 02 2021

DOI: 10.1002/wcs.1555

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

Abstract Humans seamlessly make sense of a rapidly changing environment, using a seemingly limitless knowledgebase to recognize and adapt to most situations we encounter. This knowledgebase is called semantic memory. Embodied cognition theories suggest that we represent this knowledge through simulation: understanding the meaning of coffee entails reinstantiating the neural states involved in touching, smelling, seeing, and drinking coffee. Distributional semantic theories suggest that we are sensitive to statistical regularities in natural language, and that a cognitive mechanism picks up on these regularities and transforms them into usable semantic representations reflecting the contextual usage of language. These appear to present contrasting views on semantic memory, but do they? Recent years have seen a push toward combining these approaches under a common framework. These hybrid approaches augment our understanding of semantic memory in important ways, but current versions remain unsatisfactory in part because they treat sensory‐perceptual and distributional‐linguistic data as interacting but distinct types of data that must be combined. We synthesize several approaches which, taken together, suggest that linguistic and embodied experience should instead be considered as inseparably entangled: just as sensory and perceptual systems are reactivated to understand meaning, so are experience‐based representations endemic to linguistic processing; further, sensory‐perceptual experience is susceptible to the same distributional principles as language experience. This conclusion produces a characterization of semantic memory that accounts for the interdependencies between linguistic and embodied data that arise across multiple timescales, giving rise to concept representations that reflect our shared and unique experiences. This article is categorized under: Psychology > Language Neuroscience > Cognition Linguistics > Language in Mind and Brain

A cartoonized brain depicting how distributed brain regions might contribute to conceptual knowledge under an embodied cognition framework. Pink areas are roughly meant to correspond to cortical regions, and gray areas are roughly meant to correspond to subcortical areasSource: Figure adapted from Allport (1985) and Thompson‐Schill et al. (2006). This figure is licensed under a CC‐BY 4.0 International License

[ Normal View | Magnified View ]

A simple recurrent network. Each layer consists of one or more units, and information (e.g., words, semantic features) flows first from input units, to hidden units, and then to output units. At every timepoint, the context units propagate to the hidden layer, giving the network access to its “memory” of prior states

[ Normal View | Magnified View ]

References

Allport,, D. A. (1985). Distributed memory, modular subsystems and dysphasia. In S. K. Newman, & R. Epstein, (Eds.), Current perspectives in dysphasia (pp. 207–244). Churchill Livingstone.
Althaus,, N., & Mareschal,, D. (2014). Labels direct infants` attention to commonalities during novel category learning. PLoS One, 9(7), e99670.
Andrews,, M., Frank,, S., & Vigliocco,, G. (2014). Reconciling embodied and distributional accounts of meaning in language. Topics in Cognitive Science, 6(3), 359–370.
Andrews,, M., Vigliocco,, G., & Vinson,, D. (2009). Integrating experiential and distributional data to learn semantic representations. Psychological Review, 116(3), 463–498.
Baroni,, M., Dinu,, G., & Kruszewski,, G. (2014, June). Don`t count, predict! a systematic comparison of context‐counting vs. context‐predicting semantic vectors. In Proceedings of the 52nd annual meeting of the association for computational linguistics (Vol. 1: Long papers; pp. 238–247).
Barsalou,, L. W. (1999). Perceptions of perceptual symbols. Behavioral and Brain Sciences, 22(4), 637–660.
Barsalou,, L. W. (2016). On staying grounded and avoiding quixotic dead ends. Psychonomic Bulletin %26 Review, 23(4), 1122–1142.
Barsalou,, L. W., Santos,, A., Simmons,, W. K., & Wilson,, C. D. (2008). Language and simulation in conceptual processing. In Symbols, embodiment, and meaning (pp. 245–283). Oxford University Press.
Binder,, J. R., Conant,, L. L., Humphries,, C. J., Fernandino,, L., Simons,, S. B., Aguilar,, M., & Desai,, R. H. (2016). Toward a brain‐based componential semantic representation. Cognitive Neuropsychology, 33, 130–174.
Boleda,, G. (2020). Distributional semantics and linguistic theory. Annual Review of Linguistics, 6, 213–234.
Borghi,, A. M., Barca,, L., Binkofski,, F., Castelfranchi,, C., Pezzulo,, G., & Tummolini,, L. (2019). Words as social tools: Language, sociality and inner grounding in abstract concepts. Physics of Life Reviews, 29, 120–153.
Borghi,, A. M., Binkofski,, F., Castelfranchi,, C., Cimatti,, F., Scorolli,, C., & Tummolini,, L. (2017). The challenge of abstract concepts. Psychological Bulletin, 143(3), 263–292.
Borghi,, A. M., & Zarcone,, E. (2016). Grounding abstractness: Abstract concepts and the activation of the mouth. Frontiers in Psychology, 7, 1498.
Boutonnet,, B., & Lupyan,, G. (2015). Words jump‐start vision: A label advantage in object recognition. Journal of Neuroscience, 35(25), 9329–9335.
Brown,, K. S., Yee,, E., Saltzman,, E., Magnuson,, J. S. & McRae,, K. (2020). What do computers know about semantics anyway? Testing distributional semantics models against a broad range of relatedness ratings. Poster presented at the 42nd annual conference of the cognitive science society. Virtual conference.
Bruni,, E., Tran,, N. K., & Baroni,, M. (2014). Multimodal distributional semantics. Journal of Artificial Intelligence Research, 49, 1–47.
Buccino,, G., Dalla Volta,, R., Arabia,, G., Morelli,, M., Chiriaco,, C., Lupo,, A., Silipo,, F., & Quattrone,, A. (2018). Processing graspable object images and their nouns is impaired in Parkinson`s disease patients. Cortex, 100, 32–39.
Casasanto,, D., & Lupyan,, G. (2015). All concepts are ad hoc concepts. In E. Margolis, & S. Laurence, (Eds.), The conceptual mind: New directions in the study of concepts (pp. 543–566). MIT Press.
Chao,, L. L., & Martin,, A. (1999). Cortical regions associated with perceiving, naming, and knowing about colors. Journal of Cognitive Neuroscience, 11(1), 25–35.
Clark,, A. (2006). Language, embodiment, and the cognitive niche. Trends in Cognitive Sciences, 10(8), 370–374.
Connell,, L. (2019). What have labels ever done for us? The linguistic shortcut in conceptual processing. Language, Cognition and Neuroscience, 34(10), 1308–1318.
Connell,, L., & Lynott,, D. (2013). Flexible and fast: Linguistic shortcut affects both shallow and deep conceptual processing. Psychonomic Bulletin and Review, 20(3), 542–550.
Connell,, L., & Lynott,, D. (2014). Principles of representation: Why you can`t represent the same concept twice. Topics in Cognitive Science, 6(3), 390–406.
Connell,, L., Lynott,, D., & Banks,, B. (2018). Interoception: The forgotten modality in perceptual grounding of abstract and concrete concepts. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1752), 20170143.
Damasio,, A. R. (1989). Time‐locked multiregional retroactivation: A systems‐level proposal for the neural substrates of recall and recognition. Cognition, 33, 25–62.
Davis,, C. P., Altmann,, G. T. M., & Yee,, E. (2020a). Situational systematicity: A role for schema in understanding the differences between abstract and concrete concepts. Cognitive Neuropsychology, 37, 142–153.
Davis,, C. P., Altmann,, G. T. M., & Yee,, E. (2020b). Language as a mental travel guide. Commentary on Above and beyond the concrete: The diverse representational substrates of the predictive brain. Behavioral and Brain Sciences.43, e121.
Davis,, C. P., Joergensen,, G. H., Boddy,, P., Dowling,, C., & Yee,, E. (2020). Making it harder to “see” meaning: The more you see something, the more its conceptual representation is susceptible to visual interference. Psychological Science, 31(5), 505–517.
Davis,, C. P., & Yee,, E. (2019). Features, labels, space, and time: Factors supporting taxonomic relationships in the anterior temporal lobe and thematic relationships in the angular gyrus. Language, Cognition and Neuroscience, 34(10), 1347–1357.
Dove,, G. (2018). Language as a disruptive technology: Abstract concepts, embodiment and the flexible mind. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1752), 20170135.
Dove,, G. (2020). More than a scaffold: Language is a neuroenhancement. Cognitive Neuropsychology, 37, 288–311.
Edmiston,, P., & Lupyan,, G. (2015). What makes words special? Words as unmotivated cues. Cognition, 143, 93–100.
Elman,, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Elman,, J. L. (2009). On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon. Cognitive Science, 33(4), 547–582.
Fernandino,, L., Conant,, L. L., Binder,, J. R., Blindauer,, K., Hiner,, B., Spangler,, K., & Desai,, R. H. (2013a). Where is the action? Action sentence processing in Parkinson`s disease. Neuropsychologia, 51(8), 1510–1517.
Fernandino,, L., Conant,, L. L., Binder,, J. R., Blindauer,, K., Hiner,, B., Spangler,, K., & Desai,, R. H. (2013b). Parkinson`s disease disrupts both automatic and controlled processing of action verbs. Brain and Language, 127(1), 65–74.
Firth,, J. R. (1957). A synopsis of linguistic theory 1930–1955. In Studies in linguistic analysis (pp. 1–32). Blackwell Publishers.
Gage,, N. Y., & Hickok,, G. (2005). Multiregional cell assemblies, temporal binding and the representation of conceptual knowledge in cortex: A modern theory by a “classical” neurologist, Carl Wernicke. Cortex, 41(6), 823–832.
Gibson,, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.
Glenberg,, A. M. (1997). What memory is for. Behavioral and Brain Sciences, 20, 1–55.
Glenberg,, A. M., & Robertson,, D. A. (2000). Symbol grounding and meaning: A comparison of high‐dimensional and embodied theories of meaning. Journal of Memory and Language, 43(3), 379–401.
Griffiths,, T. L., Steyvers,, M., & Tenenbaum,, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2), 211–244.
Günther,, F., Dudschig,, C., & Kaup,, B. (2018). Symbol grounding without direct experience: Do words inherit sensorimotor activation from purely linguistic context? Cognitive Science, 42, 336–374.
Günther,, F., Nguyen,, T., Chen,, L., Dudschig,, C., Kaup,, B., & Glenberg,, A. (2020). Immediate sensorimotor grounding of novel concepts learned from language alone. Journal of Memory and Language. 115, 104172.
Günther,, F., Rinaldi,, L., & Marelli,, M. (2019). Vector‐space models of semantic representation from a cognitive perspective: A discussion of common misconceptions. Perspectives on Psychological Science, 14, 1006–1033.
Harnad,, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346.
Harnad,, S. (2003). Categorical perception. In Encyclopedia of cognitive science (Vol. 67, No. 4). MacMillan: Nature Publishing Group.
Harnad,, S. (2017). To cognize is to categorize: Cognition is categorization. In C. Lefebvre, & H. Cohen, (Eds.), Handbook of categorization in cognitive science (pp. 21–54). Elsevier.
Harris,, Z. (1954). Distributional structure. Word, 10, 146–162.
Hauk,, O., Johnsrude,, I., & Pulvermüller,, F. (2004). Somatotopic representations of action words in human motor and premotor cortex. Neuron, 41, 301–307.
Hintzman,, D. L. (1986). “Schema abstraction” in a multiple‐trace memory model. Psychological Review, 93(4), 411–428.
Hoffman,, P., Lambon Ralph,, M. A., & Rogers,, T. T. (2013). Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words. Behavior Research Methods, 45(3), 718–730.
Hoffman,, P., McClelland,, J. L., & Lambon Ralph,, M. A. (2018). Concepts, control and context: A connectionist account of normal and disordered semantic cognition. Psychological Review, 125(3), 293–328.
Hume,, D. (1748/1977). An enquiry concerning human understanding. Hackett Publishing.
Johns,, B. T., & Jones,, M. N. (2012). Perceptual inference through global lexical similarity. Topics in Cognitive Science, 4(1), 103–120.
Johns,, B. T., & Jones,, M. N. (2015). Generating structure from experience: A retrieval‐based model of language processing. Canadian Journal of Experimental Psychology, 69(3), 233–251.
Kiefer,, M., Sim,, E. J., Herrnberger,, B., Grothe,, J., & Hoenig,, K. (2008). The sound of concepts: Four markers for a link between auditory and conceptual brain systems. Journal of Neuroscience, 28(47), 12224–12230.
Kousta,, S. T., Vigliocco,, G., Vinson,, D. P., Andrews,, M., & Del Campo,, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 140(1), 14–34.
Lambon Ralph,, M. A., Jefferies,, E., Patterson,, K., & Rogers,, T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience, 18(1), 42.
Landauer,, T. K., & Dumais,, S. T. (1997). A solution to Plato`s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 1(2), 1–54.
Lenci,, A. (2018). Distributional models of word meaning. Annual Review of Linguistics, 4(1), 1065975714.
Liberman,, A. M., Cooper,, F. S., Shankweiler,, D. P., & Studdert‐Kennedy,, M. (1967). Perception of the speech code. Psychological Review, 74, 431–461.
Locke,, J. (1689/1975). An essay concerning human understanding. Clarendon Press.
Louwerse,, M., & Connell,, L. (2011). A taste of words: Linguistic context and perceptual simulation predict the modality of words. Cognitive Science, 35(2), 381–398.
Louwerse,, M. M. (2007). Symbolic or embodied representations: A case for symbol interdependency. In Handbook of latent semantic analysis (pp. 107–120). Routledge.
Louwerse,, M. M. (2008). Embodied relations are encoded in language. Psychonomic Bulletin and Review, 15(4), 838–844.
Louwerse,, M. M. (2018). Knowing the meaning of a word by the linguistic and perceptual company it keeps. Topics in Cognitive Science, 10, 573–589.
Louwerse,, M. M., Hutchinson,, S., Hutchinson,, R., & Recchia,, G. (2015). Effect size matters: The role of language statistics and perceptual simulation in conceptual processing. Language, Cognition and Neuroscience, 30, 430–447.
Louwerse,, M. M., & Jeuniaux,, P. (2010). The linguistic and embodied nature of conceptual processing. Cognition, 114(1), 96–104.
Lund,, K., & Burgess,, C. (1996). Producing high‐dimensional semantic spaces from lexical co‐occurrence. Behavior Research Methods, Instruments, and Computers, 28(2), 203–208.
Lupyan,, G. (2009). Extracommunicative functions of language: Verbal interference causes selective categorization impairments. Psychonomic Bulletin %26 Review, 16, 711–718.
Lupyan,, G. (2012). Linguistically modulated perception and cognition: The label‐feedback hypothesis. Frontiers in Psychology, 3, 54.
Lupyan,, G. (2017). The paradox of the universal triangle: Concepts, language, and prototypes. Quarterly Journal of Experimental Psychology, 70(3), 389–412.
Lupyan,, G., Abdel Rahman,, R., Boroditsky,, L., & Clark,, A. (2020). Effects of language on visual perception. Trends in Cognitive Sciences, 24, 930–944.
Lupyan,, G., & Spivey,, M. J. (2010). Redundant spoken labels facilitate perception of multiple items. Attention, Perception, %26 Psychophysics, 72(8), 2236–2253.
Lupyan,, G., & Ward,, E. J. (2013). Language can boost otherwise unseen objects into visual awareness. Proceedings of the National Academy of Sciences, 110(35), 14196–14201.
Lynott,, D., & Connell,, L. (2009). Modality exclusivity norms for 423 object properties. Behavior Research Methods, 41(2), 558–564.
Lynott,, D., Connell,, L., Brysbaert,, M., Brand,, J., & Carney,, J. (2020). The Lancaster sensorimotor norms: Multidimensional measures of perceptual and action strength for 40,000 English words. Behavior Research Methods, 52, 1271–1291.
Mahon,, B. Z. (2015). What is embodied about cognition? Language, Cognition and Neuroscience, 30(4), 420–429.
Mahon,, B. Z., & Caramazza,, A. (2008). A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. Journal of Physiology‐Paris, 102(1), 59–70.
Mandera,, P., Keuleers,, E., & Brysbaert,, M. (2017). Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation. Journal of Memory and Language, 92, 57–78.
Markman,, E. M. (1990). Constraints children place on word meanings. Cognitive Science, 14(1), 57–77.
Markman,, E. M., & Hutchinson,, J. E. (1984). Children`s sensitivity to constraints on word meaning: Taxonomic versus thematic relations. Cognitive Psychology, 16(1), 1–27.
Martin,, A. (2016). GRAPES—Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Psychonomic Bulletin %26 Review, 23, 979–990.
Martin,, A., Haxby,, J. V., Lalonde,, F. M., Wiggs,, C. L., & Ungerleider,, L. G. (1995). Discrete cortical regions associated with knowledge of color and knowledge of action. Science, 270, 102–105.
McRae,, K., Cree,, G. S., Seidenberg,, M. S., & McNorgan,, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37(4), 547–559.
Meteyard,, L., Cuadrado,, S. R., Bahrami,, B., & Vigliocco,, G. (2012). Coming of age: A review of embodiment and the neuroscience of semantics. Cortex, 48(7), 788–804.
Mikolov,, T., Sutskever,, I., Chen,, K., Corrado,, G. S., & Dean,, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119). Curran Associates Inc.
Myung,, J. Y., Blumstein,, S. E., & Sedivy,, J. C. (2006). Playing on the typewriter, typing on the piano: Manipulation knowledge of objects. Cognition, 98(3), 223–243.
Öttl,, B., Dudschig,, C., & Kaup,, B. (2017). Forming associations between language and sensorimotor traces during novel word learning. Language and Cognition, 9(1), 156–171.
Ouyang,, L., Boroditsky,, L., & Frank,, M. C. (2017). Semantic coherence facilitates distributional learning. Cognitive Science, 41, 855–884.
Patterson,, K., Nestor,, P. J., & Rogers,, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8(12), 976–987.
Perfetti,, C. (1998). The limits of co‐occurrence: Tools and theories in language research. Discourse Processes, 25, 363–377.
Pulvermüller,, F. (2013). How neurons make meaning: Brain mechanisms for embodied and abstract‐symbolic semantics. Trends in Cognitive Sciences, 17(9), 458–470.
Rice,, G. E., Hoffman,, P., Binney,, R. J., & Lambon Ralph,, M. A. (2018). Concrete versus abstract forms of social concept: An fMRI comparison of knowledge about people versus social terms. Philosophical Transactions of the Royal Society B, 373(1752), 20170136.
Riordan,, B., & Jones,, M. N. (2011). Redundancy in perceptual and linguistic experience: Comparing feature‐based and distributional models of semantic representation. Topics in Cognitive Science, 3(2), 303–345.
Rogers,, T. T., Lambon Ralph,, M. A., Garrard,, P., Bozeat,, S., McClelland,, J. L., Hodges,, J. R., & Patterson,, K. (2004). Structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychological Review, 111(1), 205–235.
Rohde,, D. L. T., Gonnerman,, L., & Plaut,, D. C. (2009). An improved model of semantic similarity based on lexical co‐occurrence. Communications of the ACM. 8, 116.
Sadeghi,, Z., McClelland,, J. L., & Hoffman,, P. (2015). You shall know an object by the company it keeps: An investigation of semantic representations derived from object co‐occurrence in visual scenes. Neuropsychologia, 76, 52–61.
Santos,, A., Chaigneau,, S. E., Simmons,, W. K., & Barsalou,, L. (2011). Property generation reflects word association and situated simulation. Language and Cognition, 3, 83–119.
Searle,, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–457.
Simmons,, W. K., Ramjee,, V., Beauchamp,, M. S., McRae,, K., Martin,, A., & Barsalou,, L. W. (2007). A common neural substrate for perceiving and knowing about color. Neuropsychologia, 45(12), 2802–2810.
Sloutsky,, V. M. (2010). From perceptual categories to concepts: What develops? Cognitive Science, 34(7), 1244–1286.
Snefjella,, B., & Kuperman,, V. (2016). It`s all in the delivery: Effects of context valence, arousal, and concreteness on visual word processing. Cognition, 156, 135–146.
Snefjella,, B., Lana,, N., & Kuperman,, V. (2020). How emotion is learned: Semantic learning of novel words in emotional contexts. Journal of Memory and Language, 115, 104171.
Steyvers,, M. (2010). Combining feature norms and text data with topic models. Acta Psychologica, 133(3), 234–243.
Taylor,, L. J., & Zwaan,, R. A. (2009). Action in cognition: The case of language. Language and Cognition, 1(1), 45–58.
Thompson‐Schill,, S. L., Kan,, I. P., & Oliver,, R. T. (2006). Functional neuroimaging of semantic memory. In R. Cabeza, & A. Kingstone, (Eds.), Handbook of functional neuroimaging of cognition (Vol. 2, pp. 149–190). The MIT Press.
Trumpp,, N. M., Kliese,, D., Hoenig,, K., Haarmeier,, T., & Kiefer,, M. (2013). Losing the sound of concepts: Damage to auditory association cortex impairs the processing of sound‐related concepts. Cortex, 49(2), 474–486.
Vigliocco,, G., Kousta,, S. T., Della Rosa,, P. A., Vinson,, D. P., Tettamanti,, M., Devlin,, J. T., & Cappa,, S. F. (2013). The neural representation of abstract words: The role of emotion. Cerebral Cortex, 24(7), 1767–1777.
Vigliocco,, G., Meteyard,, L., Andrews,, M., & Kousta,, S. (2009). Toward a theory of semantic representation. Language and Cognition, 1(2), 219–247.
Vinson,, D. P., & Vigliocco,, G. (2008). Semantic feature production norms for a large set of objects and events. Behavior Research Methods, 40(1), 183–190.
Vukovic,, N., Feurra,, M., Shpektor,, A., Myachykov,, A., & Shtyrov,, Y. (2017). Primary motor cortex functionally contributes to language comprehension: An online rTMS study. Neuropsychologia, 96, 222–229.
Wernicke,, C. (1900). Grundriss der Psychiatrie, Verlag von Georg Thieme.
Willems,, R. M., Toni,, I., Hagoort,, P., & Casasanto,, D. (2010). Neural dissociations between action verb understanding and motor imagery. Journal of Cognitive Neuroscience, 22(10), 2387–2400.
Willits,, J. A., D`Mello,, S. K., Duran,, N. D., & Olney,, A. (2007, January). Distributional statistics and thematic role relationships. In Proceedings of the annual meeting of the cognitive science society (Vol. 29, No. 29).
Wilson‐Mendenhall,, C. D., Simmons,, W. K., Martin,, A., & Barsalou,, L. W. (2013). Contextual processing of abstract concepts reveals neural representations of nonlinguistic semantic content. Journal of Cognitive Neuroscience, 25(6), 920–935.
Wingfield,, C., & Connell,, L. (2019, preprint). Understanding the role of linguistic distributional knowledge in cognition. PsyArXiv. https://doi.org/10.31234/osf.io/hpm4z
Wittgenstein,, L. (1953/2010). Philosophical investigations. John Wiley %26 Sons.
Yee,, E. (2019). Abstraction and concepts: When, how, where, what and why? Language, Cognition and Neuroscience, 34(10), 1257–1265.
Yee,, E., Chrysikou,, E. G., Hoffman,, E., & Thompson‐Schill,, S. L. (2013). Manual experience shapes object representations. Psychological Science, 24(6), 909–919.
Yee,, E., Huffstetler,, S., & Thompson‐Schill,, S. L. (2011). Function follows form: Activation of shape and function features during object identification. Journal of Experimental Psychology: General, 140(3), 348–363.
Yee,, E., Jones,, M. N., & McRae,, K. (2018). Semantic memory. J. T. Wixted, & S. L. Thompson‐Schill, In The Stevens` handbook of experimental psychology and cognitive neuroscience (Vol. 3). John Wiley %26 Sons.
Yee,, E., & Thompson‐Schill,, S. L. (2016). Putting concepts into context. Psychonomic Bulletin %26 Review, 23(4), 1015–1027.
Yeh,, W., & Barsalou,, L. W. (2006). The situated nature of concepts. The American Journal of Psychology, 119(3), 349–384.
Ziegler,, J. C., Montant,, M., Briesemeister,, B. B., Brink,, T. T., Wicker,, B., Ponz,, A., Bonnard,, M., Jacobs,, A. M., & Braun,, M. (2018). Do words stink? Neural reuse as a principle for understanding emotions in reading. Journal of Cognitive Neuroscience, 30(7), 1023–1032.
Zwaan,, R. A., & Yaxley,, R. H. (2003). Spatial iconicity affects semantic relatedness judgments. Psychonomic Bulletin %26 Review, 10(4), 954–958.

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

Building semantic memory from embodied and distributional language experience

Browse by Topic

Access to this WIREs title is by subscription only.

The latest WIREs articles in your inbox