Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-pftt2 Total loading time: 0 Render date: 2024-06-12T04:17:10.600Z Has data issue: false hasContentIssue false

1 - Grounding Symbolic Operations in the Brain's Modal Systems

Published online by Cambridge University Press:  05 June 2012

Gün R. Semin  and
Eliot R. Smith
By
Lawrence W. Barsalou
Gün R. Semin
Affiliation:
Koninklijke Nederlandse Akademie van Wetenschappen, Amsterdam
Eliot R. Smith
Affiliation:
Indiana University, Bloomington
Lawrence W. Barsalou
Affiliation:
Emory University, GA, USA
Get access

Summary

A central theme of modern cognitive science is that symbolic interpretation underlies human intelligence. The human brain does not simply register images, as do cameras or other recording devices. A collection of images or recordings does not make a system intelligent. Instead, symbolic interpretation of image content is essential for intelligent activity.

What cognitive operations underlie symbolic interpretation? Across decades of analysis, a consistent set of symbolic operations has arisen repeatedly in logic and knowledge engineering: binding types to tokens; binding arguments to values; drawing inductive inferences from category knowledge; predicating properties and relations of individuals; combining symbols to form complex symbolic expressions; representing abstract concepts that interpret metacognitive states. It is difficult to imagine performing intelligent computation without these operations. For this reason, many theorists have argued that symbolic operations are central, not only to artificial intelligence but to human intelligence (e.g., Fodor, 1975; Pylyshyn, 1973).

Symbolic operations provide an intelligent system with considerable power for interpreting its experience. Using type-token binding, an intelligent system can place individual components of an image into familiar categories (e.g., categorizing components of an image as people and cars). Operations on these categories then provide rich inferential knowledge that allows the perceiver to predict how categorized individuals will behave, and to select effective actions that can be taken (e.g., a perceived person may talk, cars can be driven).

Type
Chapter
Information
Embodied Grounding
Social, Cognitive, Affective, and Neuroscientific Approaches
 , pp. 9 - 42
Publisher: Cambridge University Press
Print publication year: 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
Barsalou, L. W. (1987). The instability of graded structure: Implications for the nature of concepts. In Neisser, U. (Ed.), Concepts and conceptual development: Ecological and intellectual factors in categorization (pp. 101–140). Cambridge, UK: Cambridge University Press.Google Scholar
Barsalou, L. W. (1989). Intraconcept similarity and its implications for interconcept similarity. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and analogical reasoning (pp. 76–121). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Barsalou, L. W. (1992). Frames, concepts, and conceptual fields. In Kittay, E. & Lehrer, A. (Eds.), Frames, fields, and contrasts: New essays in semantic and lexical organization. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Barsalou, L. W. (1993). Flexibility, structure, and linguistic vagary in concepts: Manifestations of a compositional system of perceptual symbols. In Collins, A. C., Gathercole, S. E., & Conway, M. A. (Eds.), Theories of memory (pp. 29–101). London: Lawrence Erlbaum Associates.Google Scholar
Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–660.Google ScholarPubMed
Barsalou, L. W. (2003a). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London: Biological Sciences, 358, 1177–1187.CrossRefGoogle Scholar
Barsalou, L. W. (2003b). Situated simulation in the human conceptual system. Language and Cognitive Processes, 18, 513–562.CrossRefGoogle Scholar
Barsalou, L. W. (2005a). Abstraction as dynamic interpretation in perceptual symbol systems. In Gershkoff-Stowe, L. & Rakison, D. (Eds.), Building object categories (pp. 389–431). Carnegie Symposium Series. Mahwah, NJ: Erlbaum.Google Scholar
Barsalou, L. W. (2005b). Continuity of the conceptual system across species. Trends in Cognitive Sciences, 9, 309–311.CrossRefGoogle Scholar
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.CrossRefGoogle ScholarPubMed
Barsalou, L. W., Breazeal, C., & Smith, L. B. (2007). Language as coordinated non-cognition. Cognitive Processing, 8, 79–91.CrossRefGoogle Scholar
Barsalou, L. W., & Hale, C. R. (1993). Components of conceptual representation: From feature lists to recursive frames. In Mechelen, I., Hampton, J., Michalski, R., & Theuns, P. (Eds.), Categories and concepts: Theoretical views and inductive data analysis (pp. 97–144). San Diego, CA: Academic Press.Google Scholar
Barsalou, L. W., Niedenthal, P. M., Barbey, A., & Ruppert, J. (2003). Social embodiment. In Ross, B. (Ed.), The Psychology of Learning and Motivation, Vol. 43 (pp. 43–92). San Diego, CA: Academic Press.Google Scholar
Barsalou, L. W., Pecher, D., Zeelenberg, R., Simmons, W. K., & Hamann, S. B. (2005). Multimodal simulation in conceptual processing. In Ahn, W., Goldstone, R., Love, B., Markman, A., & Wolff, P. (Eds.), Categorization inside and outside the lab: Essays in honor of Douglas L. Medin (pp. 249–270). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Barsalou, L. W., Santos, A., Simmons, W. K., & Wilson, C. D. (in press). Language and simulation in conceptual processing. In Vega, M., Glenberg, A. M., & Graesser, A. C. (Eds.). Symbols, embodiment, and meaning. Oxford: Oxford University Press.Google Scholar
Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7, 84–91.CrossRefGoogle ScholarPubMed
Barsalou, L. W., & Wiemer-Hastings, K. (2005). Situating abstract concepts. In Pecher, D. and Zwaan, R. (Eds.), Grounding cognition: The role of perception and action in memory, language, and thought (pp. 129–163). New York: Cambridge University Press.CrossRefGoogle Scholar
Caramazza, A., & Shelton, J. R. (1998). Domain-specific knowledge systems in the brain: The animate-inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34.Google ScholarPubMed
Charniak, E., & McDermott, D. (1985). Introduction to artificial intelligence. Reading, MA: Addison-Wesley.Google Scholar
Cree, G.S,. & McRae, K. (2003). Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns). Journal of Experimental Psychology: General, 132, 163–201.CrossRefGoogle Scholar
Damasio, A. R. (1989). Time-locked multiregional retroactivation: A systems-level proposal for the neural substrates of recall and recognition. Cognition, 33, 25–62.CrossRefGoogle ScholarPubMed
Donald, M. (1993). Precis of “Origins of the modern mind: Three stages in the evolution of culture and cognition.”Behavioral and Brain Sciences, 16, 739–791.Google Scholar
Epicurus (1987). Sensation, imagination, and memory. In Long, A. A. & Sedley, D. N. (1987), The Hellenistic philosophers, Vol. 1. New York. Cambridge University Press. (Original work written in 341–270 BC)Google Scholar
Farah, M. J., & McClelland, J. L. (1991). A computational model of semantic memory impairment: Modality specificity and emergent category specificity. Journal of Experimental Psychology: General, 120, 339–357.CrossRefGoogle ScholarPubMed
Fodor, J. A. (1975). The language of thought. Cambridge, MA: Harvard University Press.Google Scholar
Gil-da-Costa, R., Braun, A., Lopes, M., Hauser, M. D., Carson, R. E., Herscovitch, P., & Martin, A. (2004). Toward an evolutionary perspective on conceptual representation: Species-specific calls activate visual and affective processing systems. Proceedings of the National Academy of Sciences, 101, 17516–17521.CrossRefGoogle ScholarPubMed
Glaser, W. R. (1992). Picture naming. Cognition, 42, 61–105.CrossRefGoogle ScholarPubMed
Goldberg, R. F., Perfetti, C. A., & Schneider, W. (2006). Perceptual knowledge retrieval activates sensory brain regions. Journal of Neuroscience, 26, 4917–4921.CrossRefGoogle ScholarPubMed
Kan, I. P., Barsalou, L. W., Solomon, K. O., Minor, J. K., & Thompson-Schill, S. L. (2003). Role of mental imagery in a property verification task: fMRI evidence for perceptual representations of conceptual knowledge. Cognitive Neuropsychology, 20, 525–540.CrossRefGoogle Scholar
Kant, E. (1965). The critique of pure reason (N. K. Smith, Trans). New York: St. Martin's Press. (Original work published in 1787).Google Scholar
Kellenbach, M. L., Brett, M., & Patterson, K. (2001). Large, colorful, or noisy? Attribute- and modal activations during retrieval of perceptual attribute knowledge. Cognitive, Affective, and Behavioral Neuroscience, 1, 207–221.CrossRefGoogle ScholarPubMed
Marques, J. F. (2006). Specialization and semantic organization: Evidence for multiple semantics linked to sensory modalities. Memory & Cognition, 34, 60–67.CrossRefGoogle ScholarPubMed
Martin, A. (2001). Functional neuroimaging of semantic memory. In Cabeza, R. & Kingstone, A. (Eds.), Handbook of functional neuroimaging of cognition (pp. 153–186). Cambridge, MA: MIT Press.Google Scholar
Martin, A. (2007). The representation of object concepts in the brain. Annual Review of Psychology, 58, 25–45.CrossRefGoogle Scholar
McRae, K., Sa, V. R., & Seidenberg, M. S. (1997). On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology: General, 126, 99–130.CrossRefGoogle ScholarPubMed
Morrison, J. B., & Tversky, B. (1997). Body schemas. In Shafto, M. G. & Langley, P. (Eds.), Proceedings of Cognitive Science Society (pp. 525–529). Mahwah, NJ: Erlbaum.Google Scholar
Niedenthal, P. M., Barsalou, L. W., Winkielman, P., Krauth-Gruber, S., & Ric, F. (2005). Embodiment in attitudes, social perception, and emotion. Personality and Social Psychology Review, 9, 184–211.CrossRefGoogle ScholarPubMed
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
O'Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press.Google Scholar
Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart, & Winston.Google Scholar
Paivio, A. (1986). Mental representations: A dual coding approach. New York: Oxford University Press.Google Scholar
Pecher, D., Zeelenberg, R., & Barsalou, L. W. (2003). Verifying properties from different modalities for concepts produces switching costs. Psychological Science, 14, 119–124.CrossRefGoogle ScholarPubMed
Pecher, D., Zeelenberg, R., & Barsalou, L. W. (2004). Sensorimotor simulations underlie conceptual representations: Modality-specific effects of prior activation. Psychonomic Bulletin & Review, 11, 164–167.CrossRefGoogle ScholarPubMed
Pollack, J. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77–105.CrossRefGoogle Scholar
Prinz, J. J. (2002). Furnishing the mind: Concepts and their perceptual basis. Cambridge, MA: MIT Press.Google Scholar
Prinz, J. J., & Barsalou, L. W. (2000). Steering a course for embodied representation. In Dietrich, E. & Markman, A. (Eds.), Cognitive dynamics: Conceptual change in humans and machines (51–77). Cambridge, MA: MIT Press.Google Scholar
Pulvermüller, F. (1999). Words in the brain's language. Behavioral and Brain Sciences, 22, 253–336.CrossRefGoogle ScholarPubMed
Pylyshyn, Z. W. (1973). What the mind's eye tells the mind's brain: A critique of mental imagery. Psychological Bulletin, 80, 1–24.Google Scholar
Reid, T. (1969). Essays on the intellectual powers of man. Cambridge, MA: MIT Press. (Original work published in 1785).Google Scholar
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (Eds.) (1986). Parallel distributed processing. Explorations in the microstructure of cognition, Vol. 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
Sabsevitz, D. S., Medler, D. A., Seidenberg, M., & Binder, J. R. (2005). Modulation of the semantic system by word imageability. NeuroImage, 27, 188–200.CrossRefGoogle ScholarPubMed
Schwanenflugel, P. J. (1991). Why are abstract concepts hard to understand? In Schwanenflugel, P. J. (Ed.), The psychology of word meaning (pp. 223–250). Mahwah, NJ: Erlbaum.Google Scholar
Schyns, P. G., Goldstone, R. L., & Thibaut, J. P. (1998). The development of features in object concepts. Behavioral and Brain Sciences, 21, 1–54.Google ScholarPubMed
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417–424.CrossRefGoogle Scholar
Simmons, W. K., & Barsalou, L. W. (2003). The similarity-in-topography principle: Reconciling theories of conceptual deficits. Cognitive Neuropsychology, 20, 451–486.CrossRefGoogle ScholarPubMed
Simmons, W. K., Martin, A., & Barsalou, L. W. (2005). Pictures of appetizing foods activate gustatory cortices for taste and reward. Cerebral Cortex, 15, 1602–1608.CrossRefGoogle ScholarPubMed
Simmons, W. K., Ramjee, V., Beauchamp, M. S., McRae, K., Martin, A., & Barsalou, L. W W.. (2007). Common neural substrates for perceiving and knowing about color and action. Neuropsychologia, 45, 2802–2810.CrossRefGoogle Scholar
Smith, L. B. (2005). Cognition as a dynamic system: Principles from embodiment. Developmental Review, 25, 278–298.CrossRefGoogle Scholar
Smith, L., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11, 13–29.CrossRefGoogle ScholarPubMed
Smith, E. E., Osherson, D. N., Rips, L. J., & Keane, M. (1988). Combining prototypes: A selective modification model. Cognitive Science, 12, 485–528.CrossRefGoogle Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159–216.CrossRefGoogle Scholar
Solomon, K. O. (1997). The spontaneous use of perceptual representations during conceptual processing. Doctoral dissertation. University of Chicago, Chicago, IL.Google Scholar
Solomon, K. O., & Barsalou, L. W. (2001). Representing properties locally. Cognitive Psychology, 43, 129–169.CrossRefGoogle ScholarPubMed
Solomon, K. O., & Barsalou, L. W. (2004). Perceptual simulation in property verification. Memory & Cognition, 32, 244–259.CrossRefGoogle ScholarPubMed
Thelen, E. (2000). Grounded in the world: Developmental origins of the embodied mind. Infancy, 1, 3–30.CrossRefGoogle Scholar
Thompson-Schill, S. L. (2003), Neuroimaging studies of semantic memory: Inferring “how” from “where.”Neurosychologia, 41, 280–292.CrossRefGoogle Scholar
Gelder, T. (1990). Compositionality: A connectionist variation on a classical theme. Cognitive Science, 14, 355–384.CrossRefGoogle Scholar
Vermeulen, N., Niedenthal, P. M., & Luminet, O. (in press). Switching between sensory and affective systems incurs processing costs. Cognitive Science.Google Scholar
Wiemer-Hastings, K., Krug, J., & Xu, X. (2001). Imagery, context availability, contextual constraint, and abstractness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society, 1134–1139. Mahwah, NJ: Erlbaum.Google Scholar
Wilson, C. D., Simmons, W. K., Martin, A., & Barsalou, L. W. (in preparation). Simulating properties of abstract concepts.
Wu, L. L., & Barsalou, L. W. (submitted). Occlusion affects conceptual combination.
Yeh, W., & Barsalou, L. W. (2006). The situated nature of concepts. American Journal of Psychology, 119, 349–384.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×