Neural Correlates of Concreteness in Semantic Categorization

2007 ◽  
Vol 19 (8) ◽  
pp. 1407-1419 ◽  
Author(s):  
Penny M. Pexman ◽  
Ian S. Hargreaves ◽  
Jodi D. Edwards ◽  
Luke C. Henry ◽  
Bradley G. Goodyear
Keyword(s):  
Semantic Representation ◽  
Dual Coding ◽  
Abstract Concepts ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Symbol Systems ◽  
Semantic Categorization ◽  
Context Availability ◽  
Abstract Words ◽  
Perceptual Symbol Systems

In some contexts, concrete words (CARROT) are recognized and remembered more readily than abstract words (TRUTH). This concreteness effect has historically been explained by two theories of semantic representation: dual-coding [Paivio, A. Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287, 1991] and context-availability [Schwanenflugel, P. J. Why are abstract concepts hard to understand? In P. J. Schwanenflugel (Ed.), The psychology of word meanings (pp. 223–250). Hillsdale, NJ: Erlbaum, 1991]. Past efforts to adjudicate between these theories using functional magnetic resonance imaging have produced mixed results. Using event-related functional magnetic resonance imaging, we reexamined this issue with a semantic categorization task that allowed for uniform semantic judgments of concrete and abstract words. The participants were 20 healthy adults. Functional analyses contrasted activation associated with concrete and abstract meanings of ambiguous and unambiguous words. Results showed that for both ambiguous and unambiguous words, abstract meanings were associated with more widespread cortical activation than concrete meanings in numerous regions associated with semantic processing, including temporal, parietal, and frontal cortices. These results are inconsistent with both dual-coding and context-availability theories, as these theories propose that the representations of abstract concepts are relatively impoverished. Our results suggest, instead, that semantic retrieval of abstract concepts involves a network of association areas. We argue that this finding is compatible with a theory of semantic representation such as Barsalou's [Barsalou, L. W. Perceptual symbol systems. Behavioral & Brain Sciences, 22, 577–660, 1999] perceptual symbol systems, whereby concrete and abstract concepts are represented by similar mechanisms but with differences in focal content.

Perceiving abstract concepts

1999 ◽  
Vol 22 (4) ◽  
pp. 635-636
Author(s):  
Katja Wiemer-Hastings ◽  
Arthur C. Graesser
Keyword(s):  
Abstract Concepts ◽  
Symbol Systems ◽  
Abstract Noun ◽  
Perceptual Symbol Systems

The meanings of abstract concepts depend on context. Perceptual symbol systems (PSS) provide a powerful framework for representing such context. Whereas a few expected difficulties for simulations are consistent with empirical findings, the theory does not clearly predict simulations of specific abstract concepts in a testable way and does not appear to distinguish abstract noun concepts (like truth) from their stem concepts (such as true).

Perceptions of perceptual symbols

1999 ◽  
Vol 22 (4) ◽  
pp. 637-660 ◽  
Author(s):  
Lawrence W. Barsalou
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Causal Theory ◽  
Category Structure ◽  
Abstract Concepts ◽  
Situated Action ◽  
Symbol Systems ◽  
Perceptual Symbols ◽  
Sensory Motor ◽  
Perceptual Symbol Systems

Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.

scholarly journals Modelling concrete and abstract concepts using brain-constrained deep neural networks

2021 ◽  
Author(s):  
Malte R. Henningsen-Schomers ◽  
Friedemann Pulvermüller
Keyword(s):  
Current Knowledge ◽  
Semantic Representation ◽  
Family Resemblance ◽  
Semantic Feature ◽  
Abstract Concepts ◽  
Strongly Connected ◽  
Feature Sharing ◽  
Abstract Words ◽  
Feature Overlap ◽  
Concrete Concepts

AbstractA neurobiologically constrained deep neural network mimicking cortical areas relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically ‘ground’ concrete and abstract concepts. Grounding sets consisted of three grounding patterns with neurons representing specific perceptual or action-related features; neurons were either unique to one pattern or shared between patterns of the same set. Concrete categories were modelled as pattern triplets overlapping in their ‘shared neurons’, thus implementing semantic feature sharing of all instances of a category. In contrast, abstract concepts had partially shared feature neurons common to only pairs of category instances, thus, exhibiting family resemblance, but lacking full feature overlap. Stimulation with concrete and abstract conceptual patterns and biologically realistic unsupervised learning caused formation of strongly connected cell assemblies (CAs) specific to individual grounding patterns, whose neurons were spread out across all areas of the deep network. After learning, the shared neurons of the instances of concrete concepts were more prominent in central areas when compared with peripheral sensorimotor ones, whereas for abstract concepts the converse pattern of results was observed, with central areas exhibiting relatively fewer neurons shared between pairs of category members. We interpret these results in light of the current knowledge about the relative difficulty children show when learning abstract words. Implications for future neurocomputational modelling experiments as well as neurobiological theories of semantic representation are discussed.

Neural substrates of object identification: Functional magnetic resonance imaging evidence that category and visual attribute contribute to semantic knowledge

2009 ◽  
Vol 15 (2) ◽  
pp. 169-181 ◽  
Author(s):  
CHRISTINA E. WIERENGA ◽  
WILLIAM M. PERLSTEIN ◽  
MICHELLE BENJAMIN ◽  
CHRISTIANA M. LEONARD ◽  
LESLIE GONZALEZ ROTHI ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Semantic Representation ◽  
Semantic Knowledge ◽  
Fusiform Gyrus ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Visual Attributes ◽  
Visual Attribute

AbstractRecent findings suggest that neural representations of semantic knowledge contain information about category, modality, and attributes. Although an object’s category is defined according to shared attributes that uniquely distinguish it from other category members, a clear dissociation between visual attribute and category representation has not yet been reported. We investigated the contribution of category (living and nonliving) and visual attribute (global form and local details) to semantic representation in the fusiform gyrus. During functional magnetic resonance imaging (fMRI), 40 adults named pictures of animals, tools, and vehicles. In a preliminary study, identification of objects in these categories was differentially dependent on global versus local visual feature processing. fMRI findings indicate that activation in the lateral and medial regions of the fusiform gyrus distinguished stimuli according to category, that is, living versus nonliving, respectively. In contrast, visual attributes of global form (animals) were associated with higher activity in the right fusiform gyrus, while local details (tools) were associated with higher activity in the left fusiform gyrus. When both global and local attributes were relevant to processing (vehicles), cortex in both left and right medial fusiform gyri was more active than for other categories. Taken together, results support distinctions in the role of visual attributes and category in semantic representation. (JINS, 2009, 15, 169–181.)

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