Abstract not found

Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts—even concepts about action and perception—are symbolic and abstract, and therefore must be implemented outside the brain’s sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called “abstract ” concepts that constitute the meanings of grammatical constructions and general inference patterns.

Three theories currently compete to explain the conceptual deficits that result from brain damage: sensory-functional theory, domain-specific theory, and conceptual structure theory. We argue that all three theories capture important aspects of conceptual deficits, and offer different insights into their origins. Conceptual topography theory (CTT) integrates these insights, beginning with A. R. Damasio’s (1989) convergence zone theory and elaborating it with the similarity-in-topography (SIT) principle. According to CTT, feature maps in sensory-motor systems represent the features of a category’s exemplars. A hierarchical system of convergence zones then conjoins these features to form both property and category representations. According to the SIT principle, the proximity of two conjunctive neurons in a convergence zone increases with the similarity of the features they conjoin. As a result, conjunctive neurons become topographically organised into local regions that represent properties and categories. Depending on the level and location of a lesion in this system, a wide variety of deficits is possible. Consistent with the literature, these deficits range from the loss of a single category to the loss of multiple categories that share sensory-motor properties.

A key issue in cognitive neuroscience concerns the neural representation of conceptual knowledge. Currently, debate focuses around the issue of whether there are neural regions specialised for the processing of specific semantic attributes or categories, or whether concepts are represented in an undifferentiated neural system. Neuropsychological studies of patients with selective semantic deficits and previous neuroimaging studies do not unequivocally support either account. We carried out a PET study to determine whether there is any regional specialisation for the processing of concepts from different semantic categories using picture stimuli and a semantic categorisation task. We found robust activation of a large semantic network extending from left inferior frontal cortex into the inferior temporal lobe and including occipital cortex and the fusiform gyrus. The only category effect that we found was additional activation for animals in the right occipital cortex, which we interpret as being due to the extra visual processing demands required in order to differentiate one animal from another. We also carried out analyses in specific cortical regions that have been claimed to be preferentially activated for various categories, but found no evidence of any differential activation as a function of category. We interpret these data within the framework of cognitive accounts in which conceptual knowledge is represented within a nondifferentiated distributed system.

According to perceptual symbol systems (Barsalou, 1999), sensory-motor simulations underlie the representation of concepts. It follows that sensory-motor phenomena should arise in conceptual processing. Previous studies have shown that switching from one modality to another during perceptual processing incurs a processing cost. If perceptual simulation underlies conceptual processing, then verifying the properties of concepts should exhibit a switching cost as well. For example, verifying a property in the auditory modality (e.g., BLENDER-loud) should be slower after verifying a property in a different modality (e.g., CRANBERRIES-tart) than in the same modality (e.g., LEAVES-rustling). Only words were presented to subjects, and there were no instructions to use imagery. Nevertheless switching modalities incurred a cost, analogous to switching modalities in perception. A second experiment showed that this effect was not due to associative priming between properties in the same modality. These results support the hypothesis that perceptual simulation underlies conceptual processing. Modern psychology relies heavily on the digital computer as a metaphor for human cognition (e.g., Fodor, 1975; Pylyshyn, 1984). According to this view, the software of the mind can be distinguished from the hardware of the body, with mental representations being amodal redescriptions of sensory-motor experience. Increasingly, however, researchers argue that this approach is fundamentally wrong, suggesting instead that interactions between sensory-motor systems and the physical world underlie cognition. For example, Barsalou's (1999) theory of perceptual symbol systems proposes that conceptual knowledge is grounded in sensory-motor systems. To represent a concept, neural systems partially run a...

According to perceptual symbol systems, sensorimotor simulations underlie the representation of concepts. It follows that sensorimotor phenomena should arise in conceptual processing. Previous studies have shown that switching from one modality to another during perceptual processing incurs a processing cost. If perceptual simulation underlies conceptual processing, then verifying the properties of concepts should exhibit a switching cost as well. For example, verifying a property in the auditory modality (e.g., BLENDER-loud) should be slower after verifying a property in a different modality (e.g., CRANBERRIES-tart) than after verifying a property in the same modality (e.g., LEAVES-rustling). Only words were presented to subjects, and there were no instructions to use imagery. Nevertheless, switching modalities incurred a cost, analogous to the cost of switching modalities in perception. A second experiment showed that this effect was not due to associative priming between properties in the same modality. These results support the hypothesis that perceptual simulation underlies conceptual processing.

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as experiential data and distributional data. Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by contrast, describe the statistical distribution of words across spoken and written language. The authors claim that experiential and distributional data represent distinct data types and that each is a nontrivial source of semantic information. Their theoretical proposal is that human semantic representations are derived from an optimal statistical combination of these 2 data types. Using a Bayesian probabilistic model, they demonstrate how word meanings can be learned by treating experiential and distributional data as a single joint distribution and learning the statistical structure that underlies it. The semantic representations that are learned in this manner are measurably more realistic—as verified by comparison to a set of human-based measures of semantic representation—than those available from either data type individually or from both sources independently. This is not a result of merely using quantitatively more data, but rather it is because experiential and distributional data are qualitatively distinct, yet intercorrelated, types of data. The semantic representations that are learned are based on statistical structures that exist both within and between the experiential and distributional data types.

If people represent concepts with perceptual simulations, two predictions follow in the property verification task (e.g., Is face a property of GORILLA?). First, perceptual variables such as property size should predict the performance of neutral subjects, because these variables determine the ease of processing properties in perceptual simulations (i.e., perceptual effort). Second, uninstructed neutral subjects should spontaneously construct simulations to verify properties and therefore perform similarly to imagery subjects asked explicitly to use images (i.e., instructional equivalence). As predicted, neutral subjects exhibited both perceptual effort and instructional equivalence, consistent with the assumption that they construct perceptual simulations spontaneously to verify properties. Notably, however, this pattern occurred only when highly associated false properties prevented the use of a word association strategy. In other conditions that used unassociated false properties, the associative strength between concept and property words became a diagnostic cue for true versus false responses, so that associative strength became a better predictor of verification than simulation. This pattern indicates that conceptual tasks engender mixtures of simulation and word association, and that researchers must deter word association strategies when the goal is to assess conceptual knowledge. Researchers increasingly report that modality-specific

Abstract not found

of objects