Abstract
Research on grounded cognition suggests that the processing of a word or concept reactivates the perceptual representations that are associated with the referent object. The objective of this work is to demonstrate how behavioral and functional neuroimaging data on grounded cognition can be understood as different manifestations of the same cortical circuit designed to achieve stable category learning, as proposed by the adaptive resonance theory (ART). We showed that the ART neural network provides a mechanistic explanation of why reaction times in behavioral studies depend on the expectation or attentional priming created by the word meaning (Richter and Zwaan, 2009). A mismatch between top-down expectation and bottom-up sensory data activates an orienting subsystem that slows execution of the current task. Furthermore, we simulated the data from functional neuroimaging studies of color knowledge retrieval that showed anterior shift (Chao and Martin, 1999; Thompson-Schill, 2003) and an overlap effect (Simmons et al., 2007; Hsu et al., 2011) in the left fusiform gyrus. We explain the anterior effect as a result of the partial activation of different components of the same ART circuit in the condition of passive viewing. Conversely, a demanding perceptual task requires activation of the whole ART circuit. This condition is reflected in the fMRI image as an overlap between cortical activation during perceptual and conceptual processing. We conclude that the ART neural network is able to explain how the brain grounds symbols in perception via perceptual simulation.
Highlights
The classical approach to knowledge representation assumes that a cognitive system contains symbols that refer to an aspect of the external world
Another property is that symbols are arbitrarily related to their referents, that is, any symbol can represent anything in the world; the specific meaning that will be attached to the particular symbol is a matter of convention (Markman and Dietrich, 2000)
Purely symbolic representations suffer from the symbol grounding problem; that is, they are unable to tie the meaning of the symbol to its referent object (Harnad, 1990; Glenberg and Robertson, 2000)
Summary
The classical approach to knowledge representation assumes that a cognitive system contains symbols that refer to an aspect of the external world. An important property of symbols is that they are amodal or detached from specific sensory or motor experiences produced by the referent objects Another property is that symbols are arbitrarily related to their referents, that is, any symbol can represent anything in the world; the specific meaning that will be attached to the particular symbol is a matter of convention (Markman and Dietrich, 2000). These properties of symbolic representation allow great flexibility in modeling cognitive processing because they reduce the computational burden and allow the focus to be on the abstract relations between. Purely symbolic representations suffer from the symbol grounding problem; that is, they are unable to tie the meaning of the symbol to its referent object (Harnad, 1990; Glenberg and Robertson, 2000).
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