Dual coding of knowledge in the human brain. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Bi, Yanchao
• Is Part Of: Trends in cognitive sciences. Volume 25:Issue 10(2021)
• Page Start: 883
• Page End: 895
• Publication Date: 2021-10
• Subjects: knowledge -- semantics -- dual coding -- symbolic -- language; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2021.07.006
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : How does the human brain code knowledge about the world? While disciplines such as artificial intelligence represent world knowledge based on human language, neurocognitive models of knowledge have been dominated by sensory embodiment, in which knowledge is derived from sensory/motor experience and supported by high-level sensory/motor and association cortices. The neural correlates of an alternative disembodied symbolic system had previously been difficult to establish. A recent line of studies exploring knowledge about visual properties, such as color, in visually deprived individuals converge to provide positive, compelling evidence for non-sensory, language-derived, knowledge representation in dorsal anterior temporal lobe and extended language network, in addition to the sensory-derived representations, leading to a sketch of a dual-coding knowledge neural framework. Highlights: Neural systems have been assumed to represent knowledge through the embodiment of sensory experiences. This type of knowledge representation is considered fundamentally different from the artificial intelligence approach, which implements knowledge representation using the 'symbolic' approach. Recent behavioral and neuroimaging studies of color knowledge (and other visual knowledge) with and without visual experience provide empirical evidence for the neural coding of non-sensory, language-derived knowledge, along with the sensory-derived representation, in different brain systems. The proposed dual-coding neural framework motivated by these recent empirical findings offers nature's solution to the classical challenges of knowledge representation associated with either only the symbolic tradition in artificial intelligence or only the embodied approach in neuroscience.