Rigidity, chaos and integration: hemispheric interaction and individual differences in metaphor comprehension

Abstract

Neurotypical individuals cope flexibly with the full range of semantic relations expressed in human language, including metaphoric relations. This impressive semantic ability may be associated with distinct and flexible patterns of hemispheric interaction, including higher right hemisphere (RH) involvement for processing novel metaphors. However, this ability may be impaired in specific clinical conditions, such as Asperger syndrome (AS) and schizophrenia. The impaired semantic processing is accompanied by different patterns of hemispheric interaction during semantic processing, showing either reduced (in Asperger syndrome) or excessive (in schizophrenia) RH involvement. This paper interprets these individual differences using the terms Rigidity, Chaos and Integration, which describe patterns of semantic memory network states that either lead to semantic well-being or are disruptive of it. We argue that these semantic network states lie on a rigidity-chaos semantic continuum. We define these terms via network science terminology and provide network, cognitive and neural evidence to support our claim. This continuum includes left hemisphere (LH) hyper-rigid semantic memory state on one end (e.g., in persons with AS), and RH chaotic and over-flexible semantic memory state on the other end (e.g., in persons with schizophrenia). In between these two extremes lie different states of semantic memory structure which are related to individual differences in semantic creativity. We suggest that efficient semantic processing is achieved by semantic integration, a balance between semantic rigidity and semantic chaos. Such integration is achieved via intra-hemispheric communication. However, impairments to this well-balanced and integrated pattern of hemispheric interaction, e.g., when one hemisphere dominates the other, may lead to either semantic rigidity or semantic chaos, moving away from semantic integration and thus impairing the processing of metaphoric language.

Processing of unconventional stimuli requires the recruitment of the non-specialized hemisphere

“…This intra-hemispheric activation may be required for the complex interplay between specialized and non-specialized systems in processing conventional and unconventional stimuli. These neurocognitive findings converge with a recent novel theory we have proposed in regard to semantic processing (Faust and Kenett, 2014 ): We propose that efficient semantic processing is achieved via a balance between rigid, structured semantic processing (as expressed by the specialized linguistic LH lateralized system) and more chaotic, flexible semantic processing (as expressed by the non-specialized linguistic RH lateralized system). Thus, a well-balanced interaction between specialized and non-specialized neurocognitive systems is seemingly critical for the efficient processing of all types of stimuli, and for coping with the less conventional, creative features of reality.…”

Personality and complex brain networks: The role of openness to experience in default network efficiency

“…For example, van den Heuvel et al [] found that fluid intelligence was inversely related to characteristic path length within a whole‐brain functional network, pointing to an important role of individual differences in cognitive ability in predicting brain network efficiency. Such findings provide compelling evidence for a role of cognitive, personality, and creativity variables in shaping structural and functional brain systems [Beaty et al, ; Faust and Kenett, ; Kenett et al, ; Medaglia et al, ; Ryman et al, ; van den Heuvel et al, ]. Future research should continue to explore factors underlying variation in neurocognitive network topology.…”

Cognitive mechanisms for inferring the meaning of novel signals during symbolisation

“…The claim is not that an ‘Aha!’ experience always happens or only happens for hard relevance-deciding problems, or that all relevance-deciding problems involve insight. Rather, insight problems lie on the opposite of the continuum from analytic, algorithmic, code-like problems [ 39 ], and the more of a burden that is placed on relevance-deciding mechanisms in a novel signalling task, the more likely it is that participants report an ‘Aha!’ experience.…”