Reverse-engineering the cortical architecture for controlled semantic cognition

Jackson, Rebecca L.; Rogers, Timothy T.; Ralph, Matthew A. Lambon

doi:10.1038/s41562-020-01034-z

Cited by 68 publications

(78 citation statements)

References 81 publications

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“…Our model is supported by a recent computational modeling study ( Jackson et al. 2021 ), which revealed that the core functions of the conceptual system—conceptual abstraction and task dependency—are best achieved by a hierarchical multilevel architecture composed of a modality-specific layer, an intermediate layer (~multimodal regions), and a single top-level hub (~amodal ATL).…”

Section: Discussionsupporting

confidence: 57%

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Task-Dependent Functional and Effective Connectivity during Conceptual Processing

2021

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Conceptual knowledge is central to cognition. Previous neuroimaging research indicates that conceptual processing involves both modality-specific perceptual-motor areas and multimodal convergence zones. For example, our previous functional magnetic resonance imaging (fMRI) study revealed that both modality-specific and multimodal regions respond to sound and action features of concepts in a task-dependent fashion (Kuhnke P, Kiefer M, Hartwigsen G. 2020b. Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb Cortex. 30:3938–3959.). However, it remains unknown whether and how modality-specific and multimodal areas interact during conceptual tasks. Here, we asked 1) whether multimodal and modality-specific areas are functionally coupled during conceptual processing, 2) whether their coupling depends on the task, 3) whether information flows top-down, bottom-up or both, and 4) whether their coupling is behaviorally relevant. We combined psychophysiological interaction analyses with dynamic causal modeling on the fMRI data of our previous study. We found that functional coupling between multimodal and modality-specific areas strongly depended on the task, involved both top-down and bottom-up information flow, and predicted conceptually guided behavior. Notably, we also found coupling between different modality-specific areas and between different multimodal areas. These results suggest that functional coupling in the conceptual system is extensive, reciprocal, task-dependent, and behaviorally relevant. We propose a new model of the conceptual system that incorporates task-dependent functional interactions between modality-specific and multimodal areas.

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Section: Discussionsupporting

confidence: 57%

“…2004 ; Chen et al. 2017 ; Jackson et al. 2021 ) indicates a crucial role of the ATL in conceptual processing across virtually all types of concepts, regardless of their perceptual-motor content.…”

Section: Introductionmentioning

confidence: 99%

Task-Dependent Functional and Effective Connectivity during Conceptual Processing

2021

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“…Many computational models of semantic representation exist and these make specific predictions about the representational structures involved (e.g., O'Connor et al., 2009 ; Rogers et al., 2004 ; Schapiro et al., 2013 ; Taylor et al., 2012 ). Conversely, there has been little formal modelling of semantic control processes (though for recent exceptions, see Hoffman et al., 2018 ; Jackson et al., 2021 ). The lack of established theoretical models makes it difficult to make precise predictions about how neural patterns in control regions should vary and how these areas might be distinguished from those that represent knowledge.…”

Section: Discussionmentioning

confidence: 99%

Stimulus-independent neural coding of event semantics: Evidence from cross-sentence fMRI decoding

et al. 2021

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Multivariate neuroimaging studies indicate that the brain represents word and object concepts in a format that readily generalises across stimuli. Here we investigated whether this was true for neural representations of simple events described using sentences. Participants viewed sentences describing four events in different ways. Multivariate classifiers were trained to discriminate the four events using a subset of sentences, allowing us to test generalisation to novel sentences. We found that neural patterns in a left-lateralised network of frontal, temporal and parietal regions discriminated events in a way that generalised successfully over changes in the syntactic and lexical properties of the sentences used to describe them. In contrast, decoding in visual areas was sentence-specific and failed to generalise to novel sentences. In the reverse analysis, we tested for decoding of syntactic and lexical structure, independent of the event being described. Regions displaying this coding were limited and largely fell outside the canonical semantic network. Our results indicate that a distributed neural network represents the meaning of event sentences in a way that is robust to changes in their structure and form. They suggest that the semantic system disregards the surface properties of stimuli in order to represent their underlying conceptual significance.

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“…Conversely, there has been little formal modelling of semantic control processes (though for recent exceptions, see Hoffman et al, 2018;Jackson, Rogers, & Lambon Ralph, 2021). The lack of established theoretical models makes it difficult to make precise predictions about how neural patterns in control regions should vary and how these areas might be distinguished from those that represent knowledge.…”

Section: Discussionmentioning

confidence: 99%

Stimulus-independent neural coding of event semantics: Evidence from cross-sentence fMRI decoding

Asyraff

Lemarchand

Tamm

et al. 2020

Preprint

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Multivariate neuroimaging studies indicate that the brain represents word and object concepts in a format that readily generalises across stimuli. Here we investigated whether this was true for neural representations of events described using sentences. Participants viewed sentences describing four events in different ways. Multivariate classifiers were trained to discriminate the four events using a subset of sentences, allowing us to test generalisation to novel sentences. We found that neural patterns in a left-lateralised network of frontal, temporal and parietal regions discriminated events in a way that generalised successfully over changes in the syntactic and lexical properties of the sentences used to describe them. In contrast, decoding in visual areas was sentence-specific and failed to generalise to novel sentences. In the reverse analysis, we tested for decoding of syntactic and lexical form, independent of the event being described. Regions displaying this coding were limited and largely fell outside the canonical semantic network. Our results indicate that a distributed neural network represents the meaning of event sentences in a way that is robust to changes in their structure and form. They suggest that the semantic system disregards the surface properties of stimuli in order to represent their underlying conceptual significance.

show abstract

Reverse-engineering the cortical architecture for controlled semantic cognition

Cited by 68 publications

References 81 publications

Task-Dependent Functional and Effective Connectivity during Conceptual Processing

Task-Dependent Functional and Effective Connectivity during Conceptual Processing

Stimulus-independent neural coding of event semantics: Evidence from cross-sentence fMRI decoding

Stimulus-independent neural coding of event semantics: Evidence from cross-sentence fMRI decoding

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