Evidence map›Paper›PMID 34089873›Full record

ArticleNeuroImage2021

Distinct and common neural coding of semantic and non-semantic control demands.

Zhiyao Gao, Li Zheng, Rocco Chiou, André Gouws, Katya Krieger-Redwood, Xiuyi Wang, Dominika Varga, Matthew A Lambon Ralph, Jonathan Smallwood, Elizabeth Jefferies

Abstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

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  9. The Dimensionality of Neural Coding for Cognitive Control Is Gradually Transformed within the Lateral Prefrontal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhiyao GaoDepartment of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom.
Li ZhengDepartment of Psychology, University of Arizona, Tucson, AZ 85719, USA.
Rocco ChiouMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
André GouwsDepartment of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom.
Katya Krieger-RedwoodDepartment of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom.
Xiuyi WangDepartment of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom.
Dominika VargaSchool of Psychology, University of Sussex, Brighton BN1 9RH, United Kingdom.
Matthew A Lambon RalphMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
Jonathan SmallwoodDepartment of Psychology, Queen's University, Kingston, ON K7L 3N6, Canada.
Elizabeth JefferiesDepartment of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom. Electronic address: beth.jefferies@york.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The flexible retrieval of knowledge is critical in everyday situations involving problem solving, reasoning and social interaction. Current theories emphasise the importance of a left-lateralised semantic control network (SCN) in supporting flexible semantic behaviour, while a bilateral multiple-demand network (MDN) is implicated in executive functions across domains. No study, however, has examined whether semantic and non-semantic demands are reflected in a common neural code within regions specifically implicated in semantic control. Using functional MRI and univariate parametric modulation analysis as well as multivariate pattern analysis, we found that semantic and non-semantic demands gave rise to both similar and distinct neural responses across control-related networks. Though activity patterns in SCN and MDN could decode the difficulty of both semantic and verbal working memory decisions, there was no shared common neural coding of cognitive demands in SCN regions. In contrast, regions in MDN showed common patterns across manipulations of semantic and working memory control demands, with successful cross-classification of difficulty across tasks. Therefore, SCN and MDN can be dissociated according to the information they maintain about cognitive demands.

Indexed as

AssociationAdultBrain MappingCerebral CortexExecutive FunctionFemaleHumansMagnetic Resonance ImagingMaleMemory, Short-TermNerve NetPattern Recognition, VisualReadingSemanticsSupport Vector MachineVerbal Learning

Identifiers

PMID34089873
PMCPMC8271095

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.