Literature DB >> 27315269

Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis.

Alexandra Woolgar1,2, Jade Jackson1,2, John Duncan3,4.   

Abstract

How is the processing of task information organized in the brain? Many views of brain function emphasize modularity, with different regions specialized for processing different types of information. However, recent accounts also highlight flexibility, pointing especially to the highly consistent pattern of frontoparietal activation across many tasks. Although early insights from functional imaging were based on overall activation levels during different cognitive operations, in the last decade many researchers have used multivoxel pattern analyses to interrogate the representational content of activations, mapping out the brain regions that make particular stimulus, rule, or response distinctions. Here, we drew on 100 searchlight decoding analyses from 57 published papers to characterize the information coded in different brain networks. The outcome was highly structured. Visual, auditory, and motor networks predominantly (but not exclusively) coded visual, auditory, and motor information, respectively. By contrast, the frontoparietal multiple-demand network was characterized by domain generality, coding visual, auditory, motor, and rule information. The contribution of the default mode network and voxels elsewhere was minor. The data suggest a balanced picture of brain organization in which sensory and motor networks are relatively specialized for information in their own domain, whereas a specific frontoparietal network acts as a domain-general "core" with the capacity to code many different aspects of a task.

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Year:  2016        PMID: 27315269     DOI: 10.1162/jocn_a_00981

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  25 in total

1.  Multisensory coding in the multiple-demand regions: vibrotactile task information is coded in frontoparietal cortex.

Authors:  Alexandra Woolgar; Regine Zopf
Journal:  J Neurophysiol       Date:  2017-04-12       Impact factor: 2.714

2.  Sensory-Biased and Multiple-Demand Processing in Human Lateral Frontal Cortex.

Authors:  Abigail L Noyce; Nishmar Cestero; Samantha W Michalka; Barbara G Shinn-Cunningham; David C Somers
Journal:  J Neurosci       Date:  2017-08-08       Impact factor: 6.167

3.  An Information-Driven 2-Pathway Characterization of Occipitotemporal and Posterior Parietal Visual Object Representations.

Authors:  Maryam Vaziri-Pashkam; Yaoda Xu
Journal:  Cereb Cortex       Date:  2019-05-01       Impact factor: 5.357

4.  Neural representation of newly instructed rule identities during early implementation trials.

Authors:  Hannes Ruge; Theo Aj Schäfer; Katharina Zwosta; Holger Mohr; Uta Wolfensteller
Journal:  Elife       Date:  2019-11-18       Impact factor: 8.140

5.  Progressive Recruitment of the Frontoparietal Multiple-demand System with Increased Task Complexity, Time Pressure, and Reward.

Authors:  Sneha Shashidhara; Daniel J Mitchell; Yaara Erez; John Duncan
Journal:  J Cogn Neurosci       Date:  2019-07-05       Impact factor: 3.225

6.  Both default and multiple-demand regions represent semantic goal information.

Authors:  Xiuyi Wang; Zhiyao Gao; Jonathan Smallwood; Elizabeth Jefferies
Journal:  J Neurosci       Date:  2021-03-04       Impact factor: 6.167

7.  The dimensionality of neural representations for control.

Authors:  David Badre; Apoorva Bhandari; Haley Keglovits; Atsushi Kikumoto
Journal:  Curr Opin Behav Sci       Date:  2020-08-19

8.  Biased Neural Representation of Feature-Based Attention in the Human Frontoparietal Network.

Authors:  Mengyuan Gong; Taosheng Liu
Journal:  J Neurosci       Date:  2020-10-01       Impact factor: 6.167

9.  The multiple-demand system but not the language system supports fluid intelligence.

Authors:  Alex Woolgar; John Duncan; Facundo Manes; Evelina Fedorenko
Journal:  Nat Hum Behav       Date:  2018-01-29

Review 10.  Integrated Intelligence from Distributed Brain Activity.

Authors:  John Duncan; Moataz Assem; Sneha Shashidhara
Journal:  Trends Cogn Sci       Date:  2020-08-05       Impact factor: 20.229

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