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      Quantitative modelling demonstrates format‐invariant representations of mathematical problems in the brain

      1 , 2 , 2 , 3 , 4
      European Journal of Neuroscience
      Wiley

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          Abstract

          Mathematical problems can be described in either symbolic form or natural language. Previous studies have reported that activation overlaps exist for these two types of mathematical problems, but it is unclear whether they are based on similar brain representations. Furthermore, quantitative modelling of mathematical problem solving has yet to be attempted. In the present study, subjects underwent 3 h of functional magnetic resonance experiments involving math word and math expression problems, and a read word condition without any calculations was used as a control. To evaluate the brain representations of mathematical problems quantitatively, we constructed voxel‐wise encoding models. Both intra‐ and cross‐format encoding modelling significantly predicted brain activity predominantly in the left intraparietal sulcus (IPS), even after subtraction of the control condition. Representational similarity analysis and principal component analysis revealed that mathematical problems with different formats had similar cortical organization in the IPS. These findings support the idea that mathematical problems are represented in the brain in a format‐invariant manner.

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          Most cited references51

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The assessment and analysis of handedness: The Edinburgh inventory

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              Identifying natural images from human brain activity.

              A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.
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                Author and article information

                Contributors
                Journal
                European Journal of Neuroscience
                Eur J of Neuroscience
                Wiley
                0953-816X
                1460-9568
                March 2023
                February 09 2023
                March 2023
                : 57
                : 6
                : 1003-1017
                Affiliations
                [1 ] Lyon Neuroscience Research Center (CRNL), INSERM U1028‐CNRS UMR5292 University of Lyon Bron France
                [2 ] Center for Information and Neural Networks National Institute of Information and Communications Technology Suita Japan
                [3 ] Graduate School of Frontier Biosciences Osaka University Suita Japan
                [4 ] Graduate School of Medicine Osaka University Suita Japan
                Article
                10.1111/ejn.15925
                900064c7-6727-4d1b-b42a-449cca44044b
                © 2023

                http://creativecommons.org/licenses/by/4.0/

                http://creativecommons.org/licenses/by/4.0/

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