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The neural basis for symbolic numbers: multi-constituent neural networks and the connectivity

Liu, Ruizhe (2020) The neural basis for symbolic numbers: multi-constituent neural networks and the connectivity. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

We are able to understand numbers in various formats. For instance, we can read Arabic numerals (defined as the visual code in the current study) from a book and talk about these numbers (the verbal code). We can use our hands to express a number (the manual code). We can also compare the number of jellybeans in different jars without actually counting them (the semantics). How does the human brain represent these different number codes? Existing findings show that the number codes might be locally represented by unique brain regions. However, different number codes might be tightly connected to each other, e.g., one might be internally saying the number word when seeing an Arabic numeral and this potential integration between number codes has not been thoroughly explored in previous studies. Here, we used a number code localizer task to first examine the neural representation of the four number codes separately. Specifically, adults and third to fourth graders were asked to complete a number comparison task (the semantic task) and a phonological comparison (the verbal task) task in an MRI scanner. The stimuli were either Arabic numerals (the visual code) or hand images (the manual code) displaying different numbers. The contrast between different number codes yielded multiple unique brain regions supporting the neural representation of each number code. We then tested the context-dependent connectivity among all the brain regions yielded by the contrasting analyses and found that some brain regions were involved in representing more than one number code. The results stand against the localist view and support the notion that the neural representation of each number code is distributed
among a network consisting of brain regions unique to each number code as well as brain regions common to several number codes.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Liu, Ruizherul23@pitt.edurul230000-0003-3606-2759
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairLibertus, MelissaLIBERTUS@pitt.edu
Committee MemberFiez, Juliefiez@pitt.edu
Committee MemberCoutanche, Marcmarc.coutanche@pitt.edu
Committee MemberCantlon, Jessicajcantlon@andrew.cmu.edu
Date: 8 June 2020
Date Type: Publication
Defense Date: 30 March 2020
Approval Date: 8 June 2020
Submission Date: 8 April 2020
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 120
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Psychology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: symbolic numbers, numerical processing, fMRI, neural network, context-dependent connectivity
Date Deposited: 08 Jun 2020 16:37
Last Modified: 08 Jun 2020 16:37
URI: http://d-scholarship.pitt.edu/id/eprint/38845

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