Neurocognitive development of relational reasoning

Crone, Eveline A.; Wendelken, Carter; Leijenhorst, Linda Van; Honomichl, Ryan; Christoff, Kalina; Bunge, Silvia A.

doi:10.1111/j.1467-7687.2008.00743.x

Cited by 189 publications

(202 citation statements)

References 40 publications

(69 reference statements)

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“…Consistent with this perspective, our group has found that lateral parietal activation during performance of a Raven-like matrix reasoning task scales with the number of relations to be considered [34]. Further, lateral parietal cortex is engaged more strongly when fMRI study participants represent ordered relations (e.g., x is larger than y) than associative relations (e.g., x and y are related) on a test of transitive inference [35] -i.e., when the nature of the relationships must be considered to solve the problem.…”

Section: Neurodevelopment Of Reasoningsupporting

confidence: 58%

Neurodevelopment of relational reasoning: Implications for mathematical pedagogy

Singley

Bunge

2014

Trends in Neuroscience and Education

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a b s t r a c tReasoning ability supports the development of mathematics proficiency, as demonstrated by correlational and longitudinal evidence, and yet this skill is not emphasized in traditional elementary mathematics curricula. We propose that targeting reasoning skills from elementary school onward could help more students succeed in advanced mathematics courses. Here, we review the links between reasoning and mathematics, discuss the neural basis and development of reasoning ability, and identify promising school curricula.

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Section: Neurodevelopment Of Reasoningsupporting

confidence: 58%

Neurodevelopment of relational reasoning: Implications for mathematical pedagogy

Singley

Bunge

2014

Trends in Neuroscience and Education

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“…Other researchers who have developed their own Raven-like matrix problems have also defined the difficulty of their problems according to the number of rules or relations that must be combined to solve the problem (Arendasy & Sommer, 2005;Christoff et al, 2001;Crone et al, 2009;Green & Kluever, 1992;Halford, Wilson, & Phillips, 1998;Waltz et al, 1999). A zero-relation problem is one in which a matrix contains no changes and the answer is a simple one-to-one match to the shape that is repeated in the matrix.…”

Section: P Smentioning

confidence: 99%

Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

Matzen

Benz

Dixon

et al. 2010

Behavior Research Methods

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Raven's Progressive Matrices and similar matrix problems have been used in research and intelligence testing for decades. The matrix problems serve as a nonverbal test of analogical reasoning and are thought to measure analytical intelligence (Carpenter, Just, & Shell, 1990), also known as fluid intelligence (cf. Cattell, 1963). Although these matrix problems have a wide variety of research applications, the relatively small number of matrices in Raven's original sets (108 total;Raven, Court, & Raven, 1998) limits their utility in several domains, including neuroimaging experiments and computational modeling of cognitive processes.Our goal in the present study was to create and characterize a very large set of matrix problems that have properties similar to those of Raven's original matrices. We sought to create the matrix set in a systematic way that would allow researchers to have a great deal of control over the underlying structure, surface features, and difficulty of the matrix problems. This in turn would allow researchers to systematically expand the range of difficulty in their stimulus sets beyond the range provided by the original Raven's matrices. To accomplish these goals, we analyzed the underlying structures in Raven's original Standard Progressive Matrices (SPMs) to determine what types and combinations of relations were used. On the basis of that analysis, we developed software that can use the same underlying patterns to generate large numbers of unique matrix problems using parameters chosen by the researcher. Specifically, the software is designed so that researchers can choose the type, direction, and number of relations in a problem and create any number of unique matrices that share the same underlying structure (e.g., changes in numerosity in a diagonal pattern) but have different surface features (e.g., shapes, colors).Finally, we used the matrix generation software to produce a representative set of matrix problems that cover the range of underlying structures that can be produced by the software. This set of matrices was compared with Raven's SPMs in a norming study. The first goal of the norming study was to compare the difficulty of the generated matrices with the difficulty of the SPMs with the same underlying structure. The second goal was to assess the difficulty of specific structural features within the matrices and the range of problem difficulties that can be produced by the matrix generation software when those features are combined. Analysis of Raven's Progressive Matrix StructuresPrevious studies have analyzed the factors that contribute to the difficulty of Raven and Raven-like matrix problems. Raven's Progressive Matrices is a widely used test for assessing intelligence and reasoning ability (Raven, Court, & Raven, 1998). Since the test is nonverbal, it can be applied to many different populations and has been used all over the world (Court & Raven, 1995). However, relatively few matrices are in the sets developed by Raven, which limits their use in experiments requiring l...

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“…No previous study has directly investigated the development of this ability. In children, manipulation of SI thoughts has been studied in the context of fluid intelligence (Wright et al, 2008;Crone et al, 2009) and working memory (WM) tasks (Crone et al, 2006), while the ability to resist distracting SO information has been studied in perceptual (Bunge et al, 2002;Booth et al, 2003) and WM tasks (Olesen et al, 2007). Here, we used a single task that could be performed on the basis of either SO or SI information, without high WM requirements (Gilbert et al, , 2008.…”

Section: Introductionmentioning

confidence: 99%

Development of the Selection and Manipulation of Self-Generated Thoughts in Adolescence

Dumontheil¹,

Hassan²,

Gilbert³

et al. 2010

J. Neurosci.

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The ability to select and manipulate self-generated (stimulus-independent, SI), as opposed to stimulus-oriented (SO), information, in a controlled and flexible way has previously only been studied in adults. This ability is thought to rely in part on the rostrolateral prefrontal cortex (RLPFC), which continues to mature anatomically during adolescence. We investigated (1) the development of this ability behaviorally, (2) the associated functional brain development, and (3) the link between functional and structural maturation. Participants classified according to their shape letters either presented visually (SO phases) or that they generated in their head by continuing the alphabet sequence (SI phases). SI phases were performed in the presence or absence of distracting letters. A total of 179 participants (7-27 years old) took part in a behavioral study. Resistance to visual distractors exhibited small improvements with age. SI thoughts manipulation and switching between SI and SO thoughts showed steeper performance improvements extending into late adolescence. Thirtyseven participants (11-30 years old) took part in a functional MRI (fMRI) study. SI thought manipulation and switching between SO and SI thought were each associated with brain regions consistently recruited across age. A single frontal brain region in each contrast exhibited decreased activity with age: left inferior frontal gyrus/anterior insula for SI thought manipulation, and right superior RLPFC for switchingbetweenSOandSIthoughts.Byintegratingstructuralandfunctionaldata,wedemonstratedthattheobservedfunctionalchangeswith age were not purely consequences of structural maturation and thus may reflect the maturation of neurocognitive strategies.

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Neurocognitive development of relational reasoning

Cited by 189 publications

References 40 publications

Neurodevelopment of relational reasoning: Implications for mathematical pedagogy

Neurodevelopment of relational reasoning: Implications for mathematical pedagogy

Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

Development of the Selection and Manipulation of Self-Generated Thoughts in Adolescence

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