Functional organization of activation patterns in children: Whole brain fMRI imaging during three different cognitive tasks

Booth, James R.; MacWhinney, Brian; Thulborn, Keith R.; Sacco, Kelley; Voyvodic, James T.; Feldman, Heidi M.

doi:10.1016/s0278-5846(99)00025-1

Cited by 111 publications

(59 citation statements)

References 29 publications

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“…For example, if the left hemisphere becomes damaged early in development, homologous regions in the right hemisphere activate during the performance of language tasks (Booth et al, 1999;Müller et al, 1998). In 4CAPS, this shift is a natural consequence of the algorithm by which cognitive functions are assigned to centers on the basis of resource availability and relative specializations, as was demonstrated by the sentence comprehension model's account of the Thulborn et al (1999) stroke lesion data.…”

Section: Plasticitymentioning

confidence: 99%

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Just

Varma

2007

Cognitive, Affective, & Behavioral Neuroscience

168

118

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The relationship of the mind to the brain has established itself at the forefront of scientific interest in large part because of the rapid development of functional magnetic resonance imaging (fMRI). Exciting new findings have often been expressed in the form of compelling images that indicate that brain areas become activated in various tasks or fail to activate in various special populations. These brain images appear to tell a simple and straightforward story. The invited inference is that a one-to-one mapping exists between the activation of brain areas and the execution of certain psychological processes, such as the process of perceiving a face. At first glance, this story imposes some order on the results; some consistencies emerge across brain imaging studies, and some imaging results seem to be related to studies of brain lesions in neuropsychological patients. But the consistencies are limited in scope, and the mapping between the infinity of possible tasks and the finite-indeed, rather small-number of activating brain areas identified by neuroimaging techniques is greatly underdetermined, if not logically troubling. We propose that the idea of a one-to-one mapping of cortical activation to high-level cognitive processes that is suggested by the brain activation images is incorrect-a gross oversimplification of a more complex (and more interesting) many-to-many mapping, governed by more subtle organizational principles. In brief, we argue that thinking is a network phenomenon and suggest the beginnings of a theory of the organization of cortical networks.This article explores how the various cortical areas that subserve cognition might function in conjunction with each other. The main objectives are to specify the operating principles that govern the complex, dynamic, and adaptive relationships among brain areas and to relate brain function to cognitive function. It is not our goal here to specify the cognitive specializations of each brain area, although to describe how areas work together, we necessarily have to hypothesize the cognitive endowments of some brain areas. We believe there is value in developing a theory of how various brain areas collaborate in order to realize cognitive information processing, despite the remaining uncertainty about the functional specializations of the individual areas.As functional imaging provides progressively finer detail about brain activation, computational modeling provides a theory-building workspace in which the new pieces of information can be put together and their cofunctioning can be examined. In this workspace, the component mechanisms can be specified in detail and their ability to account for the observed phenomena can be tested, as a few initial attempts have shown (Arbib, Billard, Iacoboni, & Oztop, 2000; Fincham, Carter, van Veen, Stenger, & Anderson, 2002;Horwitz & Tagamets, 1999;Just, Carpenter, & Varma, 1999). One of the goals of this study is to instantiate the operating principles in a cognitive neuroarchitecture, 4CAPS, that has been develop...

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Section: Plasticitymentioning

confidence: 99%

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Just

Varma

2007

Cognitive, Affective, & Behavioral Neuroscience

168

118

View full text Add to dashboard Cite

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“…Firstly, it measures a relatively pure form of visual-spatial processing ability (Shepard & Metzler, 1971). Booth et al (1999) have suggested that during this process, mentally rotated stimuli are temporally stored in working memory, and the visual-spatial sketchpad is implicated in the manipulation of the visual images (Gathercole, Pickering, Ambridge & Wearing, (2004); Hyun & Luck, 2007). Thus, reduced working memory capacity (i.e.…”

Section: Mathematical Abilities Visual-spatial Abilities and Cerebrmentioning

confidence: 99%

Visual perception, visual-spatial cognition and mathematics: Associations and predictions in children with cerebral palsy

Critten

Campbell

Farran

et al. 2018

Research in Developmental Disabilities

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A B S T R A C TBackground: Previous research suggests that children with cerebral palsy (CP) have impairments in visual-spatial and mathematics abilities, although we know very little about the association between these two domains. Aims: To investigate the extent of visual-spatial and mathematical impairments in children with CP and the associations between these two domains. Method and Procedure: Thirty-two children with predominantly quadriplegic spastic and/or athetoid (dyskinetic) CP (13 years 7 months) and a group of typically developing (TD) children (8 years 6 months) matched by receptive vocabulary were given a battery of visual-spatial and mathematics tasks. Visual-spatial assessments ranged from simple tests of perception to complex reasoning about these stimuli. A standardised test of mathematics ability was administered to both groups. Outcomes and Results: The children with CP had significantly poorer mathematical and visualspatial abilities than the TD group. For the TD group age was the best predictor of mathematical ability, in the CP group receptive vocabulary and visual perception abilities were the best predictors of mathematical ability. Conclusion and Implications: The CP group had extensive difficulties with visual perception; visual short-term memory; visual reasoning; and mental rotation all of which were associated with their mathematical abilities. These findings have implications for the teaching of visual perception and visual memory skills in young children with CP as these may help the development of mathematical abilities. What this paper addsResults supported and extended previous research. Children with CP, in comparison with a TD group, were found to have impairments to visual-spatial perception including processing information in visual short-term memory, in mental rotation tasks and in matrices reasoning tasks. The CP group also had significantly poorer mathematical abilities than the TD group. We provide additional information about the presence of significant relationships between visual-spatial perceptual abilities and the mathematical abilities of both groups of children. We demonstrated that the CP group showed extensive, significant relationships between visual-spatial tests and their mathematical abilities. These findings suggest the potential importance of teaching and developing visual-spatial https://doi

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“…As development progresses from childhood through adolescence, the various areas of the brain lose their plasticity and their capacity for radically new forms of learning (Booth et al, 1999). When children are young, they can easily recover from the loss of large areas of cerebral cortex.…”

Section: Neuronal Commitmentmentioning

confidence: 99%

The competition model: the input, the context, and the brain

MacWhinney¹

2001

Cognition and Second Language Instruction

Self Cite

172

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Language learning is a three-way interaction between the input, the learner, and the interactional context (Bloom, 1974). This three-way interaction provides a general framework for understanding first and second language acquisition, in both naturalistic and formal contexts. In order to elaborate this general framework, we need to model its three components:1. The input. We need to know how the linguistic input can be structured to maximize effective learning. What aspects of the phonology, syntax, semantics, and morphology of the input does the learner use to "crack the code" of the new language? 2. The learner. We need to understand exactly how the cognitive abilities of the learner shape the process and outcome of second language instruction. 3. The context. Traditionally, the classroom environment maintains a rather uniform structure in which interaction is controlled by the instructor. How does this framework affect learning and how can it be varied to improve the learning process?This paper will examine these three components within the framework of the Competition Model (MacWhinney, 1987; MacWhinney & Bates, 1989). To quantify the role of the input in second language learning, the model relies on the concepts of cue reliability and availability. To characterize the cognitive abilities of the learner, the model relies on findings from cognitive neuroscience. To understand the role of the context, the model elaborates the concepts of environmental and social support. The Competition Model views both first and second language learning as constructive, data-driven processes that rely not on universals of linguistic structure, but on universals of cognitive structure. It attributes development to learning and transfer, rather than to the principles and parameters of Universal Grammar (Chomsky, 1965). The InputThe Competition Model is designed to quantify the ways in which distributional properties of the input control language learning and language processing. The basic claim of the model in regard to input is that language comprehension is based on the detection of a series of cues and the reliability and availability of these determines the strength of cues in comprehension. The model contrasts sharply with generative grammar in this regard. Generative grammar views language through the lens of abstract trees in a universal deep structure. The Competition Model recognizes the importance of surface phrase structure, but relates all sentence processing to cue detection and interpretation. Those cues that are highest in reliability and availability are the ones that most strongly control comprehension and which are acquired first during language learning.In order to elaborate this simple relation between cues and comprehension, we need to specify how languages distribute cues across sentences. To do this, the Competition Model has turned to crosslinguistic studies of sentence processing. In particular, the forms or

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Functional organization of activation patterns in children: Whole brain fMRI imaging during three different cognitive tasks

Cited by 111 publications

References 29 publications

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Visual perception, visual-spatial cognition and mathematics: Associations and predictions in children with cerebral palsy

The competition model: the input, the context, and the brain

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