Using the Force: STEM Knowledge and Experience Construct Shared Neural Representations of Engineering Concepts

Cetron, Joshua S.; Connolly, Andrew C.; Diamond, Solomon; May, Vicki V.; Haxby, James V.; Kraemer, David J. M.

doi:10.31234/osf.io/ue5fa

Cited by 4 publications

(6 citation statements)

References 54 publications

(66 reference statements)

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“…The finding that the neural representations of the more advanced students were more similar to the faculty than were the representations of more junior students suggests that the neural representations change systematically with additional learning or with academic progress. Thus it may be possible to assess the degree of a student's learning in terms of some property of the neural representations and more generally to investigate the relationship of individual differences in academic achievement to properties of neural representations, as others have suggested 4,6 .…”

Section: Differentiating Faculty From Students Based On Their Neural ...mentioning

confidence: 99%

The neuroscience of advanced scientific concepts

2021

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Cognitive neuroscience methods can identify the fMRI-measured neural representation of familiar individual concepts, such as apple, and decompose them into meaningful neural and semantic components. This approach was applied here to determine the neural representations and underlying dimensions of representation of far more abstract physics concepts related to matter and energy, such as fermion and dark matter, in the brains of 10 Carnegie Mellon physics faculty members who thought about the main properties of each of the concepts. One novel dimension coded the measurability vs. immeasurability of a concept. Another novel dimension of representation evoked particularly by post-classical concepts was associated with four types of cognitive processes, each linked to particular brain regions: (1) Reasoning about intangibles, taking into account their separation from direct experience and observability; (2) Assessing consilience with other, firmer knowledge; (3) Causal reasoning about relations that are not apparent or observable; and (4) Knowledge management of a large knowledge organization consisting of a multi-level structure of other concepts. Two other underlying dimensions, previously found in physics students, periodicity, and mathematical formulation, were also present in this faculty sample. The data were analyzed using factor analysis of stably responding voxels, a Gaussian-naïve Bayes machine-learning classification of the activation patterns associated with each concept, and a regression model that predicted activation patterns associated with each concept based on independent ratings of the dimensions of the concepts. The findings indicate that the human brain systematically organizes novel scientific concepts in terms of new dimensions of neural representation.

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Section: Differentiating Faculty From Students Based On Their Neural ...mentioning

confidence: 99%

The neuroscience of advanced scientific concepts

2021

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“…MDS was employed as a noisereduction step to account for assumed redundancies in the high-dimensional feature space. In previous analyses with this dataset, we found that two feature dimensions were sufficient to show meaningful differences in category-level information between informational networks 24 .…”

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confidence: 73%

“…For a more detailed exploration of the neural instantiation of concept knowledge as a function of prior knowledge and experience, see Cetron et al (preprint) 24 .…”

Section: Discussionmentioning

confidence: 99%

“…The relevant regions for engineering students included both a dorsal stream frontoparietal network, which has been previously implicated in spatial cognition 11,18,25 , and a ventral stream occipito-temporal network previously implicated in visual object identification and categorization 3,4 . For a more detailed exploration of these data with respect to the localization of different representational geometries corresponding to learned concept knowledge, see Cetron et al (preprint) 24 . Figure 4 shows the distinct and overlapping regions contributing to each type of neural score by group.…”

Section: Whole-brain Univariate Neural Score Computation Overview Thmentioning

confidence: 99%

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Decoding individual differences in STEM learning from functional MRI data

Cetron¹,

Connolly²,

Diamond³

et al. 2018

Preprint

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Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

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“…MVPA methods could readily be extended to novice and expert scientists to examine how neural representations change with experience. Similar to this idea, Cetron et al (2017) used MVPA analyses to examine the effects of engineering education on object concepts. Images of, for example, bridges were shown to lay people and engineers, and MVPA revealed how engineers perceive these objects similarly too each other, but differently from naïve participants.…”

Section: The Benefits Of An Educational Neuroscience Of Concept Learningmentioning

confidence: 99%

Developing an Educational Neuroscience of Category Learning

Goldwater

Hilton

Davis

2021

Mind Brain and Education

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There is a disconnect between neuroscience research on concept learning and representation (focusing on categories of concrete objects), and concept learning challenges in science education (which concern systems of causal relationships among objects and events). Bridging this gap will both inform theories of science learning and expand our understanding about how the brain learns and represents knowledge. We examine both literatures, point to where they converge and diverge, and offer paths forward for collaboration between science educators and cognitive neuroscientists to the benefit of each. In cognitive neuroscience, we describe advances in neuroimaging analyses based on cognitive models that directly relate cognitive processes to neural activity. Adapting science education content to support these model-based analyses offers unique opportunities to answer open questions about the cognition behind successful science learning. Further, adapting educational materials for use in neuroimaging may create useful learning exercises that offer unique affordances complementing typical classroom activities.

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Using the Force: STEM Knowledge and Experience Construct Shared Neural Representations of Engineering Concepts

Cited by 4 publications

References 54 publications

The neuroscience of advanced scientific concepts

The neuroscience of advanced scientific concepts

Decoding individual differences in STEM learning from functional MRI data

Developing an Educational Neuroscience of Category Learning

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