Using Support Vector Machines for Facet Partitioning in Multidimensional Scaling.

In this article we focus on interpreting multidimensional scaling (MDS) configurations using facet theory. The facet theory approach is attempting to partition a representational space, facet by facet, into regions with certain simplifying constraints on the regions' boundaries (e.g.

Individual differences in white and grey matter structure associated with verbal habits of thought.

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire.

Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts.

How does STEM knowledge learned in school change students' brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their "novice" peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices' when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures.

Mental models use common neural spatial structure for spatial and abstract content.

Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content.

Individual differences in encoded neural representations within cortical speech production network.

When two individuals view the same item, they do not necessarily perceive an item in the same way. If an individual is presented with a stimulus to be recalled later, the information that is encoded is dependent on the features of the stimulus to which one attends. Past studies have shown that, on the group level, verbal and visual information (e.

Decoding individual differences in STEM learning from functional MRI data.

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.