Tyler Davis

TL;DR: Simulation results reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels, and illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code.

TL;DR: This work focuses on how the combination of study design and pattern estimator impacts the Type I error rate of the subsequent pattern analysis, and shows that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses.

TL;DR: The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data, and observed medial temporal lobe activation consistent with predicted psychological processes that enable exception learning.

TL;DR: Techniques for univariate, adaptation, and multivoxel analysis are discussed and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes.

TL;DR: By establishing a link between neural similarity and psychological memory strength, this work suggests that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels.