fMRI Evidence for Modality-Specific Processing of Conceptual Knowledge on Six Modalities

Traditional theories assume that amodal representations, such as feature lists and semantic networks, represent conceptual knowledge about the world. According to this view, the sensory, motor, and introspective states that arise during perception and action are irrelevant to representing knowledge. Instead the conceptual system lies outside modality-specific systems and operates according to different principles. Increasingly, however, researchers report that modality-specific systems become active during purely conceptual tasks, suggesting that these systems play central roles in representing knowledge (for a review, see Martin, 2001, Handbook of Functional Neuroimaging of Cognition). In particular, researchers report that the visual system becomes active while processing visual properties, and that the motor system becomes active while processing action properties. The present study corroborates and extends these findings. During fMRI, subjects verified whether or not properties could potentially be true of concepts (e.g., BLENDER-loud). Subjects received only linguistic stimuli, and nothing was said about using imagery. Highly related false properties were used on false trials to block word association strategies (e.g., BUFFALOwinged). To assess the full extent of the modality-specific hypothesis, properties were verified on each of six modalities. Examples include GEMSTONE-glittering (vision), BLENDER-loud (audition), FAUCET-turned (motor), MARBLE-cool (touch), CUCUMBER-bland (taste), and SOAP-perfumed (smell). Neural activity during property verification was compared to a lexical decision baseline. For all six sets of the modalityspecific properties, significant activation was observed in the respective neural system. Finding modality-specific processing across six modalities contributes to the growing conclusion that knowledge is grounded in modality-specific systems of the brain

The contribution of fMRI in the study of visual categorization and expertise

No description supplie

Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains

Existing approaches for multivariate functional principal component analysis are restricted to data on the same one-dimensional interval. The presented approach focuses on multivariate functional data on different domains that may differ in dimension, e.g. functions and images. The theoretical basis for multivariate functional principal component analysis is given in terms of a Karhunen-Lo\`eve Theorem. For the practically relevant case of a finite Karhunen-Lo\`eve representation, a relationship between univariate and multivariate functional principal component analysis is established. This offers an estimation strategy to calculate multivariate functional principal components and scores based on their univariate counterparts. For the resulting estimators, asymptotic results are derived. The approach can be extended to finite univariate expansions in general, not necessarily orthonormal bases. It is also applicable for sparse functional data or data with measurement error. A flexible R-implementation is available on CRAN. The new method is shown to be competitive to existing approaches for data observed on a common one-dimensional domain. The motivating application is a neuroimaging study, where the goal is to explore how longitudinal trajectories of a neuropsychological test score covary with FDG-PET brain scans at baseline. Supplementary material, including detailed proofs, additional simulation results and software is available online.Comment: Revised Version. R-Code for the online appendix is available in the .zip file associated with this article in subdirectory "/Software". The software associated with this article is available on CRAN (packages funData and MFPCA