22 research outputs found
Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings
Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts
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Multi-view kernel construction
In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. These multiple views of the data are often not directly comparable to one another, and thus a principled method for their integration is warranted. In this paper we develop a new algorithm to leverage information from multiple views for unsupervised clustering by constructing a custom kernel. We generate a multipartite graph (with the number of parts given by the number of views) that induces a kernel we then use for spectral clustering. Our algorithm can be seen as a generalization of co-clustering and spectral clustering and a relative of Kernel Canonical Correlation Analysis. We demonstrate the algorithm on four data sets: an illustrative artificial data set, synthetic fMRI data, voxels from an fMRI study, and a collection of web pages. Finally, we compare its performance to common alternatives
Exploring commonalities across participants in the neural representation of objects
Department of Psycholog