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James V. Haxby

Bio: James V. Haxby is an academic researcher from Dartmouth College. The author has contributed to research in topics: Dementia & Face perception. The author has an hindex of 110, co-authored 292 publications receiving 55092 citations. Previous affiliations of James V. Haxby include University of Trento & Veterans Health Administration.


Papers
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Journal ArticleDOI
TL;DR: A model for the organization of this system that emphasizes a distinction between the representation of invariant and changeable aspects of faces is proposed and is hierarchical insofar as it is divided into a core system and an extended system.

4,430 citations

Journal ArticleDOI
28 Sep 2001-Science
TL;DR: The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures, and a distinct pattern of response was found for each stimulus category.
Abstract: The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.

3,763 citations

Journal ArticleDOI
TL;DR: How researchers are using multi-voxel pattern analysis methods to characterize neural coding and information processing in domains ranging from visual perception to memory search is reviewed.

2,242 citations

Journal ArticleDOI
TL;DR: Findings from positron emission tomography activation studies have localized these pathways within the human brain, yielding insights into cortical hierarchies, specialization of function, and attentional mechanisms.

1,775 citations

Journal ArticleDOI
15 Feb 1996-Nature
TL;DR: It is found that naming pictures of animals and tools was associated with bilateral activation of the ventral temporal lobes and Broca's area, and the brain regions active during object identification are dependent, in part, on the intrinsic properties of the object presented.
Abstract: An intriguing and puzzling consequence of damage to the human brain is selective loss of knowledge about a specific category of objects. One patient may be unable to identify or name living things, whereas another may have selective difficulty identifying man-made objects. To investigate the neural correlates of this remarkable dissociation, we used positron emission tomography to map regions of the normal brain that are associated with naming animals and tools. We found that naming pictures of animals and tools was associated with bilateral activation of the ventral temporal lobes and Broca's area. In addition, naming animals selectively activated the left medial occipital lobe--a region involved in the earliest stages of visual processing. In contrast, naming tools selectively activated a left premotor area also activated by imagined hand movements, and an area in the left middle temporal gyrus also activated by the generation of action words. Thus the brain regions active during object identification are dependent, in part, on the intrinsic properties of the object presented.

1,451 citations


Cited by
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Journal Article
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

47,974 citations

Journal ArticleDOI
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

40,826 citations

Posted Content
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

28,898 citations

Journal ArticleDOI
TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.

13,678 citations

Journal ArticleDOI
TL;DR: The results suggest that it is important to recognize both the unity and diversity ofExecutive functions and that latent variable analysis is a useful approach to studying the organization and roles of executive functions.

12,182 citations