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Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations.

Kaplan JT, Man K, Greening SG - Front Hum Neurosci (2015)

Bottom Line: MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching.Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands.We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

View Article: PubMed Central - PubMed

Affiliation: Brain and Creativity Institute, University of Southern California Los Angeles, CA, USA ; Department of Psychology, University of Southern California Los Angeles, CA, USA.

ABSTRACT
Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

No MeSH data available.


A schematic of Multivariate Pattern Similarity Analysis. In this example, subjects either see or touch two classes of objects, apples and bananas. (A) First, a classifier is trained on the labeled patterns of neural activity evoked by seeing the two objects. (B) Next, the same classifier is given unlabeled data from when the subject touches the same objects and makes a prediction. If the classifier, which was trained on data from vision, can correctly identify the patterns evoked by touch, then we conclude that the representation is modality invariant.
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Figure 1: A schematic of Multivariate Pattern Similarity Analysis. In this example, subjects either see or touch two classes of objects, apples and bananas. (A) First, a classifier is trained on the labeled patterns of neural activity evoked by seeing the two objects. (B) Next, the same classifier is given unlabeled data from when the subject touches the same objects and makes a prediction. If the classifier, which was trained on data from vision, can correctly identify the patterns evoked by touch, then we conclude that the representation is modality invariant.

Mentions: Thus, by requiring learning transfer from training to testing datasets, MVPA constitutes a test for the consistency of information across different sets of data. This property of the test has begun to be exploited by neuroscientists who are interested in how neural patterns may be similar across different kinds of stimulus presentations, sensory modalities, and cognitive contexts. For instance, a classifier trained on data from visual presentation of objects may be asked to then classify neural patterns elicited by tactile presentations of the same objects. The success of learning transfer in such an experiment would provide direct evidence that the neural representations are similar across the two contexts. In the case of this example we are testing whether or not there is a common coding of object identity that is invariant to visual or tactile presentation. We suggest calling this kind of analysis, when a classifier is trained on data from one cognitive domain and tested on data from another, Multivariate Cross-Classification (MVCC). A schematic of MVCC is presented in Figure 1. In this paper we discuss methodological issues relevant to MVCC and review recent work employing this technique in order to demonstrate its power in contributing to the understanding of abstract neural representations.


Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations.

Kaplan JT, Man K, Greening SG - Front Hum Neurosci (2015)

A schematic of Multivariate Pattern Similarity Analysis. In this example, subjects either see or touch two classes of objects, apples and bananas. (A) First, a classifier is trained on the labeled patterns of neural activity evoked by seeing the two objects. (B) Next, the same classifier is given unlabeled data from when the subject touches the same objects and makes a prediction. If the classifier, which was trained on data from vision, can correctly identify the patterns evoked by touch, then we conclude that the representation is modality invariant.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373279&req=5

Figure 1: A schematic of Multivariate Pattern Similarity Analysis. In this example, subjects either see or touch two classes of objects, apples and bananas. (A) First, a classifier is trained on the labeled patterns of neural activity evoked by seeing the two objects. (B) Next, the same classifier is given unlabeled data from when the subject touches the same objects and makes a prediction. If the classifier, which was trained on data from vision, can correctly identify the patterns evoked by touch, then we conclude that the representation is modality invariant.
Mentions: Thus, by requiring learning transfer from training to testing datasets, MVPA constitutes a test for the consistency of information across different sets of data. This property of the test has begun to be exploited by neuroscientists who are interested in how neural patterns may be similar across different kinds of stimulus presentations, sensory modalities, and cognitive contexts. For instance, a classifier trained on data from visual presentation of objects may be asked to then classify neural patterns elicited by tactile presentations of the same objects. The success of learning transfer in such an experiment would provide direct evidence that the neural representations are similar across the two contexts. In the case of this example we are testing whether or not there is a common coding of object identity that is invariant to visual or tactile presentation. We suggest calling this kind of analysis, when a classifier is trained on data from one cognitive domain and tested on data from another, Multivariate Cross-Classification (MVCC). A schematic of MVCC is presented in Figure 1. In this paper we discuss methodological issues relevant to MVCC and review recent work employing this technique in order to demonstrate its power in contributing to the understanding of abstract neural representations.

Bottom Line: MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching.Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands.We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

View Article: PubMed Central - PubMed

Affiliation: Brain and Creativity Institute, University of Southern California Los Angeles, CA, USA ; Department of Psychology, University of Southern California Los Angeles, CA, USA.

ABSTRACT
Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

No MeSH data available.