Studying Common Neural Representation of Objects Across Participants
نویسنده
چکیده
I describe a novel application of a generalization of principal component analysis to study commonality of neural representation of objects across participants. Introduction In previous work, the category of an object a participant was viewing was identified based only on other participants’ characteristic neural activation patterns. What is the degree of overlap in neural representation of categories across participants? In this work I propose the application of STATIS (which stands for Structuration des Tableaux À Trois Indices de la Statistique) to cognitive states data to study the overall consensus of neural representation of objects across participants. STATIS is a non-iterative exploratory data analysis method for comparison of multiple matrices. STATIS STATIS is a generalization of principal components analysis for multiple matrices. Each participant’s preprocessed data is represented in an objects by voxels matrix Xk, k = 1, . . . , p. Let Sk = XkX ′ k be a normalized cross-product matrix representing the similarity between objects for the kth participant. Participant cross-product matrices are aggregated into a compromise cross-product matrix, which represents the agreement in neural representation of objects across participants. The compromise matrix is a weighted average of individual cross-product matrices. Participants with neural representation of exemplars similar to other participants are assigned larger weights, and participants with neural representation of exemplars most different from others are assigned lower weights. Weights are derived from a p×p between participants cosine matrix C, where the kk′ entry is computed by an RV-coefficient (Escoufier, 1973): RV (Sk, Sk′) = tr(S ′ kSk′) √ tr(S kSk)tr(S ′ k′Sk′) (1) The first principal component of the cosine matrix is proportional to the mean of the elements of C. Hence the weights are given by the elements of the first eigenvector of C, rescaled to sum up to one. Formally, the compromise matrix S+ is:
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