STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling

Abdi, Hervé; Williams, Lynne J.; Valentin, Domininique; Bennani-Dosse, Mohammed

doi:10.1002/wics.198

Cited by 124 publications

(135 citation statements)

References 94 publications

(270 reference statements)

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“…Distance matrices were created by computing the Euclidean distance between pairs of rows from the 11 × 11 matrix, so that pairs of retrieved videos were compared along mean discriminant scores from the 11 prediction categories. These distance matrices were projected onto factor maps based on a three-way generalization of classical multidimensional scaling analysis called DISTATIS (Abdi, Williams, Valentin, & Bennani-Dosse, 2012;Abdi, Dunlop, & Williams, 2009;Abdi, 2007). In DISTATIS, multiple distance matrices are optimally integrated into a common distance matrix that is then analyzed with multidimensional scaling, so that distance matrices derived from multiple individuals can be combined.…”

Section: Vividness Distatis Analysismentioning

confidence: 99%

“…Videos whose discriminant scores tended to vary together were closer on the factor map than videos whose discriminant scores did not covary with one another. A bootstrap-based procedure (Abdi et al, 2009(Abdi et al, , 2012 was used to compute 95% confidence ellipsoid intervals around each video. When confidence ellipsoids did not intersect between pairs of videos, the two videos were considered significantly different at p < .05.…”

Section: Vividness Distatis Analysismentioning

confidence: 99%

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Distributed Patterns of Reactivation Predict Vividness of Recollection

St-Laurent

Abdi

Buchsbaum

2015

Journal of Cognitive Neuroscience

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Abstract■ According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection. ■

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Section: Vividness Distatis Analysismentioning

confidence: 99%

Section: Vividness Distatis Analysismentioning

confidence: 99%

Distributed Patterns of Reactivation Predict Vividness of Recollection

St-Laurent

Abdi

Buchsbaum

2015

Journal of Cognitive Neuroscience

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“…For visualization of the representational structure within the cluster-defined ROIs, we used STATIS (Structuration des Tableaux À Trois Indices de la Statistique; Abdi et al, 2012). STATIS is a method for combining data tables from multiple subjects to provide a group MDS solution using principal components analysis (PCA; Kruskal and Wish, 1978;Abdi and Williams, 2010;Abdi et al, 2012). Each participant contributes a data matrix with dimensions N ϫ M, where N is the number of stimulus classes (in our case 12) and M is the number of voxels.…”

Section: Four-step Analysis Proceduresmentioning

confidence: 99%

How the Human Brain Represents Perceived Dangerousness or “Predacity” of Animals

et al. 2016

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Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity or "animacy;" (2) dangerousness or "predacity;" and (3) size. We investigated the neural basis of the perceived dangerousness or aggressiveness of animals, which we refer to more generally as "perception of threat." Using functional magnetic resonance imaging (fMRI), we analyzed neural activity evoked by viewing images of animal categories that spanned the dissociable semantic dimensions of threat and taxonomic class. The results reveal a distributed network for perception of threat extending along the right superior temporal sulcus. We compared neural representational spaces with target representational spaces based on behavioral judgments and a computational model of early vision and found a processing pathway in which perceived threat emerges as a dominant dimension: whereas visual features predominate in early visual cortex and taxonomy in lateral occipital and ventral temporal cortices, these dimensions fall away progressively from posterior to anterior temporal cortices, leaving threat as the dominant explanatory variable. Our results suggest that the perception of threat in the human brain is associated with neural structures that underlie perception and cognition of social actions and intentions, suggesting a broader role for these regions than has been thought previously, one that includes the perception of potential threat from agents independent of their biological class.

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“…We also computed a "compromise" BADA solution for the perception, memory, and posttraining perception. In this compromise analysis, each data set was first subjected to a singular value decomposition and normalized by dividing each datum in this set by the first singular value its data set (see Abdi, Williams, Valentin, & Bennani-Dosse, 2012, for a justification of this normalization). Then the three data sets were concatenated into a single matrix by averaging them, and a BADA solution was applied to the full data set .…”

Section: Bada: Group Analysismentioning

confidence: 99%

The Neural Basis of Vivid Memory Is Patterned on Perception

Buchsbaum

Lemire-Rodger

Fang

et al. 2012

Journal of Cognitive Neuroscience

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Abstract■ When we have a rich and vivid memory for a past experience, it often feels like we are transported back in time to witness once again this event. Indeed, a perfect memory would exactly mimic the experiential quality of direct sensory perception. We used fMRI and multivoxel pattern analysis to map and quantify the similarity between patterns of activation evoked by direct perception of a diverse set of short video clips and the vivid remembering, with closed eyes, of these clips. We found that the patterns of distributed brain activation during vivid memory mimicked the patterns evoked during sensory perception. Using whole-brain patterns of activation evoked by perception of the videos, we were able to accurately classify brain patterns that were elicited when participants tried to vividly recall those same videos. A discriminant analysis of the activation patterns associated with each video revealed a high degree (explaining over 80% of the variance) of shared representational similarity between perception and memory. These results show that complex, multifeatured memory involves a partial reinstatement of the whole pattern of brain activity that is evoked during initial perception of the stimulus. ■

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STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling

Abstract: International audienc

Cited by 124 publications

References 94 publications

Distributed Patterns of Reactivation Predict Vividness of Recollection

Distributed Patterns of Reactivation Predict Vividness of Recollection

How the Human Brain Represents Perceived Dangerousness or “Predacity” of Animals

The Neural Basis of Vivid Memory Is Patterned on Perception

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