Skip navigation
Please use this identifier to cite or link to this item:
Title: Applying Full Correlation Matrix Analysis to Memory Data
Authors: Reid, Malcolm
Advisors: Norman, Ken
Contributors: Botvinick, Matthew
Department: Psychology
Class Year: 2014
Abstract: Full correlation matrix analysis (FCMA) is a novel technique developed by Turk-­Browne, Wang, and Singer for studying functional connectivity. It allows us to train machine learning classifiers on correlations rather than voxel intensities. In doing so, it may shed insight into communication and neural mechanisms. This thesis uses FCMA on memory data collected in the Norman lab to see if a better classifier could be trained on correlations than on intensity. The study searches through the parameter space for the optimal configuration for FCMA but fails to find a classifier with above chance accuracy using FCMA. However, the study does succeed in finding accurate classifiers using conventional intensity-­based analysis. The study goes on to speculate what this means in terms of FCMA and memory.
Extent: 50 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Psychology, 1930-2017

Files in This Item:
File SizeFormat 
Reid_Malcolm.pdf1.93 MBAdobe PDF    Request a copy

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.