Interpreting neural decoding models using grouped model reliance

• Published in: PLoS computational biology Vol. 16; no. 1; p. e1007148
• Main Authors: Valentin, Simon, Harkotte, Maximilian, Popov, Tzvetan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2020; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Biology and Life Sciences; Brain - physiology; Brain research; Cognitive ability; Cognitive Neuroscience - methods; Computational Biology - methods; Computer and Information Sciences; Decision Trees; Digits; EEG; Electroencephalography; Electroencephalography - classification; Female; Frequencies; Humans; Learning algorithms; Machine Learning; Male; Medicine and Health Sciences; Memory; Memory tasks; Memory, Short-Term - physiology; Mental task performance; Methods; Models, Neurological; Nervous system; Neural coding; Neurosciences; Permutations; Physical Sciences; Psychology; Research and Analysis Methods; Researchers; Retention; Short term memory; Support Vector Machine; Support vector machines; Task Performance and Analysis; Variables; Young Adult; Germany

Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, there is a need for methods that allow to interpret...