Estimating person-specific neural correlates of mental rotation: A machine learning approach.

ABSTRACT: Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space. It has been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, electroencephalographic (EEG) activity of

SUBMITTER: Uslu S 

PROVIDER: S-EPMC10830051 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature