1 Department of Neurology and Neurosurgery, Laboratory of Brain and Cognition, Montréal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada.
2 Concordia University, Montréal, QC, Canada.
3 Department of Psychology, McGill University, Montréal, QC, Canada.
4 Department of Psychology, York University, Toronto, ON, Canada.
5 Department of Neurology and Neurosurgery, Laboratory of Brain and Cognition, Montréal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada. nathan.spreng@gmail.com.
6 Department of Psychology, McGill University, Montréal, QC, Canada. nathan.spreng@gmail.com.
7 Department of Psychiatry, McGill University, Montréal, QC, Canada. nathan.spreng@gmail.com.
8 McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada. nathan.spreng@gmail.com.

Autobiographical memory (AM) involves a rich phenomenological re-experiencing of a spatio-temporal event from the past, which is challenging to objectively quantify. The Autobiographical Interview (AI; Levine et al. Psychology and Aging, 17(4), 677-689, 2002) is a manualized performance-based assessment designed to quantify episodic (internal) and semantic (external) features of recalled and verbally conveyed prior experiences. The AI has been widely adopted, yet has not undergone a comprehensive psychometric validation. We investigated the reliability, validity, association to individual differences measures, and factor structure in healthy younger and older adults (N = 352). Evidence for the AI's reliability was strong: the subjective scoring protocol showed high inter-rater reliability and previously identified age effects were replicated. Internal consistency across timepoints was robust, suggesting stability in recollection. Central to our validation, internal AI scores were positively correlated with standard, performance-based measures of episodic memory, demonstrating convergent validity. The two-factor structure for the AI was not well supported by confirmatory factor analysis. Adjusting internal and external detail scores for the number of words spoken (detail density) improved trait estimation of AM performance. Overall, the AI demonstrated sound psychometric properties for inquiry into the qualities of autobiographical remembering.