| Poster | Poster Session 09 Program Schedule
02/16/2024
03:30 pm - 04:45 pm
Room: Majestic Complex (Posters 61-120)
Poster Session 09: Epilepsy | Oncology | MS | Infectious Disease
Final Abstract #76
Autobiographical memory episodic specificity derived from structured interviews predicts the specificity and frequency of naturalistically observed, everyday autobiographical thought sharing
Katelyn McVeigh, University of Arizona, Tucson, United States
Austin Deffner, University of Arizona, Tucson, United States
Daniel Hernandez, University of Arizona, Tucson, United States
Matthias Mehl, University of Arizona, Tucson, United States
Jessica Andrews-Hanna, University of Arizona, Tucson, United States
Matthew Grilli, University of Arizona, Tucson, United States
Category: Other
Keyword 1: aging (normal)
Keyword 2: neuropsychological assessment
Keyword 3: ecological validity
Objective:
Autobiographical event memory narratives elicited from structured interviews are believed to provide insight into how memories are retrieved in daily life. Despite this assumption, to our knowledge, no study has tested whether structured interview episodic specificity predicts how elaborately or frequently autobiographical memories and other forms of autobiographical thoughts are shared in natural, daily interactions. To close this gap in knowledge, we conducted a study designed to not only objectively measure episodic specificity in structured interviews but also measure autobiographical thoughts in daily life in the same group of participants. We hypothesized that structured interviews are an ecologically valid measurement instrument, and therefore predict the way autobiographical thoughts are shared in natural contexts.
Participants and Methods:
Seventy healthy, cognitively unimpaired young and older adults (ages 18-80, M=54.0, SD=22.74) completed the structured Autobiographical Interview (AI) in their homes via videoconference. Their memory narratives were transcribed and scored using the established AI protocol to parse internal (episodic) and external (semantic/other) details. Participants then used the Electronically Activated Recorder (EAR) for 10 days to unobtrusively sample audio recordings of real-life, everyday instances of their autobiographical thought sharing. EAR recordings containing autobiographical thoughts (65% of all sound files containing speech) were scored using a modified version of the AI protocol to capture everyday autobiographical thought specificity. Using linear regression, we examined whether the proportion of internal details shared in the AI predicted the proportion of internal details shared in everyday autobiographical thoughts captured with the EAR, accounting for age, gender, and education. We conducted a similar analysis for everyday autobiographical thought frequency (total number of EAR recordings with autobiographical thought divided by total number of EAR recordings with speech), and we examined the relationship between CVLT-II Long Delay Free Recall (LDRF) performance and our autobiographical thought measures of interest.
Results:
The overall regression model examining EAR autobiographical thought specificity was statistically significant (R2=0.23, F(4,65)=9.21, p<0.001). The proportion of internal details shared in the AI significantly predicted the proportion of internal details shared in everyday autobiographical thought (β=0.19, p=0.001), while age (β=0.0002, p=0.42), gender (β=-0.02, p=0.18), and education (β=0.002, p=0.96) did not.
The overall regression model examining EAR autobiographical thought sharing frequency was statistically significant (R2=0.25, F(4,65)=5.37, p<0.001). Age (β=-0.004, p<0.01) and the proportion of internal details shared in the AI (β=-0.43, p=0.02) significantly predicted everyday autobiographical thought sharing frequency, while gender (β=0.004, p=0.92) and education (β=0.002, p=0.85) did not.
Performance on the CVLT LDFR was not associated with the proportion of internal details shared in the AI (β=-0.001, p=0.91), EAR autobiographical thought sharing frequency (β=0.008, p=0.40), or the proportion of internal details shared in EAR autobiographical thoughts (β=0.0004, p=0.90).
Conclusions:
To our knowledge, these analyses provide the first evidence that autobiographical memory episodic specificity, derived from structured interviews, is a significant predictor of natural autobiographical thought sharing in daily life. Future research is needed to identify additional sources of variance in naturalistic cognition, as structured interview episodic specificity was a modest predictor overall. Future work also is needed to further explore the relationship between structured neuropsychological functioning and aspects of everyday, autobiographical thought sharing.
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