Classification of general and personal semantic details in the Autobiographical Interview Journal Articles uri icon

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  • The Autobiographical Interview (AI) separates internal (episodic) and external (non-episodic) details from transcribed protocols using an exhaustive and reliable scoring system. While the details comprising the internal composite are centered on elements of episodic memory, external details are more heterogeneous as they are meant to capture a variety of non-episodic utterances: general semantics, different types of personal semantics details, metacognitive statements, repetitions, and details about off topic events. Elevated external details are consistently observed in aging and in neurodegenerative diseases. In the present study, we augmented the AI scoring system to differentiate subtypes of external details to test whether the elevation of these details in aging and in frontotemporal lobar degeneration (including mixed frontotemporal/semantic dementia [FTD/SD] and progressive non-fluent aphasia [PNFA]) would be specific to general and personal semantics or would concern all subtypes. Specifically, we separated general semantic details from personal semantic details (including autobiographical facts, self-knowledge, and repeated events). With aging, external detail elevation was observed for general and personal semantic details but not for other types of external details. In frontotemporal lobar degeneration, patients with FTD/SD (but not PNFA) generated an excess of personal semantic details but not general semantic details. The increase in personal but not general semantic details in FTD/SD is consistent with prevalent impairment of general semantic memory in SD, and with the personalization of concepts in this condition. Under standard AI instructions, external details were intended to capture off-topic utterances and were not intended as a direct measure of semantic abilities. Future investigations concerned with semantic processing in aging and in dementia could modify standard instructions of the AI to directly probe semantic content.


  • Renoult, Louis
  • Armson, Michael J
  • Diamond, Nicholas B
  • Fan, Carina L
  • Jeyakumar, Nivethika
  • Levesque, Laryssa
  • Oliva, Laura
  • McKinnon, Margaret
  • Papadopoulos, Alissa
  • Selarka, Dhawal
  • St Jacques, Peggy L
  • Levine, Brian

publication date

  • July 2020