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Understanding autobiographical memory content using computational text analysis.

Ryan C Yeung1, Marek Stastna2, Myra A Fernandes1

  • 1Department of Psychology, University of Waterloo, Waterloo, Canada.

Memory (Hove, England)
|August 10, 2022
PubMed
Summary
This summary is machine-generated.

New computational methods analyze autobiographical memory (AM) content at scale. These tools reveal patterns in involuntary autobiographical memories (IAMs), offering insights into memory content and function.

Keywords:
Autobiographical memorycontent analysisinvoluntary autobiographical memorynatural language processingtext as data

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Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Neuroscience

Background:

  • Autobiographical memory (AM) research is expanding, yet content analysis methods are limited.
  • Manual analysis of AM content is time-consuming and labor-intensive, hindering large-scale studies.
  • Analyzing AM content is crucial for understanding its nature and functions, particularly for involuntary autobiographical memories (IAMs).

Purpose of the Study:

  • To develop and present novel computational methods for analyzing large-scale AM content.
  • To overcome the limitations of manual content analysis in AM research.
  • To investigate the content of recurrent involuntary autobiographical memories (IAMs) and their characteristics.

Main Methods:

  • Utilized frequency analyses to identify common and distinctive words in a large corpus of IAM descriptions.
  • Employed structural topic modeling to uncover coherent themes within recurrent IAMs.
  • Analyzed text descriptions from 6,187 undergraduates regarding their recurrent IAMs.

Main Results:

  • Identified frequently occurring and distinctive words within recurrent IAMs, such as 'argument' for negative memories.
  • Discovered coherent topics like 'Negative past relationships' and 'Experiences with family members'.
  • Found significant differences in topic prevalence based on the valence (positive/negative) of IAMs.

Conclusions:

  • Computational methods enable large-scale, granular, and reproducible analysis of AM content.
  • These methods provide new avenues for research into the nature and function of autobiographical memory.
  • The study demonstrates the potential for computational approaches to advance the scope and scale of AM research.