Abstract:
:Reduced specificity of autobiographical memories is a hallmark of depressive cognition. Autobiographical memory (AM) specificity is typically measured by the Autobiographical Memory Test (AMT), in which respondents are asked to describe personal memories in response to emotional cue words. Due to this free descriptive responding format, the AMT relies on experts' hand scoring for subsequent statistical analyses. This manual coding potentially impedes research activities in big data analytics such as large epidemiological studies. Here, we propose computerized algorithms to automatically score AM specificity for the Dutch (adult participants) and English (youth participants) versions of the AMT by using natural language processing and machine learning techniques. The algorithms showed reliable performances in discriminating specific and nonspecific (e.g., overgeneralized) autobiographical memories in independent testing data sets (area under the receiver operating characteristic curve > .90). Furthermore, outcome values of the algorithms (i.e., decision values of support vector machines) showed a gradient across similar (e.g., specific and extended memories) and different (e.g., specific memory and semantic associates) categories of AMT responses, suggesting that, for both adults and youth, the algorithms well capture the extent to which a memory has features of specific memories. (PsycINFO Database Record
journal_name
Psychol Assessjournal_title
Psychological assessmentauthors
Takano K,Gutenbrunner C,Martens K,Salmon K,Raes Fdoi
10.1037/pas0000472subject
Has Abstractpub_date
2018-02-01 00:00:00pages
259-273issue
2eissn
1040-3590issn
1939-134Xpii
2017-14701-001journal_volume
30pub_type
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