Computerized Scoring Algorithms for the Autobiographical Memory Test

doi:10.1037/pas0000472

Computerized Scoring Algorithms for the Autobiographical Memory Test

2018-02-01

21

PRE

AI

K

Keisuke Takano *

C

Charlotte Gutenbrunner

DOI：10.1037/pas0000472

原文链接

原文求助

摘要

En 中文

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.

Keyword:

autobiographical memory

natural language processing

machine learning

overgeneralized autobiographical memory

Computerized Scoring Algorithms for the Autobiographical Memory Test

摘要

AI总结

期刊

机构

引用论文