The computerized scoring algorithm for the autobiographical memory test: updates and extensions for analyzing memories of English-speaking adults

ABSTRACT

The Autobiographical Memory Test (AMT) has been central in psychopathological studies of memory dysfunctions, as reduced memory specificity or overgeneralised autobiographical memory has been recognised as a hallmark vulnerability for depression. In the AMT, participants are asked to generate specific memories in response to emotional cue words, and their responses are scored by human experts. Because the manual coding takes some time, particularly when analysing a large dataset, recent studies have proposed computerised scoring algorithms. These algorithms have been shown to reliably discriminate between specific and non-specific memories of English-speaking children and Dutch- and Japanese-speaking adults. The key limitation is that the algorithm is not developed for English-speaking adult memories, which may cover a wider range of vocabulary that the existing algorithm for English-speaking child memories cannot process correctly. In the present study, we trained a new support vector machine to score memories of English-speaking adults. In a performance test (predicting memory specificity against human expert coding), the adult-memory algorithm outperformed the child-memory variant. In another independent performance test, the adult-memory algorithm showed robust performances to score memories that were generated in response to a different set of cues. These results suggest that the adult-memory algorithm reliably scores memory specificity.

KEYWORDS:

• Autobiographical memory
• computer scoring
• machine learning
• overgeneralised autobiographical memory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In some studies, future events are labelled as semantic associates. This difference does not influence the SVM training as it bases on binary (specific vs. non-specific) classification.

2 The model trained here is available via Open Science Framework: https://osf.io/ryshb/?view_only=1dc1501f2d1a4684822ea14ada15d995; A full list of the tokens and POS tags that were used in the algorithm can also be found in this page.

Additional information

Funding

Keisuke Takano was supported by Humboldt Research Fellowship for Postdoctoral Researchers of the Alexander von Humboldt foundation. Filip Raes was supported by the KU Leuven Research Council grant PF/10/005.