Searching through memory is mediated by complex interactions between the underlying mental lexicon and the processes that operate on this lexicon. However, these interactions are difficult to study due to the effortless manner in which neurotypical individuals perform cognitive tasks. In this work, we examine these interactions within a sample of prelingually deaf individuals with cochlear implants and normal hearing individuals who were administered the verbal fluency task for the "animals" category. Specifically, we tested how different candidates for underlying mental lexicons and processes account for search behavior within the verbal fluency task across the two groups. The models learned semantic representations from different combinations of textual (word2vec) and speech-based (speech2vec) information. The representations were then combined with process models of memory search based on optimal foraging theory that incorporate different lexical sources for transitions within and between clusters of items produced in the fluency task. Our findings show that semantic, word frequency, and phonological information jointly influence search behavior and highlight the delicate balance of different lexical sources that produces successful search outcomes.
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Automated Construction of Lexicons to Improve Depression Screening with Text Messages
Given that depression is one of the most prevalent mental illnesses, developing effective and unobtrusive diagnosis tools is of great importance. Recent work that screens for depression with text messages leverage models relying on lexical category features. Given the colloquial nature of text messages, the performance of these models may be limited by formal lexicons. We thus propose a strategy to automatically construct alternative lexicons that contain more relevant and colloquial terms. Specifically, we generate 36 lexicons from fiction, forum, and news corpuses. These lexicons are then used to extract lexical category features from the text messages. We utilize machine learning models to compare the depression screening capabilities of these lexical category features. Out of our 36 constructed lexicons, 14 achieved statistically significantly higher average F1 scores over the pre-existing formal lexicon and basic bag-of-words approach. In comparison to the pre-existing lexicon, our best performing lexicon increased the average F1 scores by 10%. We thus confirm our hypothesis that less formal lexicons can improve the performance of classification models that screen for depression with text messages. By providing our automatically constructed lexicons, we aid future machine learning research that leverages less formal text.
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- Award ID(s):
- 1852498
- NSF-PAR ID:
- 10399241
- Date Published:
- Journal Name:
- IEEE Journal of Biomedical and Health Informatics
- ISSN:
- 2168-2194
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JBHI.2022.3203345
