Machine Learning for Supplementing Behavioral Assessment

Bailey, Jordan D.; Baker, Jonathan C.; Rzeszutek, Mark J.; Lanovaz, Marc J.

doi:10.1007/s40614-020-00273-9

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…Data on injury reports may not directly correspond with more dynamic and objective measures of problem behavior severity. Recent advancements in wearable biosensor technology (e.g., Goodwin et al, 2018Goodwin et al, , 2019 and machine-learning algorithms (e.g., Bailey et al, 2021) have the potential to expand research on problem behavior severity (see Rooker et al, 2018) and improve the sophistication of measurement of this important variable.…”

Section: Functional Analysis Safetymentioning

confidence: 99%

Functional analysis of problem behavior: A 40‐year review

Melanson

Fahmie

2023

J of App Behav Analysis

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Extensive reviews of functional analysis literature were conducted 10 (Beavers et al., 2013) and 20 (Hanley et al., 2003) years ago; we expanded this review to capture the vast and innovative functional analysis research that has occurred over the past decade. Our review produced 1,333 functional analysis outcomes from 326 studies on the functional analysis of problem behavior between June 2012 and May 2022. Some characteristics of functional analysis studies were similar across the current and previous two reviews (e.g., child participants, developmental disability diagnosis, use of line graphs depicting session means, differentiated response outcomes). Other characteristics deviated from the previous two reviews (e.g., increase in autistic representation, outpatient settings, use of supplementary assessments, the inclusion of tangible conditions, and multiple function outcomes; decrease in session durations). We update previously reported participant and methodological characteristics, summarize outcomes, comment on recent trends, and propose future directions in the functional analysis literature.

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Section: Functional Analysis Safetymentioning

confidence: 99%

Functional analysis of problem behavior: A 40‐year review

Melanson

Fahmie

2023

J of App Behav Analysis

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“…Overall, the lexicon-based approach will be more practical, adequate, and insight generating for most behavior analysts than the learning-based approach. We therefore focus exclusively on lexicon-based approaches in this discussion, referring interested readers to more comprehensive works for an introduction to machine learning-based strategies (e.g., Liu, 2020) and their recent application to behavior analysis (Bailey et al, 2021;Lanovaz et al, 2020;Lanovaz & Hranchuk, 2021;Taylor & Lanovaz, 2021;Turgeon & Lanovaz, 2020).…”

Section: Sentiment Analysismentioning

confidence: 99%

Lexicon-Based Sentiment Analysis in Behavioral Research

Cero,

Luo,

Falligant

2024

Perspect Behav Sci

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A complete science of human behavior requires a comprehensive account of the verbal behavior those humans exhibit. Existing behavioral theories of such verbal behavior have produced compelling insight into language's underlying function, but the expansive program of research those theories deserve has unfortunately been slow to develop. We argue that the status quo's manually implemented and study-specific coding systems are too resource intensive to be worthwhile for most behavior analysts. These high input costs in turn discourage research on verbal behavior overall. We propose lexicon-based sentiment analysis as a more modern and efficient approach to the study of human verbal products, especially naturally-occurring ones (e.g., psychotherapy transcripts, social media posts). In the present discussion, we introduce the reader to principles of sentiment analysis, highlighting its usefulness as a behavior analytic tool for the study of verbal behavior. We conclude with an outline of approaches for handling some of the more complex forms of speech, like negation, sarcasm, and speculation. The appendix also provides a worked example of how sentiment analysis could be applied to existing questions in behavior analysis, complete with code that readers can incorporate into their own work.

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