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Multi-Label Multi-Task Deep Learning for behavioral coding

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2022年第1期13卷 508-518页

作者： Gibson, James Atkins, David C. Creed, Torrey A. Imel, Zac Georgiou, Panayiotis Narayanan, Shrikanth Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA Univ Washington Dept Psychiat & Behav Sci Seattle WA 98195 USA Univ Penn Perelman Sch Med Dept Psychiat Philadelphia PA 19104 USA Univ Utah Dept Educ Psychol Salt Lake City UT 84112 USA

We propose a methodology for estimating human behaviors in psychotherapy sessions using multi-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions are annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist-client interactions in psychotherapy sessions. We experimentally compare the proposed learning approaches for estimating behaviors of interest in these datasets. Specifically, we compare single and multiple label learning approaches, single and multiple task learning approaches, and evaluate the performance of these approaches when incorporating turn context. We demonstrate that the best multi-label, multi-task learning model with turn context achieves 18.9 and 19.5 percent absolute improvements with respect to a logistic regression classifier (for each behavioral coding task respectively) and 6.4 and 6.1 percent absolute improvements with respect to the best single-label, single-task deep neural network models. Lastly, we discuss the insights these modeling paradigms provide into these complex interactions including key commonalities and differences of behaviors within and between the two prevalent psychotherapy approaches-Motivational Interviewing and Cognitive behavioral Therapy-considered.

关键词： behavioral coding behavioral signal processing multi-label learning multi-task learning

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Automated behavioral coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial

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JOURNAL OF MEDICAL INTERNET RESEARCH 2024年 26卷 e53562页

作者： Pellemans, Mathijs Salmi, Salim Merelle, Saskia Janssen, Wilco van der Mei, Rob Vrije Univ Amsterdam Dept Math De Boelelaan 1111 NL-1081 HV Amsterdam Netherlands 113 Suicide Prevent Amsterdam Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands Amsterdam UMC Dept Psychiat Amsterdam Netherlands

Background: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explorethe feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinicalpractice. Methods: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machinelearning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior(accuracy=0.72, area under the curve [AUC]=0.95, Cohen kappa=0.69). It differentiated MI congruent and incongruent counselorbehavior (AUC=0.92, kappa=0.65) and evocative and nonevocative language (AUC=0.92, kappa=0.66). For client behavior, the modelachieved an accuracy of 0.70 (AUC=0.89, kappa=0.55). The model's interpretable predictions discerned client change talk and sustaintalk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set siz

关键词： motivational interviewing behavioral coding suicide prevention artificial intelligence effectiveness counseling support tool online help mental health

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Multiple Instance Learning for behavioral coding

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2017年第1期8卷 81-94页

作者： Gibson, James Katsamanis, Athanasios Romero, Francisco Xiao, Bo Georgiou, Panayiotis Narayanan, Shrikanth Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

We propose a computational methodology for automatically estimating human behavioral patterns using the multiple instance learning (MIL) paradigm. We describe the incremental diverse density algorithm, a particular formulation of multiple instance learning, and discuss its suitability for behavioral coding. We use a rich multi-modal corpus comprised of chronically distressed married couples having problem-solving discussions as a case study to experimentally evaluate our approach. In the multiple instance learning framework, we treat each discussion as a collection of short-term behavioral expressions which are manifested in the acoustic, lexical, and visual channels. We experimentally demonstrate that this approach successfully learns representations that carry relevant information about the behavioral coding task. Furthermore, we employ this methodology to gain novel insights into human behavioral data, such as the local versus global nature of behavioral constructs as well as the level of ambiguity in the expression of behaviors through each respective modality. Finally, we assess the success of each modality for behavioral classification and compare schemes for multimodal fusion within the proposed framework.

关键词： behavioral coding behavioral signal processing couple therapy multi-modal signal processing multiple instance learning

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behavioral coding of Therapist Language in Addiction Counseling using Recurrent Neural Networks 17

Behavioral Coding of Therapist Language in Addiction Counsel...

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17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Xiao, Bo Can, Dogan Gibson, James Imel, Zac E. Atkins, David C. Georgiou, Panayiotis Narayanan, Shrikanth Univ Calif Los Angeles Signal Anal & Interpretat Lab Los Angeles CA USA Univ Utah Dept Educ Psychol Salt Lake City UT 84112 USA Univ Washington Dept Psychiat & Behav Sci Seattle WA 98195 USA

ISBN: (纸本)9781510833135

Manual annotation of human behaviors with domain specific codes is a primary method of research and treatment fidelity evaluation in psychotherapy. However, manual annotation has a prohibitively high cost and does not scale to coding large amounts of psychotherapy session data. In this paper, we present a case study of modeling therapist language in addiction counseling, and propose an automatic coding approach. The task objective is to code therapist utterances with domain specific codes. We employ Recurrent Neural Networks (RNNs) to predict these behavioral codes based on session transcripts. Experiments show that RNNs outperform the baseline method using Maximum Entropy models. The model with bi-directional Gated Recurrent Units and domain specific word embeddings achieved the highest overall accuracy. We also briefly discuss about client code prediction and comparison to previous work.

关键词： Recurrent neural network behavioral coding motivational interviewing language modeling

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Developing and Modifying behavioral coding Schemes in Pediatric Psychology: A Practical Guide

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JOURNAL OF PEDIATRIC PSYCHOLOGY 2015年第1期40卷 154-164页

作者： Chorney, Jill MacLaren McMurtry, C. Meghan Chambers, Christine T. Bakeman, Roger IWK Hlth Ctr Dept Pediat Anesthesia Halifax NS B3K 6R8 Canada IWK Hlth Ctr Ctr Pediat Pain Res Halifax NS B3K 6R8 Canada Dalhousie Univ Dept Anesthesia Halifax NS B3H 3J5 Canada Dalhousie Univ Dept Pain Management & Perioperat Med Halifax NS B3H 3J5 Canada Dalhousie Univ Dept Psychol & Neurosci Halifax NS B3H 3J5 Canada Dalhousie Univ Dept Surg Halifax NS B3H 3J5 Canada Univ Guelph Childrens Hlth Res Inst Dept Psychol Guelph ON N1G 2W1 Canada Dalhousie Univ Dept Pediat Halifax NS B3H 3J5 Canada Georgia State Univ Dept Psychol Atlanta GA 30303 USA

Objectives To provide a concise and practical guide to the development, modification, and use of behavioral coding schemes for observational data in pediatric psychology. Methods This article provides a review of relevant literature and experience in developing and refining behavioral coding schemes. Results A step-by-step guide to developing and/or modifying behavioral coding schemes is provided. Major steps include refining a research question, developing or refining the coding manual, piloting and refining the coding manual, and implementing the coding scheme. Major tasks within each step are discussed, and pediatric psychology examples are provided throughout. Conclusions behavioral coding can be a complex and time-intensive process, but the approach is invaluable in allowing researchers to address clinically relevant research questions in ways that would not otherwise be possible.

关键词： behavioral coding observational data research methods

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A Dialog Act Tagging Approach to behavioral coding: A Case Study of Addiction Counseling Conversations 16

A Dialog Act Tagging Approach to Behavioral Coding: A Case S...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Can, Dogan Atkins, David C. Narayanan, Shrikanth S. Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA Univ Washington Dept Psychiat & Behav Sci Seattle WA 98195 USA

ISBN: (纸本)9781510817906

Motivational Interviewing (MI) is a goal-oriented psychotherapy, employed in cases such as addiction, that helps clients explore and resolve their ambivalence about the problem at hand in a dialog setting. MI session quality is typically assessed with behavioral coding a time consuming and labor intensive manual annotation system. This paper examines a computational approach to modeling and assessing the quality of MI sessions. Specifically, we pose the utterance level behavioral coding task as a sequence tagging problem and use linear chain CRF models trained on coded session transcripts and Switchboard DAMSL dataset to predict utterance level behavioral codes as well as dialog acts. We then use those utterance level predictions to predict session level behavioral codes of clinical interest characterizing the quality and efficacy of psychotherapy. We experiment with different feature parameterizations and reduced code sets and present an analysis of how standard dialog acts relate to behavioral codes.

关键词： dialog act tagging behavioral coding motivational interviewing skills code

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Feature-based behavior coding for efficient exploratory analysis using pose estimation

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BEHAVIOR RESEARCH METHODS 2025年第6期57卷 1-14页

作者： Nishimura, Eigo Kyushu Univ Dept Human Life Design & Sci 4-9-1 ShiobaruMinami Ku Fukuoka 8158540 Japan

This paper introduces feature-based behavior coding (FBBC), an efficient method for exploratory analysis in behavioral research using pose estimation techniques. FBBC addresses the challenges of traditional behavioral coding methods, particularly in the exploratory stages of research when coding schemes are not yet well defined. By leveraging keypoint detection and dimensionality reduction, FBBC transforms video data into interpretable feature time series, enabling researchers to analyze diverse postural patterns more efficiently. Also presented is Behavior Senpai, an open-source software implementation of FBBC that integrates automated feature extraction with human insight. A case study demonstrates FBBC's ability to classify complex postures by combining multiple features and manual clustering. While the current iteration focuses on instantaneous posture classification, the framework shows potential for expansion to action classification. FBBC offers increased flexibility in developing coding schemes and reduces the time-consuming nature of repetitive observations. This approach represents a considerable advancement in behavioral research, bridging traditional methods with modern machine-learning techniques. As FBBC is adopted and refined, it will contribute to more comprehensive and insightful behavioral analyses across the psychological and behavioral sciences.

关键词： Open-source software behavioral coding Video analysis Keypoint detection Observational methods

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Quantifying tics: Best practices and design considerations for video-based tic coding in research

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BEHAVIOR RESEARCH METHODS 2024年第4期56卷 4073-4084页

作者： DuBois, Megan Houlihan, Kerry Raab, Brittany Pryor, Alison Kellman, Mia Brinker, Mayella Wellen, Brianna Capriotti, Matthew Conelea, Christine Univ Minnesota Dept Psychiat & Behav Sci Minneapolis MN 55455 USA Univ Minnesota Masonic Inst Developing Brain Minneapolis MN 55414 USA San Jose State Univ Psychol Dept San Jose CA 95192 USA

Tic disorders (TD), including Tourette Syndrome, are characterized by involuntary, repetitive movements and/or vocalizations that can lead to persistent disability and impairment across the lifespan. Existing research demonstrates that video-based behavioral coding (VBBC) methods can be used to reliably quantify tics, enabling a more objective approach to tic measurement above and beyond standardly used TD questionnaires. VBBC is becoming more popular given the ease and ubiquity of obtaining patient videos. However, rigor and reproducibility of this work has been limited by undescribed and unstandardized approaches to using VBBC methods in TD research. The current paper describes "best practices" for VBBC in TD research, which have been tested and refined in our research over the past 15+ years, including considerations for data acquisition, coding implementation, interrater reliability demonstration, and methods reporting. We also address ethical considerations for researchers using this method.

关键词： Tourette Syndrome Tics Tic disorder Video behavioral coding Quantification

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Conceptual Considerations and Methodological Challenges to Measuring Food Acceptance During Infancy

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CURRENT NUTRITION REPORTS 2023年第3期12卷 407-415页

作者： Moding, Kameron J. Kielb, Elizabeth I. Purdue Univ Dept Human Dev & Family Sci 1200 Mitch Daniels Blvd W Lafayette IN 47907 USA

Purpose of ReviewThe purpose of this review is to (1) present a conceptual definition of infant food acceptance, (2) explore methodological challenges to measuring different components of food acceptance, and (3) provide recommendations for future research on food acceptance during *** FindingsInfant food acceptance includes separate but overlapping components: wanting, liking, and consumption. Recent evidence suggests that although these components are often correlated, there is variability in the sensitivity of these measures to detect differences in infants' responses to target *** date, consumption and caregiver perceptions of liking have been the primary outcome measures of interest in studies of infant food acceptance. More diverse and precise measures across the three components of infant food acceptance are needed to address a wider scope of research questions, which would in turn strengthen the interpretability and translational potential of research in this area.

关键词： Infants Complementary feeding Food acceptance behavioral coding Measurement

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Extending ReRun, Annotating Interactions in VR 24

Extending ReRun, Annotating Interactions in VR

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19th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Chen, Leonard Goedicke, David Ju, Wendy Leland High Sch San Jose CA 95120 USA Cornell Tech New York NY USA

ISBN: (纸本)9798400703232

In the rapidly evolving field of autonomous vehicle (AV) technology, virtual reality (VR) simulations have proven to be an effective method in interaction research. This paper addresses the significant gap in existing tools for annotating and analyzing behaviors in 3D VR environments. We propose a novel 3D behavioral coding tool, extending the capability of the ReRun system, that enables precise marking and categorization of interactions within VR driving simulations. We demonstrate how this tool enhances simulation in AV research, providing an efficient method for integrating both qualitative and quantitative analysis of complex interactions-crucial in improving safety of autonomous systems.

关键词： behavioral coding annotation driving simulation HRI virtual reality automotive interaction studies multi-perspective post-analysis

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