{'fraction_non_alphanumeric': 0.018354978354978357, 'fraction_numerical': 0.002077922077922078, 'mean_word_length': 5.716279069767442, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quantitative Observational Practice in Family Studies : The case of reactivity

Assessment and interventions based on direct observations of human social, communicative and affective behavior are central to many realms including mental and behavioral healthcare and management. Take for example, the domain of distressed couple and family interactions, our focus application. The analyses that inform diagnosis and treatment here rely on audio-visual observations not just to transcribe who is saying and doing what but to infer meta behavior and complex interaction patterns. However, “in situ” human observation alone is often not able to get at all the requisite details, hence demanding manual behavioral coding using the audio-video recordings of interactions . The goal of this proposal is to transform the current practice of observational behavior analysis methodologies by enabling a computational framework for the analysis and modeling of emotionally-rich human interactions through signal processing and machine learning technologies. Using two large existing audio visual corpora of real couple interactions with rich psychologist-specified annotations (from 134 couples and 130 families), we propose to design and test algorithms and tools for automated behavioral coding. The proposed system can not only alleviate the tedium of manual annotation but can offer new analysis capabilities that further empower the mental health researcher and practitioner. To exemplify the proposed quantitative analysis of complex behavior, we focus on the study of reactivity during dyadic marital and triadic family conflict. Almost all behavioral family theories assume that a defining characteristic of well functioning families is an ability to successfully manage conflict. Distressed couples and families are characterized by runaway, destructive behavioral cycles and the ability to disengage from these is fundamental to functioning. This area of research is in its relative infancy and much remains to be learned about the way that multiple channels of behavioral expression contribute to these processes and abilities. Reactivity, defined as reciprocal, maladaptive behavior associated with dysregulated emotional states, is one of the least understood behavioral cycles. It is virtually impossible for a human observer to recognize it at the beginning of the process but early recognition is known to be critical to interrupting and altering reactivity. An added layer of complexity comes from the wide range of other behaviors such as reciprocity, demand/withdrawal, and emotion contagion that are related to reactivity. These human behaviors and interaction dynamics are manifested in multiple channels (e.g., voice, language, posture, face and body expressions), which are mutually coupled and interact in complex ways. We propose to develop computational techniques to process and model these cues, obtained from audio visual recordings, to automatically model psychologist-specified behavior codes. We believe that the proposed techniques will lead to transformative partnerships between mental health experts and technology through enhancing information gathering and processing, and by empowering (and not replacing) the human expert in their informed and timely decision making. Our team brings significant collective expertise in signal processing and psychology as well as resources such as multiple audio-video recordings of 134 married couples during real problem-solving dyadic interactions, collected as part of a longitudinal study at UCLA and UW and 130 families (mom, dad, and child) during real problem-solving triadic interactions collected as part of a longitudinal study at USC. The proposal’s intellectual merit lies in our novel behavioral signal processing techniques for robust sensing of the human state and in transforming the practice of observational behavioral analysis in psychology through these technologies. The state of the art in behavioral computing is predominantly based on recognizing a few canonical behavior categories from acted interactions. In contrast to the prevalent unimodal perspective, the current work focuses on quantitatively integrating information residing in multiple modalities to yield descriptions that range from global categories (e.g., presence or absence of blame) to time continuous behavioral abstractions (e.g., arousal trajectories). It will contribute novel algorithms and models for recognizing and modeling communicative and affective interaction dynamics elicited in realistic settings of couples and family therapy. The computational challenges are multifold, from automated perception of emotionally rich behaviors and cognition through models for domain-specific interpretation of the sensed information, to action through combining the knowledge and expertise of humans with the information processing abilities of the machine. The proposed advances will signal new directions in emotion and socio behavioral computing. The research can have a broad impact on a wide range of applications centered on observations of the human state and interactions, e.g., mental health applications, business customer services, negotiation tactics, law enforcement (e.g., interviews) etc.. The effort will result in open source algorithms released to the community thereby increasing impact and efforts in this field. It will result in cross-disciplinary training of both undergraduate and graduate students, including through a new graduate course. The expected outcomes include benefits to psychology through novel information augmentation, in technology through improved intelligent and robust human behavior computing, and in observational practice through the introduction of transformational tools.