Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions

Priya Bhatt1, Ajay K. Sethi1, Vaibhav Tasgaonkar1, Jugal Shroff1, Isha Pendharkar1, Aditya Desai1, Pratyush Sinha1, Aditya M. Deshpande1, Gargi Joshi1, Anil Rahate1, Priyanka Jain2, Rahee Walambe3, Ketan Kotecha3, Nidhi Jain2
1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
2Centre for Development of Advanced Computing (C-DAC), Delhi, India
3Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India

Tóm tắt

AbstractHuman behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in stressful circumstances. The ability to perceive, analyse, process, interpret, remember, and retrieve information while making judgments to respond correctly is referred to as Cognitive Behavior. After making a significant mark in emotion analysis, deception detection is one of the key areas to connect human behaviour, mainly in the forensic domain. Detection of lies, deception, malicious intent, abnormal behaviour, emotions, stress, etc., have significant roles in advanced stages of behavioral science. Artificial Intelligence and Machine learning (AI/ML) has helped a great deal in pattern recognition, data extraction and analysis, and interpretations. The goal of using AI and ML in behavioral sciences is to infer human behaviour, mainly for mental health or forensic investigations. The presented work provides an extensive review of the research on cognitive behaviour analysis. A parametric study is presented based on different physical characteristics, emotional behaviours, data collection sensing mechanisms, unimodal and multimodal datasets, modelling AI/ML methods, challenges, and future research directions.

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