Statistical Learning and Machine Learning: Advances in Neurobiological and Computational Approaches

Dear Colleagues,

Statistical learning refers to a fundamental cognitive process that allows individuals to learn regularities, patterns, and structures based on the statistical properties of the sensory input and make predictions about future events. This learning process is pervasive and operates across various domains, such as language, vision, motor skills, and social interactions. In the past three decades, statistical learning has become a prominent research topic because of its foundational nature, developmental importance, relevance to language acquisition, multidisciplinary appeal, and potential applications. Machine learning is an increasingly popular subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn from data and make predictions or decisions, including identifying various disease and clinical condition biomarkers. While machine learning and statistical learning have many similarities, it is worth noting that machine learning is a broader field that encompasses various algorithms and techniques involved in data analysis and pattern recognition. Some machine learning approaches, such as deep learning, utilize complex neural network architecture to automatically learn features and representations from data, thus going beyond traditional statistical learning methods. However, at their core, both machine learning and statistical learning aim to leverage data to uncover patterns and make predictions. As the field progresses, the interplay between statistical learning principles and machine learning algorithms will shape the development of computational models and intelligent systems that learn and adapt from data and simulate and transcend human information processing and decision making. Computational modeling approaches have inspired the creation of mathematical or logical representations of real-world systems and processes. Computational models are often used to simulate and analyze the behavior of a system, including, the evolution of the human speech sound system, under different conditions or test hypotheses. These models are explicitly designed to capture the underlying mechanisms or processes of the studied system, transforming various research fields.

This Special Issue focuses on the intersection between human cognition and artificial intelligence algorithms, drawing parallels and identifying differences between the processes. Potential areas covered by this Special Issue include, but are not limited to, the following subjects:

• Human learning processes and computational modelling;
• Neural mechanisms underlying linguistic, social, and affective statistical learning;
• Affective processing and statistical learning;
• Emotion recognition using AI;
• Socially intelligent AI;
• Trust and empathy in AI–human interactions;
• Machine learning algorithms and their inspirations from human cognition;
• Transfer learning between humans and AI;
• Real-world applications;
• Theoretical constructs and methodological issues;
• Ethical considerations and societal implications;
• Challenges and future research directions.

Researchers from fields such as linguistics, psychology, neuroscience, computer science, artificial intelligence, engineering, cognitive science, medical and health sciences, and related disciplines are invited to contribute to this Special Issue. Overall, this Special Issue represents an exciting opportunity to bridge the gap between human learning and artificial intelligence, as well as explore their synergies and implications for the future, thus promoting research that leads to improved theoretical insights, methodological improvement, more socially intelligent AI systems, improved human–machine interactions, and better understanding of the human mind’s behavior in social contexts.

Prof. Dr. Yang Zhang
Prof. Dr. Fei Chen
Prof. Dr. Chunbo Li
Guest Editors

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• statistical learning
• machine learning
• artificial intelligence
• language
• cognition
• emotion
• social processes
• real-world applications