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People learn from experience, but with considerable individual differences in the degree and type of behavioral adjustments resulting from a given experience. Error driven learning rules provide an elegant framework for ...

Multimodal data integration is increasingly central to neuroscience and perception science, where heterogeneous signals such as behavioral responses, sensory inputs, electrophysiological recordings, neuroimaging measurem...

This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states ...

A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, ne...

BackgroundBuilt environments shape navigation, attention, and motor control through continuous brain–body-environment coupling, yet architecture, rehabilitation, and clinical mobility practice still lack a shared quant...

Abstract Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in cognitive neuroscience. Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian comput...

Psychology in China is rapidly integrating neuroscience, data science, artificial intelligence, and engineering. While psychology-trained scholars remain the majority of faculty, there is an increase in the proportion of...

Abstract Human social behaviors involve complex interactions between individuals, and understanding how interbrain neural activity reflects and predicts these interactions is critical for advancing social cognitive neuro...

The philosophy of neuroscience has been a dynamic field of research in the philosophy of science since the turn of the century. As a result of this activity, a new mechanistic philosophy has emerged as the dominant appro...