AIMC Topic: Cognition
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LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...
Psychology of AI: How AI impacts the way people feel, think, and behave.
Current opinion in psychology
Jun 27, 2024
Over the past decade, artificial intelligence (AI) technologies have transformed numerous facets of our lives. In this article, we summarize key themes in emerging AI research in behavioral science. In doing so, we aim to unravel the multifaceted imp...
Emotional and cognitive trust in artificial intelligence: A framework for identifying research opportunities.
Current opinion in psychology
Jun 24, 2024
This article briefly summarizes trust as a multi-dimensional construct, and trust in AI as a unique but related construct. It argues that because trust in AI is couched within an economic landscape, these two frameworks should be combined to understa...
Representations and generalization in artificial and brain neural networks.
Proceedings of the National Academy of Sciences of the United States of America
Jun 24, 2024
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribu...
A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.
PloS one
Jun 21, 2024
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, ...
Cluster-CAM: Cluster-weighted visual interpretation of CNNs' decision in image classification.
Neural networks : the official journal of the International Neural Network Society
Jun 20, 2024
Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision...
Dynamically predicting comprehension difficulties through physiological data and intelligent wearables.
Scientific reports
Jun 13, 2024
Comprehending digital content written in natural language online is vital for many aspects of life, including learning, professional tasks, and decision-making. However, facing comprehension difficulties can have negative consequences for learning ou...
Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 13, 2024
Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection chal...
Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds.
Frontiers in public health
Jun 12, 2024
BACKGROUND: The aging process is associated with a cognitive and physical declines that affects neuromotor control, memory, executive functions, and motor abilities. Previous studies have made efforts to find biomarkers, utilizing complex factors suc...
Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties.
British journal of clinical pharmacology
Jun 11, 2024
AIMS: This study evaluated the use of machine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare ...
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