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Gender differences in elementary school students' fraction learning: roles of spatial ability and mathematical anxiety.
Front Psychol
December 2024
Shanghai Key Laboratory of Brain Functional Genomics, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Aim: In this study, we examined gender differences in fraction learning and explored potential underlying mechanisms.
Methods: The mediating effects of spatial ability and mathematical anxiety on gender differences in fraction learning were tested in elementary school students. A total of 165 sixth-grade students (83 girls) from public elementary schools participated in the study.
Automated landmark-based cat facial analysis and its applications.
Front Vet Sci
December 2024
Information Systems Department, University of Haifa, Haifa, Israel.
Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy.
View Article and Find Full Text PDFBalancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.
JMIR Ment Health
December 2024
Faculty of Applied Computer Science, Augsburg University, Augsburg, Germany.
Background: The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices.
View Article and Find Full Text PDFSet-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition.
Cogn Neurodyn
December 2024
School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102 Jilin People's Republic of China.
The utilization of Electroencephalography (EEG) for emotion recognition has emerged as the primary tool in the field of affective computing. Traditional supervised learning methods are typically constrained by the availability of labeled data, which can result in weak generalizability of learned features. Additionally, EEG signals are highly correlated with human emotional states across temporal, spatial, and spectral dimensions.
View Article and Find Full Text PDFThe theory of preloading: A cognitive-behavioural motivational model.
Harm Reduct J
December 2024
School of Applied Psychology, Griffith University, 176 Messines Ridge Road, Mt Gravatt, QLD, 4122, Australia.
Preloading of alcohol and/or drugs before an event has been examined in the research literature for the past two decades. Despite the considerable interest and scrutiny on the behaviour, there are limited, if any, attempts to conceptualise a theoretical understanding of why people preload before an event. Here we propose a Theory of Preloading (TOP)-a general cognitive-behavioural motivational model for alcohol and drug preloading.
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