The current study examined if bilingual advantages in cognitive control influence memory encoding during a divided attention task. Monolinguals, simultaneous bilinguals, and sequential bilinguals switched between classifying objects and words, then were tested for their recognition memory of stimuli previously seen during the classification task. Compared to bilingual groups, monolinguals made the most errors on the classification task and simultaneous bilinguals committed the fewest errors. On the memory task, however, no differences were found between the three language groups, but significant correlations were found between the number of errors during switch trials on the classification task and recognition memory for both target and non-target stimuli. For bilinguals, their age of second language acquisition partially accounted for the association between attentional control (number of switch errors) and subsequent memory for non-target stimuli only. These results contribute to our understanding of how individual differences in language acquisition influence interactions between cognitive domains.
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http://dx.doi.org/10.1017/S1366728915000851 | DOI Listing |
Tomography
November 2024
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong 999077.
Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application.
View Article and Find Full Text PDFJ Imaging
December 2024
PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures.
View Article and Find Full Text PDFJMIR Form Res
December 2024
thymia, International House, 64 Nile Street, London, N1 7SR, United Kingdom, 44 7477285252.
Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations.
View Article and Find Full Text PDFJ Biomed Phys Eng
December 2024
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.
Objective: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).
Material And Methods: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data.
Cancer Rep (Hoboken)
December 2024
Surgical, Medical and Dental Department of Morphological Sciences Related to Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy.
Background: Cancer incidence in the Galapagos archipelago is unknown.
Aim: In 2021, a task force including Ecuadorian and Italian researchers was established to estimate cancer incidence among the 25 244 Galapagos residents.
Methods: Registration covered all malignancies, including malignant melanoma and non-melanoma skin cancers; case recording was based on the International Classification of Diseases for Oncology.
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