Associated stimulus valence affects neural responses at an early processing stage. However, in the field of written language processing, it is unclear whether semantics of a word or low-level visual features affect early neural processing advantages. The current study aimed to investigate the role of semantic content on reward and loss associations. Participants completed a learning session to associate either words (Experiment 1, N = 24) or pseudowords (Experiment 2, N = 24) with different monetary outcomes (gain-associated, neutral or loss-associated). Gain-associated stimuli were learned fastest. Behavioural and neural response changes based on the associated outcome were further investigated in separate test sessions. Responses were faster towards gain- and loss-associated than neutral stimuli if they were words, but not pseudowords. Early P1 effects of associated outcome occurred for both pseudowords and words. Specifically, loss-association resulted in increased P1 amplitudes to pseudowords, compared to decreased amplitudes to words. Although visual features are likely to explain P1 effects for pseudowords, the inversed effect for words suggests that semantic content affects associative learning, potentially leading to stronger associations.
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http://dx.doi.org/10.1016/j.neuropsychologia.2018.12.012 | DOI Listing |
BMC Genomics
January 2025
Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 610225, China.
Background: Microsatellites are highly polymorphic repeat sequences ubiquitously interspersed throughout almost all genomes which are widely used as powerful molecular markers in diverse fields. Microsatellite expansions play pivotal roles in gene expression regulation and are implicated in various neurological diseases and cancers. Although much effort has been devoted to developing efficient tools for microsatellite identification, there is still a lack of a powerful tool for large-scale microsatellite analysis.
View Article and Find Full Text PDFEnviron Manage
January 2025
TECNALIA Research & Innovation, Basque Research and Technology Alliance (BRTA), Energy, climate, and urban transition, Parque Tecnológico de Bizkaia, Derio, Spain.
The extent and timescale of climate change impacts remain uncertain, including global temperature increase, sea level rise, and more frequent and intense extreme events. Uncertainties are compounded by cascading effects. Nevertheless, decision-makers must take action.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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