The cerebellum causally supports social processing by generating internal models of social events based on statistical learning of behavioral regularities. However, whether the cerebellum is only involved in forming or also in using internal models for the prediction of forthcoming actions is still unclear. We used cerebellar transcranial Direct Current Stimulation (ctDCS) to modulate the performance of healthy adults in using previously learned expectations in an action prediction task. In a first learning phase of this task, participants were exposed to different levels of associations between specific actions and contextual elements, to induce the formation of either strongly or moderately informative expectations. In a following testing phase, which assessed the use of these expectations for predicting ambiguous (i.e. temporally occluded) actions, we delivered ctDCS. Results showed that anodic, compared to sham, ctDCS boosted the prediction of actions embedded in moderately, but not strongly, informative contexts. Since ctDCS was delivered during the testing phase, that is after expectations were established, our findings suggest that the cerebellum is causally involved in using internal models (and not just in generating them). This encourages the exploration of the clinical effects of ctDCS to compensate poor use of predictive internal models for social perception.
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http://dx.doi.org/10.1093/scan/nsae019 | DOI Listing |
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December 2024
School of Psychology, Inner Mongolia Normal University, Hohhot, China.
The purpose of this study was to evaluate the psychometric properties of the Chinese version of the Revised Indebtedness Scale (IS-R-C) in mainland China. A total of 1057 university students participated in this study using a two-wave whole-group sampling method. Sample 1, consisting of 537 participants, was used for item analysis and exploratory factor analysis (EFA) of the Revised Indebtedness Scale (IS-R).
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
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December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
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