tVNS enhances various memory and learning mechanisms, but there is inconclusive evidence on whether probabilistic learning can be enhanced by tVNS. Here, we tested a simplified version of the probabilistic learning task with monetary rewards in a between-participants design with left and right-sided cymba conchae and tragus stimulation (compared to sham stimulation) in a sample of healthy individuals (n = 80, 64 women, on average 26.38 years old). tVNS enhances overall accuracy significantly (p = 4.09 x 10) and reduces response times (p = 1.1006 x 10) in the probabilistic learning phase. Reinforcement learning modelling of the data revealed that the tVNS group uses a riskier strategy, dedicates more time to stimulus encoding and motor processes and exhibits greater reward sensitivity relative to the sham group. The learning advantage for tVNS relative to sham persists (p = 0.005 for accuracy and p = 9.2501 × 10 for response times) during an immediate extinction phase with continued stimulation in which feedback and reward were omitted. Our observations are in line with the proposal that tVNS enhances reinforcement learning in healthy individuals. This suggests that tVNS may be useful in contexts where fast learning and learning persistence in the absence of a reward is an advantage, for example, in the case of learning new habits.
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http://dx.doi.org/10.1111/psyp.70037 | DOI Listing |
Med Image Anal
March 2025
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region of China; Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong Special Administrative Region of China. Electronic address:
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View Article and Find Full Text PDFSci Adv
March 2025
Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty.
View Article and Find Full Text PDFSupervised Cross-Modal Retrieval (SCMR) achieves significant performance with the supervision provided by substantial label annotations of multi-modal data. However, the requirement for large annotated multi-modal datasets restricts the use of supervised cross-modal retrieval in many practical scenarios. Active Learning (AL) has been proposed to reduce labeling costs while improving performance in various label-dependent tasks, in which the most informative unlabeled samples are selected for labeling and training.
View Article and Find Full Text PDFCommun Eng
March 2025
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.
Emerging devices, such as magnetic tunnel junctions, are key for energy-efficient, performant future computing systems. However, designing devices with the desirable specification and performance for these applications is often found to be time-consuming and non-trivial. Here, we investigate the design and optimization of spin-orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation.
View Article and Find Full Text PDFPLoS Comput Biol
March 2025
School of Mathematics and Statistics, Qingdao University, Qingdao, China.
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