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Although implicit motor adaptation is driven by sensory-prediction errors (SPEs), recent work has shown that task success modulates this process. Task success has typically been defined as hitting a target, which signifies the goal of the movement. Visuomotor adaptation tasks are uniquely situated to experimentally manipulate task success independently from SPE by changing the target size or the location of the target. These two, distinct manipulations may influence implicit motor adaptation in different ways, so, over four experiments, we sought to probe the efficacy of each manipulation. We found that changes in target size, which caused the target to fully envelop the cursor, only affected implicit adaptation for a narrow range of SPE sizes, while jumping the target to overlap with the cursor more reliably and robustly affected implicit adaptation. Taken together, our data indicate that, while task success exerts a small effect on implicit adaptation, these effects are susceptible to methodological variations. Future investigations of the effect of task success on implicit adaptation could benefit from employing target jump manipulations instead of target size manipulations. Recent work has suggested that task success, namely, hitting a target, influences implicit motor adaptation. Here, we observed that implicit adaptation is modulated by target jump manipulations, where the target abruptly "jumps" to meet the cursor; however, implicit adaptation was only weakly modulated by target size manipulations, where a static target either envelops or excludes the cursor. We discuss how these manipulations may exert their effects through different mechanisms.
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http://dx.doi.org/10.1152/jn.00061.2023 | DOI Listing |
Med Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
View Article and Find Full Text PDFComput Methods Programs Biomed
August 2025
Zhengzhou University, School of Computer and Artificial Intelligence, Zhengzhou, 450001, China. Electronic address:
Background And Objective: The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.
View Article and Find Full Text PDFJMIR Res Protoc
September 2025
Moores Cancer Center, University of California, San Diego, La Jolla, CA, United States.
Background: Cancer screening nonadherence persists among adults who are deaf, deafblind, and hard of hearing (DDBHH). These barriers span individual, clinician, and health care system levels, contributing to difficulties understanding cancer information, accessing screening services, and following treatment directives. Critical communication barriers include ineffective patient-physician communication, limited access to American Sign Language (ASL) cancer information, misconceptions about medical procedures, insurance navigation difficulties, and intersectional barriers for multiply marginalized individuals.
View Article and Find Full Text PDFJ Exp Anal Behav
September 2025
Oslo Metropolitan University, Norway.
Go/no-go successive matching (GNG-matching) tasks are one of several procedures used to establish conditional discriminations. This study presents a systematic review aimed at comparing procedures and outcomes of empirical studies using GNG-matching tasks for the emergence of symmetry, transitive, and global equivalence relations in humans and non-humans. A total of 22 articles were analyzed-nine with nonhumans and thirteen with humans.
View Article and Find Full Text PDFFront Hum Neurosci
August 2025
Department of Psychology, Northeastern University, Boston, MA, United States.
Mentalizing skills-the capacity to attribute mental states-play critical roles in word learning during typical language development. In autism, mentalizing difficulties may constrain word-learning pathways, limiting language-acquisition opportunities. We ask how autistic children encode and retrieve novel words and what drives individual differences.
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