Background: Theoretical models posit abnormalities in cortico-striatal pathways in two of the most common neurodevelopmental disorders (Developmental dyslexia, DD, and Attention deficit hyperactive disorder, ADHD), but it is still unclear what distinct cortico-striatal dysfunction might distinguish language disorders from others that exhibit very different symptomatology. Although impairments in tasks that depend on the cortico-striatal network, including reinforcement learning (RL), have been implicated in both disorders, there has been little attempt to dissociate between different types of RL or to compare learning processes in these two types of disorders. The present study builds upon prior research indicating the existence of two learning manifestations of RL and evaluates whether these processes can be differentiated in language and attention deficit disorders. We used a two-step RL task shown to dissociate model-based from model-free learning in human learners.
Results: Our results show that, relative to neurotypicals, DD individuals showed an impairment in model-free but not in model-based learning, whereas in ADHD the ability to use both model-free and model-based learning strategies was significantly compromised.
Conclusions: Thus, learning impairments in DD may be linked to a selective deficit in the ability to form action-outcome associations based on previous history, whereas in ADHD some learning deficits may be related to an incapacity to pursue rewards based on the tasks' structure. Our results indicate how different patterns of learning deficits may underlie different disorders, and how computation-minded experimental approaches can differentiate between them.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029183 | PMC |
http://dx.doi.org/10.1186/s12993-023-00207-w | DOI Listing |
Behav Sci (Basel)
December 2024
Institute of Education, Xiamen University, Xiamen 361005, China.
The authors would like to adjust the author order for the following two reasons: 1 [...
View Article and Find Full Text PDFCancer Med
January 2025
Department of Pediatric Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Background: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for advanced image processing technology such as Deep Learning (DL).
View Article and Find Full Text PDFNed Tijdschr Geneeskd
January 2025
Integraal Kankercentrum Nederland (IKNL), Utrecht.
Unlabelled: The number of individuals with advanced cancer is increasing, making palliative care more important. However, there is limited knowledge in the Netherlands about the quality of care received by patients in the palliative phase. This is why the Netherlands comprehensive cancer organization (IKNL) started the 'eQuiPe study' to understand the experienced quality of care and quality of life of patients with advanced cancer and their relatives to further improve palliative care.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objective: The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.
Methods: This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm.
Ann Surg
January 2025
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Objective: To assess performance of an algorithm for automated grading of surgery-related adverse events (AEs) according to Clavien-Dindo (C-D) classification.
Summary Background Data: Surgery-related AEs are common, lead to increased morbidity for patients, and raise healthcare costs. Resource-intensive manual chart review is still standard and to our knowledge algorithms using electronic health record (EHR) data to grade AEs according to C-D classification have not been explored.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!