Two experiments examined conditional discrimination in 4- to 6-year-olds. Children learned to choose one of two objects (e.g., circle) when the background was, say, red and to choose the other object (e.g., triangle) when the background was, say, blue. Awareness was assessed and interpreted as a marker of relational processing. In Experiment 1, most 4- and 5-year-olds did not reach the learning criterion. Children in Experiment 2 solved simpler reversal learning problems before the conditional discrimination problems. Most 4- to 6-year-olds reached criterion, but they did not necessarily demonstrate awareness, suggesting that reversal learning and conditional discrimination can be acquired through associative or relational processing. Relational processing increased with age and was used more on simpler problems. Fluid intelligence predicted Problem 2 performance in children who used relational (not associative) processing on Problem 1. Prior experience with simpler problems and awareness of relational structure are influential in children's conditional discrimination.
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http://dx.doi.org/10.1016/j.jecp.2011.12.004 | DOI Listing |
Dev Med Child Neurol
December 2024
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Aim: To replicate and extend previous psychometric findings for the Autism Symptom Dimensions Questionnaire (ASDQ).
Method: Using a cross-sectional design in two samples, including a total of 3366 children and adolescents (aged 2-17 years; 1399 with autism spectrum disorder) and a small case series, factor structure, measurement invariance, reliability, construct validity, screening and diagnostic efficiency, and detection of reliable change were examined for the ASDQ.
Results: Strong psychometric properties were observed, including replicable factor structure, strong measurement invariance, adequate-to-excellent scale and conditional reliability, strong convergent and discriminant validity, and good screening efficiency.
Heliyon
October 2024
School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou City, 325035, Zhejiang Province, China.
Generating high quality histopathology images like immunohistochemistry (IHC) stained images is essential for precise diagnosis and the advancement of computer-aided diagnostic (CAD) systems. Producing IHC images in laboratory is quite expensive and time consuming. Recently, some attempts have been made based on artificial intelligence techniques (particularly, deep learning) to generate IHC images.
View Article and Find Full Text PDFNeural Netw
November 2024
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, 200240, China; Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China. Electronic address:
Node classification is a fundamental task of Graph Neural Networks (GNNs). However, GNN models tend to suffer from the class imbalance problem which deteriorates the representation ability of minority classes, thus leading to unappealing classification performance. The most straightforward and effective solution is to augment the minority samples for balancing the representations of majority and minority classes.
View Article and Find Full Text PDFPLoS One
December 2024
School of Computing and Artificial Intelligence, Changzhou University, Changzhou, China.
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning, automatic liver tumor segmentation algorithms based on neural networks have emerged, for the improvement of the work efficiency.
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