Conditional discrimination in young children: the roles of associative and relational processing.

J Exp Child Psychol

School of Applied Psychology and Applied Cognitive Neuroscience Research Unit, Behavioural Basis of Health Program, Griffith Health Institute, Griffith University, Gold Coast, QLD 4222, Australia.

Published: May 2012

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.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jecp.2011.12.004DOI Listing

Publication Analysis

Top Keywords

conditional discrimination
20
relational processing
16
associative relational
8
reversal learning
8
simpler problems
8
relational
6
conditional
5
processing
5
discrimination young
4
children
4

Similar Publications

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.

View Article and Find Full Text PDF

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 PDF

CDCGAN: Class Distribution-aware Conditional GAN-based minority augmentation for imbalanced node classification.

Neural 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 PDF

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.

View Article and Find Full Text PDF
Article Synopsis
  • The W.H.O. fungal priority pathogen requires better tools for studying essential genes, and the auxin-inducible degron (AID) system offers a solution for rapidly depleting proteins.
  • The AID2 system enhances previous methods by using a specific auxin (5-Ph-IAA) and a mutant version of OsTIR1 to increase sensitivity and effectiveness in protein degradation.
  • Researchers optimized the AID2 system for their studies, proving it can effectively target and degrade essential proteins, thus allowing for deeper exploration of the genome's functionality.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!