Perceptual category learning in autism spectrum disorder: Truth and consequences.

Neurosci Biobehav Rev

Canisius College, Institute for Autism Research, Science Hall, 2001 Main St., Buffalo, NY, 14208, USA.

Published: November 2020

The ability to categorize is fundamental to cognitive development. Some categories emerge effortlessly and rapidly while others can take years of experience to acquire. Children with autism spectrum disorder (ASD) are often able to name and sort objects, suggesting that their categorization abilities are largely intact. However, recent experimental work shows that the categories formed by individuals with ASD may diverge substantially from those that most people learn. This review considers how atypical perceptual category learning can affect cognitive development in children with ASD and how atypical categorization may contribute to many of the socially problematic symptoms associated with this disorder. Theoretical approaches to understanding perceptual processing and category learning at both the behavioral and neural levels are assessed in relation to known alterations in perceptual category learning associated with ASD. Mismatches between the ways in which children learn to organize perceived events relative to their peers and adults can accumulate over time, leading to difficulties in communication, social interactions, academic performance, and behavioral flexibility.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744437PMC
http://dx.doi.org/10.1016/j.neubiorev.2020.08.016DOI Listing

Publication Analysis

Top Keywords

category learning
16
perceptual category
12
autism spectrum
8
spectrum disorder
8
cognitive development
8
perceptual
4
learning
4
learning autism
4
disorder truth
4
truth consequences
4

Similar Publications

Dynamics and triggers of misinformation on vaccines.

PLoS One

January 2025

Centro Ricerche Enrico Fermi, Rome, Italy.

The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.

View Article and Find Full Text PDF

A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications.

PLoS One

January 2025

Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.

There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification.

View Article and Find Full Text PDF

Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).

Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.

View Article and Find Full Text PDF

A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations.

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!