Auditory and visual category learning in children and adults.

Developmental psychology(2023)

引用 0|浏览5
暂无评分
摘要
Categories are fundamental to everyday life and the ability to learn new categories is relevant across the lifespan. Categories are ubiquitous across modalities, supporting complex processes such as object recognition and speech perception. Prior work has proposed that different categories may engage learning systems with unique developmental trajectories. There is a limited understanding of how perceptual and cognitive development influences learning as prior studies have examined separate participants in a single modality. The current study presents a comprehensive assessment of category learning in 8-12-year-old children (12 female; 34 white, 1 Asian, 1 more than one race; household income $85-$100 K) and 18-61-year-old adults (13 female; 32 white, 10 Black or African American, 4 Asian, 2 more than one race, 1 other; household income $40-55 K) in a broad sample collected online from the United States. Across multiple sessions, participants learned categories across modalities (auditory, visual) that engage different learning systems (explicit, procedural). Unsurprisingly, adults outperformed children across all tasks. However, this enhanced performance was asymmetrical across categories and modalities. Adults far outperformed children in learning visual explicit categories and auditory procedural categories, with fewer differences across development for other types of categories. Adults' general benefit over children was due to enhanced information processing, while their superior performance for visual explicit and auditory procedural categories was associated with less cautious correct responses. These results demonstrate an interaction between perceptual and cognitive development that influences learning of categories that may correspond to the development of real-world skills such as speech perception and reading. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
更多
查看译文
关键词
category learning,perception,cognition,drift-diffusion modeling,decision making,learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要