Suppr超能文献

人类类别学习。

Human category learning.

作者信息

Ashby F Gregory, Maddox W Todd

机构信息

Department of Psychology, University of California-Santa Barbara, Santa Barbara, CA 93106, USA.

出版信息

Annu Rev Psychol. 2005;56:149-78. doi: 10.1146/annurev.psych.56.091103.070217.

Abstract

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

摘要

最近的许多证据表明,人们学习感知类别的方式存在一些显著差异,这取决于类别的确切构建方式。目前流行四种不同类型的类别学习任务——基于规则的任务、信息整合任务、原型扭曲任务和天气预报任务。使用这四种任务获得的认知、神经心理学和神经影像学结果在性质上有所不同。基于规则(明确推理)任务的成功取决于额叶纹状体回路,需要工作记忆和执行性注意力。信息整合任务的成功需要一种程序性学习形式,并且对反馈的性质和时间敏感。原型扭曲任务会引发感知(视觉皮层)学习。多种不同策略可导致在天气预报任务中取得成功。总体而言,这四种任务的结果提供了有力证据,表明人类类别学习是由多个性质不同的系统介导的。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验