Prototype-distortion category learning: A two-phase learning process across a distributed network
Prototype-distortion category learning: A two-phase learning process across a distributed network
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DOI：
10.1016/j.bandc.2005.06.004
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发表时间：
2006-04-01
影响因子：
2.5
通讯作者：
Thulborn, KR
中科院分区：
文献类型：
--
作者：
Little, DM;Thulborn, KR
本文回顾了我们实验室进行的一系列工作，应用功能磁共振成像(FMRI)来更好地了解原型失真学习过程中发生的生物反应和变化。我们回顾了两个实验(Little，Klein，Shobat，McClure，&Thulborn，2004；Little&Thulborne，2005)的结果，这两个实验通过一个两阶段模型提供了对提高神经元效率的支持，该两阶段模型包括跨分布式网络的组织招募的初始阶段，随后是同一网络中随着体积的减少而增加专业化的阶段。在这两项研究中，参与者学会了将随机点扭曲的模式分类(Posner&Keele，1968)。在这一学习过程中的四个时间点，受试者接受了使用类别匹配任务的功能磁共振检查。在整个方案中发现了一个大范围的网络，包括额叶眼野，包括顶下小叶和上顶叶，以及视觉皮质。随着行为表现的增加，这些区域内的激活量首先增加，后来在协议中减少。在我们对这项工作的回顾的基础上，我们提出：(I)类别学习反映了其对最初为完成新任务而涉及的同一网络的专门化，以及(Ii)该网络涵盖了以前没有报道过受原型失真学习影响的区域。(C)2005 Elsevier Inc.保留所有权利。
This paper reviews a body of work conducted in our laboratory that applies functional magnetic resonance imaging (fMRI) to better understand the biological response and change that occurs during prototype-distortion learning. We review results from two experiments (Little, Klein, Shobat, McClure, & Thulborn, 2004; Little & Thulborn, 2005) that provide Support For increasing neuronal efficiency by way of a two-stage model that includes an initial period of recruitment Of tissue across a distributed network that is followed by a period of increasing specialization with decreasing volume across the same network. Across the two Studies, participants learned to classify patterns of random-dot distortions (Posner & Keele, 1968) into categories. At four points across this learning process subjects underwent examination by fMRI using a category-matching task. A large-scale network, altered across the protocol, was identified to include the Frontal eye fields, both inferior and Superior parietal lobules, and Visual cortex. As behavioral performance increased, the volume of activation within these regions first increased and later in the protocol decreased. Based oil our review of this work we propose that: (i) category learning is reflected its specialization of the same network initially implicated to complete the novel task, and (ii) this network encompasses regions not previously reported to be affected by prototype-distortion learning. (C) 2005 Elsevier Inc. All rights reserved.