On the nature of CS and US representations in Pavlovian learning.

On the nature of CS and US representations in Pavlovian learning.
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DOI:
10.3758/s13420-011-0036-4
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发表时间:
2012-03
影响因子:
1.8
通讯作者:
Delamater AR
Delamater AR
中科院分区:
心理学4区
文献类型:
--
作者:
Delamater AR

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巴甫洛夫条件反射研究中的一个重要问题是描述进入学习的事件的表征的性质。这个问题已被广泛探讨的问题,什么功能的非条件刺激进入学习,但相当少的工作已经针对的问题,表征的性质的条件刺激。本文介绍了一个多层的连接网络的方法来理解如何“感知”或“概念”的条件刺激表示可能会出现条件反射和参与各种学习现象。该模型适用于获得的等价性/独特性的线索效应,以及各种条件的歧视学习任务(模式化,biconstrands,模糊的场合设置,功能歧视)。此外,还回顾了研究非条件刺激的哪些方面进入学习。最终,它的结论是,采用多层连接网络的巴甫洛夫学习的角度为我们提供了一个更丰富的方式来查看基本的学习过程,但一些关键的理论问题仍然有待解决,特别是因为它们涉及到整合我们所知道的性质的表征条件和无条件刺激。
A significant problem in the study of Pavlovian conditioning is characterizing the nature of the representations of events that enter into learning. This issue has been explored extensively with regards to the question of what features of the unconditioned stimulus enter into learning, but considerably less work has been directed to the question of characterizing the nature of the conditioned stimulus. This paper introduces a multilayered connectionist network approach to understanding how “perceptual” or “conceptual” representations of the conditioned stimulus might emerge from conditioning and participate in various learning phenomena. The model is applied to acquired equivalence/distinctiveness of cue effects, as well as a variety of conditional discrimination learning tasks (patterning, biconditional, ambiguous occasion setting, feature discriminations). In addition, studies that have examined what aspects of the unconditioned stimulus enter into learning are also reviewed. Ultimately, it is concluded that adopting a multilayered connectionist network perspective of Pavlovian learning provides us with a richer way in which to view basic learning processes, but a number of key theoretical problems remain to be solved particularly as they relate to the integration of what we know about the nature of the representations of conditioned and unconditioned stimuli.
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