Delamater Andrew R
Psychology Department, Brooklyn College - CUNY, 2900 Bedford Ave, Brooklyn, NY 11210, USA.
Learn Behav. 2012 Mar;40(1):1-23. doi: 10.3758/s13420-011-0036-4.
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 regard 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 article 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.
巴甫洛夫条件反射研究中的一个重大问题是如何描述参与学习的事件表征的本质。关于无条件刺激的哪些特征参与学习这一问题，已进行了广泛探讨，但针对描述条件刺激本质问题的研究却少得多。本文介绍一种多层联结主义网络方法，以理解条件刺激的“感知”或“概念”表征如何从条件反射中产生，并参与各种学习现象。该模型被应用于线索效应的习得等价性/差异性，以及各种条件辨别学习任务（模式化、双条件、模糊场合设定、特征辨别）。此外，还综述了研究无条件刺激哪些方面参与学习的相关研究。最终得出结论，采用多层联结主义网络视角看待巴甫洛夫式学习，能为我们提供更丰富的方式来审视基本学习过程，但仍有一些关键理论问题有待解决，特别是与整合我们对条件刺激和无条件刺激表征本质的认识相关的问题。
