Thorwart Anna, Livesey Evan J, Harris Justin A
University of Sydney, Sydney, New South Wales, Australia.
Learn Behav. 2012 Sep;40(3):334-46. doi: 10.3758/s13420-012-0073-7.
Harris and Livesey. Learning & Behavior, 38, 1-26, (2010) described an elemental model of associative learning that implements a simple learning rule that produces results equivalent to those proposed by Rescorla and Wagner (1972), and additionally modifies in "real time" the strength of the associative connections between elements. The novel feature of this model is that stimulus elements interact by suppressively normalizing one another's activation. Because of the normalization process, element activity is a nonlinear function of sensory input strength, and the shape of the function changes depending on the number and saliences of all stimuli that are present. The model can solve a range of complex discriminations and account for related empirical findings that have been taken as evidence for configural learning processes. Here we evaluate the model's performance against the host of conditioning phenomena that are outlined in the companion article, and we present a freely available computer program for use by other researchers to simulate the model's behavior in a variety of conditioning paradigms.
哈里斯和利夫西（《学习与行为》，第38卷，第1 - 26页，2010年）描述了一种联想学习的基本模型，该模型实施了一个简单的学习规则，产生的结果与雷斯克拉和瓦格纳（1972年）提出的结果相当，并且还能“实时”修改元素之间联想连接的强度。该模型的新颖之处在于刺激元素通过相互抑制归一化彼此的激活来相互作用。由于归一化过程，元素活动是感觉输入强度的非线性函数，并且该函数的形状会根据所有存在的刺激的数量和显著性而变化。该模型可以解决一系列复杂的辨别问题，并解释相关的实证发现，这些发现被视为构型学习过程的证据。在这里，我们根据配套文章中概述的大量条件作用现象来评估该模型的性能，并且我们提供了一个免费的计算机程序，供其他研究人员用于模拟该模型在各种条件作用范式中的行为。
