We describe and report the results of computer simulations of the three-layer Hebbian network informally described by Honey, Close, and Lin (2010): A general account of discrimination that has been shaped by data from configural acquired equivalence experiments that are beyond the scope of alternative models. Simulations implemented a conditional principle-components analysis Hebbian learning algorithm and were of four published experimental demonstrations of configural acquired equivalence. Experiments involved training rats on appetitive biconditional discriminations in which discrete cues (w and x) signaled food delivery (+) or its absence (-) in 4 different contexts (A, B, C, and D): Aw+ Bw- Cw+ Dw- Ax- Bx+ Cx- Dx+. Contexts A and C acquired equivalence. In 3 of the experiments acquired equivalence was evident from subsequent revaluation, from compound testing or from whole-/part-reversal training. The fourth experiment added concurrent biconditional discriminations with the same contexts but a pair of additional discrete cues (y and z). The congruent form of the discrimination, in which A and C provided the same information about y and z, was solved relatively readily. Parametric variation allowed the network to successfully simulate the results of each of the 4 experiments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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Hebbian学习网络的计算实现及其在获得性对等的配置形式中的应用。

我们描述并报告由Honey，Close和Lin（2010）非正式描述的三层Hebbian网络的计算机仿真结果：歧视的一般性解释已由超出范围的结构性获得性当量实验的数据形成替代模型。模拟实现了条件主成分分析Hebbian学习算法，并进行了四次已发布的配置性等效性实验演示。实验涉及对大鼠进行有条件的双条件辨别训练，其中在四个不同的环境（A，B，C和D）中，离散线索（w和x）表示食物输送（+）或不存在食物（-）：Aw + Bw- Cw + Dw- Ax- Bx + Cx- Dx +。上下文A和C获得了对等。在其中的3个实验中，通过随后的重估可以明显看出获得的等效性，来自复合测试或全部/部分反转培训。第四个实验在相同的上下文中添加了并发的双条件判别，但是有一对额外的离散线索（y和z）。A和C提供有关y和z相同信息的判别的全等形式相对容易解决。参数变化使网络可以成功地模拟4个实验中每个实验的结果。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。参数变化使网络可以成功地模拟4个实验中每个实验的结果。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。参数变化使网络可以成功地模拟4个实验中每个实验的结果。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。