Computational implementation of a Hebbian learning network and its application to configural forms of acquired equivalence

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 (CPCA) Hebbian learning algorithm and were of four published experimental demonstrations of configural acquired equivalence. Experiments involved training rats on appetitive bi-conditional discriminations in which discrete cues, (w and x) signaled food delivery (+) or its absence (-) in four different contexts (A, B, C and D): Aw+ Bw- Cw+ Dw- Ax- Bx+ Cx- Dx+. Contexts A and C acquired equivalence. In three of the experiments acquired equivalence was evident from subsequent revaluation, from compound testing or from whole-/part-reversal training. The fourth experiment added concurrent bi-conditional 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 four experiments.