A 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). Simulations 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): Ax+ Bx- Cx+ Dx- Ay- By+ Cy- Dy+. Contexts A and C acquired equivalence, which was evident from subsequent revaluation, compound testing and 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