Theoretical note: simulating latent inhibition with selection neural networks.
The selection neural-network model proposed by Donahoe et al. [J. Exp. Anal. Behav. 60 (1993) 17] was used to simulate latent inhibition (LI). The model can simulate increases of LI by the number, intensity, and duration of preexposed conditioned stimulus (CS). It can also simulate dependence on total CS preexposure time, CS specificity, and attenuation by preexposure to a compound that includes the to-be-trained CS. It also predicts a potentially new phenomenon: acquisition facilitation by preexposure to a stimulus that is orthogonal to and synaptically competitive with the to-be-trained CS. The basic mechanism is the same through which the model simulates extinction, namely, weight decrement. The realization of this mechanism in the present simulations required two conditions. First, networks had to come to the experimental situation with substantial initial connection weights in the sensory-association subnetwork (0.15, compared to the 0.01 value we have used in all previous simulations). Second, the discrepancy threshold for deciding whether to increase or decrease weights had to be larger than zero (the value we have used in all published simulations). A value of 0.001 was sufficient to produce all the effects.
Burgos
José E.
JE
Centro de Estudios e Investigaciones en Comportamiento, University of Guadalajara, 12 de Diciembre 204, Col. Chapalita, CP 45030-, Jalisco, Guadalajara, Mexico