Burgos, J.E.(2007).
Autoshaping and automaintenance: a neural-network approach.
Journal of the Experimental Analysis of Behavior, 88, 115-130.
This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a
neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and
takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using
an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency
between inputs that simulated a conditioned stimulus (CS) and an input that simulated an unconditioned stimulus
(US). Responding was simulated as an output activation that was neither elicited by nor required for the US. B
was an omission-training procedure. Response directedness was defined as sensory feedback from responding,
simulated as a dependence of other inputs on responding. In Simulation 1, the phenomena were simulated with a
fully connected architecture and maximally intense response feedback. The other simulations used a partially
connected architecture without competition between CS and response feedback. In Simulation 2, a maximally
intense feedback resulted in substantial autoshaping and automaintenance. In Simulation 3, eliminating response
feedback interfered substantially with autoshaping and automaintenance. In Simulation 4, intermediate autoshaping
and automaintenance resulted from an intermediate response feedback. Implications for the operant–respondent
distinction and the behavior-neuroscience relation are discussed.
Key words: autoshaping, automaintenance, interpretation, neural networks, directedness, response feedback,
operant-respondent distinction, behavior-neuroscience relation