期刊名称：International Journal of Comparative Psychology
印刷版ISSN：0889-3667
出版年度：2015
卷号：28
期号：1
页码：1-21
出版社：University of California Press
摘要：This paper describes a neural network account of misbehavior with an extant neural network model of conditioning. The model makes no distinction between learning (weight-change mechanisms) in operant and Pavlovian conditioning, but preserves the standard behavioral distinctions between types of stimuli, responses, and contingencies, with connectionist interpretations of some possible neuroanatomical substrates. Misbehavior has been traditionally conceived as a species-specific response R* that is unnecessary for a biologically significant reward S* but interferes with another response R that is necessary for S*. Misbehavior thus conceived has been explained as interfering Pavlovian conditioned responding. Three four-layer feedforward neural networks were designed to differ only in their output layers, as a connectionist interpretation of three hypothetical operant-Pavlovian relations in misbehavior, namely, interference (Pavlovian output to operant output lateral inhibitory connection), compatibility (Pavlovian output to operant output lateral excitatory connection), and independence (no lateral connection in the output layer). These relations are proposed as neural-network interpretations of neuroanatomical substrates of conditioning with three biologically significant stimuli, namely, food, water, and sexual mate, respectively. Each network first received pairings of contextual cues with its respective S*, to simulate pretraining with such stimuli. Then, networks received operant contingencies where S* was paired with the same contextual cues, as well as cues from a token dependently on R responding, defined as a minimal R activation of 0.5. Networks showed substantial misbehavior (qua conditioned R* responding) that interfered with R to different extents, food causing the most, sexual mate the least interference. Limitations, future directions, and implications for biological constraints and the generality of learning are discussed.
