Effects of Arousal in Human Instrumental Learning: A Test of Berlyne’s Arousal Theory by Means of Inferential Simulation1

Abstract

One of the purposes of the investigations reported here was to test Berlyne’s 1960 theory of arousal, i.e., in relation to learning. Among the fundamental hypotheses of this theory there is one implying that reinforcement depends on arousal regulation. This theory was translated into three mathematical models incorporating different assumptions about the relationship between arousal regulation and the effects of learning. Furthermore, on the basis of the assumption that reinforcement is not motivationally regulated but should be treated descriptively, two models were developed, matching all of the assumptions of two of the three motivational models except that of motivational regulation of reinforcement Because of the complexity of the models, they were investigated by means of inferential computer simulation. Parameters were estimated by Monte Carlo methods. The performance of the models was compared with the performance of a group of 50 human subjects in an instrumental learning task with a 100:0 CRF schedule. The performance of the motivational models proved to deviate at a high level of significance from the performance of the subjects (Tab. 2 & 3, Fig. 4). Moreover, the distribution of the performance of the motivational models is bimodal. This can be explained by the fact that most of the replications strive toward the opposite asymptote, closely approximating a criterion of several consecutive incorrect responses Further explorations showed that when negative reinforcement is disconnected from arousal regulation, performance is ameliorated (Fig. 5). This improvement is. however, rather small, so it must be concluded that the motivational models, and consequently the theory from which they are derived, are not useful for generating predictions about learning performance. The non-motivational models, on the other hand, show fairly good agreement with the performance of the subjects. Even a model with variable “learning operators” shows a reasonably good fit for some perseveration statistics as well as the general learning curve statistics (Tab. 2, 3 and 4) It is concluded that models with variable learning operators deserve further exploration, and that Berlyne’s arousal theory, as far as it is concerned with learning, seems to be defective in its most fundamental assumption.