Non-linear dynamics of operant behavior: a new approach via the extended return map.

Abstract

Previous efforts to apply non-linear dynamic tools to the analysis of operant behavior revealed some promise for this kind of approach, but also some doubts, since the complexity of animal behavior seemed to be beyond the analyzing ability of the available tools. We here outline a series of studies based on a novel approach. We modified the so-called 'return map' and developed a new method, the 'extended return map' (ERM) to extract information from the highly irregular time series data, the inter-response time (IRT) generated by Skinner-box experiments. We applied the ERM to operant lever pressing data from rats using the four fundamental reinforcement schedules: fixed interval (FI), fixed ratio (FR), variable interval (VI) and variable ratio (VR). Our results revealed interesting patterns in all experiment groups. In particular, the FI and VI groups exhibited well-organized clusters of data points. We calculated the fractal dimension out of these patterns and compared experimental data with surrogate data sets, that were generated by randomly shuffling the sequential order of original IRTs. This comparison supported the finding that patterns in ERM reflect the dynamics of the operant behaviors under study. We then built two models to simulate the functional mechanisms of the FI schedule. Both models can produce similar distributions of IRTs and the stereotypical 'scalloped' curve characteristic of FI responding. However, they differ in one important feature in their formulation: while one model uses a continuous function to describe the probability of occurrence of an operant behavior, the other one employs an abrupt switch of behavioral state. Comparison of ERMs showed that only the latter was able to produce patterns similar to the experimental results, indicative of the operation of an abrupt switch from one behavioral state to another over the course of the inter-reinforcement period. This example demonstrated the ERM to be a useful tool for the analysis of IRT accompanying intermittent reinforcement schedules and for the study of the non-linear dynamics of operant behavior.