Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

Human responding with mutual reinforcement: Baseline and effects of an intervention1

Pressing a key by one undergraduate provided another undergraduate with points exchangeable for money, and vice versa. Four types of response patterns were found. Points were often delivered with a delay from the last response or with no response. When a contingency that responses lost points (punishment) was added to participants who had emitted more responses than the partner had, their response rates decreased while the rates of their partners increased. These results demonstrate that, under the contingency of mutual reinforcement: (a) response patterns that had occurred between monkeys were replicated between humans; (b) obtained response‐reinforcer relations were different from those generally programmed in the basic reinforcement schedules; and (c) the behavior of the participant was controlled by changing the behavior of the partner.