Neural network simulation of visual inspection of graphs of single-subject interventions

Judgments of the effectiveness of single-subject behavioral interventions are often based on visual examination of graphs of response data, but previous research indicates that such judgments are often unreliable and flawed. Here it is proposed that artificial neural networks could be developed to simulate the judgments of expert judges. A prototype of such a network was designed and trained in the present study, and its use in novel experiments matched the estimates of the expert whose judgments were simulated significantly better than did a prediction equation developed using a multiple regression approach.