Simulataneous versus Successive Learning in Neural Networks
Abstract (Summary)Psychology experiments using human subjects to study simultaneous versus successive learning for a discrimination task were replicated using neural networks. The two types of learning were defined by the manner in which the stimuli were supplied to the subjects. Simultaneous and successive learning were defined by the type of the neural network (batch and iterative) and the order of the training data. The results of the experiments were similar to but did not exactly match those obtained by psychologists. The experiments were extended to incorporate larger data sets and variations of the parameters involved. Conducting such experiments led to better understanding of various neural network features, thereby providing benefit to neural network research. Further validation can help psychologists establish neural networks as a tool for their experiments. Thus this study lays the foundation for an inter-disciplinary study, which could evolve into a symbiotic relationship between machine learning and psychology.
School Location:USA - Ohio
Source Type:Master's Thesis
Date of Publication:01/01/2005