Golden, R. M. (1994). Analysis of categorical time-series text recall data using a connectionist model. Journal of Biological Systems, 2, 283-305.


ABSTRACT

Categorical time-series are generated by discrete-time probabilistic dynamical systems which can only be in one of a small number of finite states at any given instant in time. A novel statistical methodology based upon log-linear modelling is proposed for analyzing categorical time-series data which allows one to incorporate a considerable amount of prior knowledge directly into the data analysis. The statistical model can be shown to be formally equivalent to a connectionist (i.e., artificial neural network) model. Methods for model selection and hypothesis testing using the new statistical model for samples with large numbers of observations are then developed using asymptotic statistical theory. To illustrate this new method of categorical time-series data analysis, the model is applied to the analysis of text free recall data from children and adults. These analyses indicated that the model can successfully use the order of recalled text propositions to discriminate among alternative theories of prior knowledge and alternative treatment conditions. The reliability of the large sample statistical tests were also checked using a boot-strap methodology and found to be acceptable.

Semi-final post script draft (possibly missing figures)

Golden's Text Comprehension and Memory Publications