Bayesian data analysis for identification and estimation of the learning effects of pointing tasks

Abstract

Recently, in the field of human-computer interaction a model was developed for evaluating the performance of the input devices of a computer instead of the conventional Fitts' law. This model concerns two factors which are treated as systematic factor and human factor respectively, so it is called the SH-model. In this paper, in order to extend the range of application of the SH-model we propose a new model as by using the Box-Cox transformation then apply a Bayesian modeling method for estimating the learning effect of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed model can be applied satisfactorily, thus providing proof of the validity of our modeling method.