Sample-size and Repetition Effects on the Prediction Accuracy of Time and Error-rate Models in Steering Tasks

Abstract

A previous study on target pointing has shown that the accuracy of performance models improves as the number of participants and clicks increases, but the task was limited to artificially simplified one-dimensional movements. Practical user interfaces often require more complex operations, and thus we examine the effects of the number of participants and task repetitions on the fit of existing models for path-steering tasks. Empirical results showed that the model for predicting movement times consistently fitted the data with high accuracy, even when the numbers of participants and repetitions were small. However, the model for predicting error rates was less accurate in terms of R², MAE, and RMSE. Therefore, the benefit of recruiting numerous participants is relatively greater for the error-rate prediction model, which supports the previous study on target-pointing tasks.