The Interpretation of Line of Regard by Prelingually Deaf Children

The ability of 5- to 12-year-old deaf children to utilise and interpret another person's visual line of regard (where the eyes are looking) was studied in four experiments using cartoon faces. The children had little difficulty in determining whether or not a face was looking directly at them. They had more difficulty, however, with more complex tasks requiring them to infer mental states of desire and intention from line of regard and to ignore line of regard when it was inappropriate to attend to this cue. Deaf children raised in a hearing environment appear to have more difficulty with these more complex tasks than hearing children. The results are discussed in terms of the special difficulties facing some deaf children in the development of skills involving utilisation of line of regard and the implications for the development of joint attentional behaviour, theory of mind and dyadic social interaction in deaf and hearing children.     

Confidence intervals centred on bootstrap smoothed estimators

Bootstrap smoothed (bagged) parameter estimators have been proposed as an improvement on estimators found after preliminary data‐based model selection. A result of Efron in 2014 is a very convenient and widely applicable formula for a delta method approximation to the standard deviation of the bootstrap smoothed estimator. This approximation provides an easily computed guide to the accuracy of this estimator. In addition, Efron considered a confidence interval centred on the bootstrap smoothed estimator, with width proportional to the estimate of this approximation to the standard deviation. We evaluate this confidence interval in the scenario of two nested linear regression models, the full model and a simpler model, and a preliminary test of the null hypothesis that the simpler model is correct. We derive computationally convenient expressions for the ideal bootstrap smoothed estimator and the coverage probability and expected length of this confidence interval. In terms of coverage probability, this confidence interval outperforms the post‐model‐selection confidence interval with the same nominal coverage and based on the same preliminary test. We also compare the performance of the confidence interval centred on the bootstrap smoothed estimator, in terms of expected length, to the usual confidence interval, with the same minimum coverage probability, based on the full model.