This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

No abstract

In this paper we discuss a computational cognitive model of children's poor performance on pronoun interpretation (the so-called Delay of Principle B Effect, or DPBE). This cognitive model is based on a theoretical account that attributes the DPBE to children's inability as hearers to also take into account the speaker's perspective. The cognitive model predicts that child hearers are unable to do so because their speed of linguistic processing is too limited to perform this second step in interpretation. We tested this hypothesis empirically in a psycholinguistic study, in which we slowed down the speech rate to give children more time for interpretation, and in a computational simulation study. The results of the two studies confirm the predictions of our model. Moreover, these studies show that embedding a theory of linguistic competence in a cognitive architecture allows for the generation of detailed and testable predictions with respect to linguistic performance.

This paper presents a study of the effect of working memory load on the interpretation of pronouns in different discourse contexts: stories with and without a topic shift. We discuss a computational model (in ACT-R, Anderson, 2007) to explain how referring expressions are acquired and used. On the basis of simulations of this model, it is predicted that WM constraints only affect adults' pronoun resolution in stories with a topic shift, but not in stories without a topic shift. This latter prediction was tested in an experiment. The results of this experiment confirm that WM load reduces adults' sensitivity to discourse cues signaling a topic shift, thus influencing their interpretation of subsequent pronouns.

No abstract