Math Biology Seminar

Prof. Bob McMurray, Department of Psychological and Brain Sciences, University or Iowa
I’m not sure that curve means what you think it means: The chunky stochasticity of the fixation system challenges fundamental methods for understanding the dynamics of language processing.


The Visual World Paradigm (VWP) is a powerful experimental paradigm for language research. Listeners respond to speech in a “visual world” containing potential referents of the speech. Fixations to these referents provides insight into the preliminary states of language processing as decisions unfold. Part of its impact is the impressive data visualizations which reveal the millisecond-by-millisecond timecourse of processing, and supports interactive activation and competition models of word recognition. I start this talk with a brief review of this paradigm and the fundamental insights about language that it has supported.

All theoretical and statistical approaches to working with this data make the tacit assumption that the timecourse of fixations is closely related to the underlying activation in the system. However, given the serial nature of fixations and their long refractory period, it is unclear how closely the observed dynamics of the fixation curves are actually coupled to the underlying dynamics of activation. This kind of chunky stochastic system may have unpredictable consequences for the smooth data that are typically averaged.

It is difficult to precisely characterize such as system in a simple probability distribution. Thus, I investigate this assumption with a series of simulations. Each simulation starts with a set of true underlying activation functions and generates simulated fixations using a simple stochastic sampling procedure that respects the sequential nature of fixations. I then analyzed the results to determine the conditions under which the observed fixations curves match the underlying functions, the reliability of the observed data, and the implications for Type I Error and power. These simulations demonstrate that even under the simplest fixation-based models, observed fixation curves are systematically biased relative to the underlying activation functions, and they are substantially noisier, with important implications for reliability and power. I then present a potential generative model that may ultimately overcome many of these issues.

Event Date: 
January 31, 2022 - 3:30pm to 4:30pm
113 MLH
Calendar Category: 
Seminar Category: 
Mathematical Biology