Abstract
For most of the history of psychology, data analysis has focused on techniques like ANOVA and regression, characterize the observed structure in a dataset with respect to a given set of questions or factors. Such models make simplifying assumptions about the data as a while, while allowing targeted questions about the observed differences. However, in the last decade the field has moved to models starting from a different set of assumptions. These models characterize the underlying probability structure that gave rise to all of the data, and then use this to make experimental inferences. Nowhere is this contrast more apparent than in the analysis of rich timeseries data such as those generated by techniques like eye-tracking, pupillometry, EEG and so forth. This talk explores this contrast in the context of a popular technique in psycholinguistics and language acquisition: the visual world paradigm, which uses eye-movements in a semi-naturalistic task to make inferences about the millisecond-by-millisecond dynamics of language processing. Here, the auto-correlated timeseries and the incredibly rich dataset seems to appear to require highly complex approaches to analysis such as generalized additive models and growth curves that can fully capture this variance. But is this necessary? This talk challenges this approach on two grounds. First, while the movement toward greater rigor and reproducibility seems to favor more statistical precision, others have argued that equally important is a well specified linking function or derivation chain that can yield precise confirmatory predictions from a computational model. I argue that existing statistical approaches are often overspecified and suffer from too much complexity permit this kind of clarity. Instead, I argue for a return to an older approach: inference or index approaches to analysis and I present several examples of advances in this approach that push beyond simple ANOVAs. Second, I present a series of Monte-Carlo simulations that directly investigate the derivation chain to ask what is the nature of the timeseries that is the basis of analysis? While most approaches (including my own) simply average the series of fixations across hundreds of trials, my simulations suggest that the fact that these curves drive from series of discrete fixations may have profound consequences for how closely they align to the underlying decision process in the language system. Without understanding the eye-movement system (and even with it), it may not be safe to assume that these common visualizations—the basis of most complex statistical approaches—reflect the fine-grained dynamics of language. Again, this argues for simpler index approaches, and present power and reliability analyses from the Monte Carlos that suggest they can be used with no loss of power or increase in type I error. Crucially, these approaches may permit stronger and clearer hypothesis-driven analysis that more complex approaches.
Bio
Bob McMurray is an F. Wendell Miller Professor of Psychological and Brain Sciences at the University of Iowa. He has secondary appointments in the Depts. of Linguistics, Otolaryngology and Communication Sciences and Disorders. He received his Ph.D. in Brain and Cognitive Sciences from the University of Rochester in 2004. His work examines the spoken and written word recognition and how these skills develop and differ in a variety of populations including people with language and reading illness, hearing loss, and aging. He uses a variety of techniques including those of cognitive and developmental psychology (particularly eye-tracking), cognitive neuroscience (EEG and MRI) and computational modeling.
This talk is open for everyone. No need for registration.