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An exploratory data analysis of word form prediction during word-by-word reading

Significance Complex biological systems do not merely react, they anticipate. In 2005, the human-language comprehension system was considered an exception. We concluded not, based on our recordings of electrical brain activity measured before the critical words arrived during sentence reading, described in a now widely cited report. This, and the emergence of the “statistical crisis” in psychology, led to a large-scale replication attempt that failed. This prompted us to revisit the issue by analyzing our original data and two replication extensions with an exploratory data analysis (EDA) approach, enabled by advances in scientific computing technology. Our original conclusion was supported: Brains can anticipate specific upcoming words. We offer this as a case study in EDA for cognitive neurophysiology, more generally. In 2005, we reported evidence indicating that upcoming phonological word forms—e.g., kite vs. airplane—were predicted during reading. We recorded brainwaves (electroencephalograms [EEGs]) as people read word-by-word and then correlated the predictability in context of indefinite articles that preceded nouns (a kite vs. an airplane) with the average event-related brain potentials (ERPs) they elicited [K. A. DeLong, T. P. Urbach, M. Kutas, Nat. Neurosci. 8, 1117–1121 (2005)]. Amid a broader controversy about the role of word-form prediction in comprehension, those findings were recently challenged by a failed putative direct replication attempt [M. S. Nieuwland et al., eLife 7, e33468 (2018); nine labs, one experiment, and 2.6e4 observations]. To better understand the empirical justification for positing an association between prenominal article predictability and scalp potentials, we conducted a wide-ranging exploratory data analysis (EDA), pooling our original data with extant data from two followup studies (one lab, three experiments, and 1.2e4 observations). We modeled the time course of article predictability in the single-trial data by fitting linear mixed-effects regression (LMER) models at each time point and scalp location spanning a 3-s interval before, during, and after the article. Model comparisons based on Akaike information criteria (AIC) and slope-regression ERPs [rERPs; N. J. Smith, M. Kutas, Psychophysiology 52, 157–168 (2015)] provide substantial empirical support for a small positive association between article predictability and scalp potentials approximately 300 to 500 ms after article onset, predominantly over bilateral posterior scalp. We think this effect may reasonably be attributed to prediction of upcoming word forms.