TY  - JOUR
A1  - Nicenboim, Bruno
A1  - Vasishth, Shravan
A1  - Rösler, Frank
T1  - Are words pre-activated probabilistically during sentence comprehension?
BT  - evidence from new data and a Bayesian random-effects meta-analysis using publicly available data
JF  - Neuropsychologia : an international journal in behavioural and cognitive neuroscience
N2  - Several studies (e.g., Wicha et al., 2003b; DeLong et al., 2005) have shown that readers use information from the sentential context to predict nouns (or some of their features), and that predictability effects can be inferred from the EEG signal in determiners or adjectives appearing before the predicted noun. While these findings provide evidence for the pre-activation proposal, recent replication attempts together with inconsistencies in the results from the literature cast doubt on the robustness of this phenomenon. Our study presents the first attempt to use the effect of gender on predictability in German to study the pre-activation hypothesis, capitalizing on the fact that all German nouns have a gender and that their preceding determiners can show an unambiguous gender marking when the noun phrase has accusative case. Despite having a relatively large sample size (of 120 subjects), both our preregistered and exploratory analyses failed to yield conclusive evidence for or against an effect of pre-activation. The sign of the effect is, however, in the expected direction: the more unexpected the gender of the determiner, the larger the negativity. The recent, inconclusive replication attempts by Nieuwland et al. (2018) and others also show effects with signs in the expected direction. We conducted a Bayesian random-ef-fects meta-analysis using our data and the publicly available data from these recent replication attempts. Our meta-analysis shows a relatively clear but very small effect that is consistent with the pre-activation account and demonstrates a very important advantage of the Bayesian data analysis methodology: we can incrementally accumulate evidence to obtain increasingly precise estimates of the effect of interest.
KW  - ERP
KW  - pre-activation
KW  - predictions
KW  - grammatical gender
KW  - Bayesian meta-analysis
Y1  - 2020
U6  - https://doi.org/10.1016/j.neuropsychologia.2020.107427
SN  - 0028-3932
SN  - 1873-3514
VL  - 142
PB  - Elsevier Science
CY  - Oxford
ER  -