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How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis
(2021)
The use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several alternative approaches are demonstrated for reporting inconsistencies between the data and a theoretical prediction. The key here is to focus on committing to a falsifiable prediction, on quantifying uncertainty statistically, and learning to accept the fact that - in almost all practical data analysis situations - we can only draw uncertain conclusions from data, regardless of whether we manage to obtain statistical significance or not. A focus on uncertainty quantification is likely to lead to fewer excessively bold claims that, on closer investigation, may turn out to be not supported by the data.
We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.
Preregistration is an open science practice that requires the specification of research hypotheses and analysis plans before the data are inspected. Here, we discuss the benefits of preregistration for hypothesis-driven, confirmatory bilingualism research. Using examples from psycholinguistics and bilingualism, we illustrate how non-peer reviewed preregistrations can serve to implement a clean distinction between hypothesis testing and data exploration. This distinction helps researchers avoid casting post-hoc hypotheses and analyses as confirmatory ones. We argue that, in keeping with current best practices in the experimental sciences, preregistration, along with sharing data and code, should be an integral part of hypothesis-driven bilingualism research.
An important aspect of aphasia is the observation of behavioral variability between and within individual participants. Our study addresses variability in sentence comprehension in German, by testing 21 individuals with aphasia and a control group and involving (a) several constructions (declarative sentences, relative clauses and control structures with an overt pronoun or PRO), (b) three response tasks (object manipulation, sentence-picture matching with/without self-paced listening), and (c) two test phases (to investigate test-retest performance). With this systematic, large-scale study we gained insights into variability in sentence comprehension. We found that the size of syntactic effects varied both in aphasia and in control participants. Whereas variability in control participants led to systematic changes, variability in individuals with aphasia was unsystematic across test phases or response tasks. The persistent occurrence of canonicity and interference effects across response tasks and test phases, however, shows that the performance is systematically influenced by syntactic complexity.