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Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in L2 processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English – L2 Spanish learners’ eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers. 

This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition the model on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters yielding model predictions in close agreement with the observations. The updated ensemble of model simulations has predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development assuming knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries, i.e., the four European countries Norway, England, The Netherlands, and France; the province of Quebec in Canada; the South American countries Argentina and Brazil; and the four US states Alabama, North Carolina, California, and New York. These countries and states all have vastly different developments of the epidemic, and we could accurately model the SARS-CoV-2 outbreak in all of them. We realize that more complex models, e.g., with regional compartments, may be desirable, and we suggest that the approach used here should be applicable also for these models.