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Objective: Research demonstrates that college educated, English language dominant bilinguals underperform relative to English speaking monolinguals on tests of verbal ability. We investigated whether accepting responses in their two languages would reveal improved performance in bilinguals, and whether such improvement would be of sufficient magnitude to demonstrate the same performance level as monolinguals. Method: Participants were college students attending the same university. Spanish-English bilinguals were compared to English speaking monolinguals on the Bilingual Verbal Ability Tests (BVAT), which include Picture Vocabulary, Oral Vocabulary, and Verbal Analogies. Results: When given the opportunity to respond in Spanish to items failed in English, bilinguals obtained significantly higher scores on all three subtests, and their performance matched that of monolinguals on Oral Vocabulary and Verbal Analogies. Conclusion: An “either-language” scoring approach may enable optimal measurement of verbal abilities in bilinguals. We provide normative data for use in applying the either-language scoring approach on subtests of the BVAT. We discuss the findings in the context of clinical assessment. 

Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain’s electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization.