During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years ( M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

Vocabulary size seems to be affected by multiple factors, including those that belong to the properties of the words themselves and those that relate to the characteristics of the individuals assessing the words. In this study, we present results from a crowdsourced lexical decision megastudy in which more than 150,000 native speakers from around 20 Spanish-speaking countries performed a lexical decision task to 70 target word items selected from a list of about 45,000 Spanish words. We examined how demographic characteristics such as age, education level, and multilingualism affected participants' vocabulary size. Also, we explored how common factors related to words like frequency, length, and orthographic neighbourhood influenced the knowledge of a particular item. Results indicated important contributions of age to overall vocabulary size, with vocabulary size increasing in a logarithmic fashion with this factor. Furthermore, a contrast between monolingual and bilingual communities within Spain revealed no significant vocabulary size differences between the communities. Additionally, we replicated the standard effects of the words' properties and their interactions, accurately accounting for the estimated knowledge of a particular word. These results highlight the value of crowdsourced approaches to uncover effects that are traditionally masked by smallsampled in-lab factorial experimental designs.

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Learning a foreign language as an adult is a rewarding but challenging endeavor that entails accruing a massive vocabulary. The literature independently highlights that orthographic similarity and bilingual experience could facilitate foreign vocabulary acquisition. Here, we explored the combined effects of orthographic similarity and bilingual experience on foreign vocabulary learning using behavioral and computational approaches. Experiment 1 compared Spanish monolingual, Spanish-English, and Spanish-Basque bilingual participants when learning an artificial vocabulary with varying orthographic similarity to Spanish. The results indicated that similar words were easier to recognize and produce than dissimilar words, and both bilingual groups outperformed the monolingual group in learning the vocabulary, irrespective of orthographic similarity. In Experiment 2, we developed a neural network model that implemented a unified, distributed, and dynamic view of the orthographic lexicon to explain how these effects could emerge from exposure to bilingual input. We simulated adults’ orthographic lexicons by pre-training this architecture on monolingual and bilingual input. We then tested the monolingual and bilingual versions’ capacity to learn the novel words used in the behavioral task. The simulations reproduced the orthographic similarity effects and showed an overall advantage of experience with bilingual input, as observed in the behavioral results. The present study unifies the seemingly disparate effects of orthographic similarity and bilingual experience under a common computational framework, whereby distributed representations of orthographic word forms are stored in a unified space and dynamically modified by learning experiences.

Bilingual experience may confer advantages in statistical language learning tasks. Given that SL tasks can measure different aspects of foreign language learning, which aspects benefit from bilingual experience is still largely unexplored. Here, we compared a Spanish monolingual and two (Spanish-Basque and Spanish-English) bilingual groups across three well-established SL tasks. Each task targeted a different aspect of foreign language learning as a proxy—i.e., word segmentation, morphological rule generalization, and word-referent learning. In Experiment 1, we manipulated sub-lexical phonotactic patterns to vary the difficulty of three SL tasks, and the results showed no differences between the groups in word segmentation. In Experiment 2, we included non-adjacent dependencies to target affixal morphology rule learning, and again there were no differences between the groups. Finally, Experiment 3 addressed word learning using a more challenging audio-visual SL task combining exclusive and multiple word-referent mappings. We observed a bilingual experience effect only for the exclusive mappings but not for the multiple mappings. These results suggest that bilingual experience might mainly exert influences on statistical language learning at the lexical level. We discuss these findings by contextualizing SL as a cognitive mechanism, an experimental task, and a proxy for foreign language learning, highlighting the strengths and limitations in detecting bilingual experience effects.

Cognitive explorations have demonstrated the activation of plastic mechanisms in slow-growing brain lesions, generating structural and functional changes. Due to its incidence, it is essential to investigate the reorganization of functional areas in brain tumor patients as well as formulating new approaches for predicting patient quality of life after tumor resection. Following this perspective, we formulated an efficient methodology for postsurgical prognosis prediction, not only in terms of the structural damage but also to measure the neuroplastic changes associated with tumor appearence. Of note, most of previous studies employed a limited number of neuropsychological and clinical features for predicting patient prognosis. Our objective is to optimize the traditional model and to develop a method that can predict outcomes with high accuracy and identify the most significant features for cognitive impairment, working memory, executive control and language outcomes. Our approach is based on the inclusion of a large battery of neuropsychological tests as well as the introduction of grey and white matter morphological measures for model optimization. We employed Support Vector Machine (SVM), Decision Tree, and Naïve Bayes algorithms for testing the models and outcomes. Overall, SVM performance showed to be more accurate as compared to Decision Tree and Naïve Bayes. Specifically, we found that, by introducing connectivity variables (e.g., grey and white matter measures) Cognitive Status and Working Memory exhibited a predictive improvement. However, Language and Executive Control outcomes were not significantly predicted in none of the models. The importance of the present study resides in the employment of structural and functional variables for postsurgical outcome prediction. We found that connectivity variables are sensitive for predicting the postsurgical quality of life. Keywords: glioma, resection, postoperative outcome, prediction, neuropsychological test, mapping

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