A growing literature uses event-related potentials (ERPs) to investigate novel metaphor processing as a window into creative processes like conceptual expansion. Modulations of the N400 generally indicate that while novel metaphors are initially processed as semantic anomalies, after a connection is found relating the concepts, they pattern more with literal sentences. Existing research largely focuses on monolinguals, but less is known about novel metaphor processing in bilinguals' second language (L2). Here, we combine robust single-trial ERPs and behavioral measures to investigate how L2 English users process full-sentence novel metaphors. We compare our results to a previous study with English monolinguals using the same experimental design to test three competing hypotheses: L2 conceptual expansion will be more effortful than, more efficient than, or similar to L1. Group differences suggest more effortful processing for L2 English users than monolinguals. Behaviorally, L2 users show more sentence evaluation errors than monolinguals, particularly for anomalous sentences. ERP results in L2 users reveal an N400 semantic anomaly effect at the sentence-final position, with no significant differences between metaphorical and literal or metaphorical and anomalous sentences. Monolinguals show a graded N400 effect, with significant differences between literal and anomalous as well as metaphorical and anomalous sentences. By comparing L2 users' results with monolingual English users and using naturalistic full-sentence structures, our findings contribute to the emergent literature on L2 novel metaphor processing and conceptual expansion while also unraveling the cognitive challenges associated with incremental processing and integration of L2 metaphorical sentences.

Creativity research commonly involves recruiting human raters to judge the originality of responses to divergent thinking tasks, such as the alternate uses task (AUT). These manual scoring practices have benefited the field, but they also have limitations, including labor-intensiveness and subjectivity, which can adversely impact the reliability and validity of assessments. To address these challenges, researchers are increasingly employing automatic scoring approaches, such as distributional models of semantic distance. However, semantic distance has primarily been studied in English-speaking samples, with very little research in the many other languages of the world. In a multilab study (N= 6,522 participants), we aimed to validate semantic distance on the AUT in 12 languages: Arabic, Chinese, Dutch, English, Farsi, French, German, Hebrew, Italian, Polish, Russian, and Spanish. We gathered AUT responses and human creativity ratings (N= 107,672 responses), as well as criterion measures for validation (e.g., creative achievement).We compared two deep learning-based semantic models—multilingual bidirectional encoder representations from transformers and cross-lingual language model RoBERTa—to compute semantic distance and validate this automated metric with human ratings and criterion measures. We found that the top-performing model for each language correlated positively with human creativity ratings, with correlations ranging from medium to large across languages. Regarding criterion validity, semantic distance showed small-to-moderate effect sizes (comparable to human ratings) for openness, creative behavior/achievement, and creative self-concept. We provide open access to our multilingual dataset for future algorithmic development, along with Python code to compute semantic distance in 12 languages. © 2023 American Psychological Association

¿Cómo reacciona la ciudadanía en un sistema político polarizado ante una emergencia como la pandemia de la COVID-19?, ¿cómo procesa la ciudadanía las narrativas polarizadas que están en conflicto?, y ¿qué imagen se forman de la gestión política de la amenaza de la pandemia? En EE. UU, hay que retrotraerse a la epidemia de la polio de hace 70 años para encontrar una emergencia sanitaria como la pandemia de la COVID-19. No obstante, hay importantes diferencias; en la década de 1950, el clima político de los EE.UU. era mucho más uniforme y consensuado que el actual, profundamente dividido y polarizado. Este trabajo utiliza datos de un estudio longitudinal realizado durante 35 años en personas jóvenes de la Generación X (ahora ya en la cuarentena) y datos provenientes de encuestas realizadas en Estados Unidos durante tres décadas, con el propósito de examinar los patrones de adquisición de la información en la comprensión de una nueva amenaza. Nuestro análisis de los últimos 35 años de la Generación X muestra que, en las elecciones de 2020, el factor predictivo del voto individual que tuvo más fuerza fue una ideología política polarizada, pero, aquellas personas que contaban con una mejor comprensión del coronavirus fueron más críticas con la gestión de la pandemia de la COVID-19 que realizó la administración de Donald Trump y estuvieron más predispuestas a votar por Joe Biden que a votar por Trump. Un análisis paralelo de una muestra probabilística representativa de personas adultas estadounidenses en 2020 reveló el mismo patrón de influencia del partidismo ideológico, la comprensión del coronavirus y la evaluación de la administración de Trump. Los resultados muestran que el conocimiento y la comprensión pueden proporcionar un efecto moderador crítico en un sistema político polarizado y dividido.