Past research suggests that novel word learning is facilitated by multimodal contexts, which enrich semantic representations and strengthen memory traces. We explored whether environmental sounds (e.g., a creaking door) facilitate foreign language (FL) word learning. In all, 36 Spanish-speaking natives learned 60 written Spanish–FL word pairs, each accompanied by one of three sound conditions: a congruent sound matching the word pairs’ denotation, a meaningless tone or silence. Participants then completed a semantic priming and lexical decision task where reaction times and accuracy were collected. Performance was similar for congruent sound and tone conditions and, compared to silence, showed lower accuracy in the lexical decision task and a marginal benefit in the semantic task. These findings suggest that environmental sounds can influence learning, with varying effects depending on the task. Results are discussed in terms of current language learning models.

Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obtained from data assimilation methods, represent the state-of-the-art for sea-surface wind speed monitoring but may be biased by model errors and their spatial resolution is not competitive with satellite products. In this work, we propose a scheme based on both data assimilation and deep learning concepts to process spatiotemporally heterogeneous input sources to reconstruct high-resolution time series of spatial wind speed fields. This method allows to us make the most of the complementary information conveyed by the different sea-surface information typically available in operational settings. We use synthetic wind speed data to emulate satellite images, in situ time series and reanalyzed wind fields. Starting from these pseudo-observations, we run extensive numerical simulations to assess the impact of each input source on the model reconstruction performance. We show that our proposed framework outperforms a deep learning–based inversion scheme and can successfully exploit the spatiotemporal complementary information of the different input sources. We also show that the model can learn the possible bias in reanalysis products and attenuate it in the output reconstructions.