Anecdotal evidence suggests that learning a new foreign language (FL) makes you forget previously learned FLs. To seek empirical evidence for this claim, we tested whether learning words in a previously unknown L3 hampers subsequent retrieval of their L2 translation equivalents. In two experiments, Dutch native speakers with knowledge of English (L2), but not Spanish (L3), first completed an English vocabulary test, based on which 46 participant-specific, known English words were chosen. Half of those were then learned in Spanish. Finally, participants' memory for all 46 English words was probed again in a picture naming task. In Experiment 1, all tests took place within one session. In Experiment 2, we separated the English pre-test from Spanish learning by a day and manipulated the timing of the English post-test (immediately after learning vs. 1 day later). By separating the post-test from Spanish learning, we asked whether consolidation of the new Spanish words would increase their interference strength. We found significant main effects of interference in naming latencies and accuracy: Participants speeded up less and were less accurate to recall words in English for which they had learned Spanish translations, compared with words for which they had not. Consolidation time did not significantly affect these interference effects. Thus, learning a new language indeed comes at the cost of subsequent retrieval ability in other FLs. Such interference effects set in immediately after learning and do not need time to emerge, even when the other FL has been known for a long time.
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http://dx.doi.org/10.1177/17470218231181380 | DOI Listing |
Palliat Support Care
January 2025
Department of Theology and Religious Education, College of Liberal Arts, Manila, Philippines.
Teaching death, spirituality, and palliative care equips students with critical skills and perspectives for holistic patient care. This interdisciplinary approach fosters empathy, resilience, and personal growth while enhancing competence in end-of-life care. Using experiential methods like simulations and real patient interactions, educators bridge theory and practice.
View Article and Find Full Text PDFPalliat Support Care
January 2025
Department of Pediatrics, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
Objectives: Explore humanitarian healthcare professionals' (HCPs) perceptions about implementing children's palliative care and to identify their educational needs and challenges, including learning topics, training methods, and barriers to education.
Methods: Humanitarian HCPs were interviewed about perspectives on children's palliative care and preferences and needs for training. Interviews were transcribed, coded, and arranged into overarching themes.
Brief Bioinform
November 2024
School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFAccurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.
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