Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in L2 processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English - L2 Spanish learners' eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844092 | PMC |
http://dx.doi.org/10.3389/fpsyg.2020.607621 | DOI Listing |
Comput Biol Med
December 2024
Aerospace Hi-tech Holding Group Co., LTD, Harbin, Heilongjiang, 150060, China.
CNN-based techniques have achieved impressive outcomes in medical image segmentation but struggle to capture long-term dependencies between pixels. The Transformer, with its strong feature extraction and representation learning abilities, performs exceptionally well within the domain of medical image partitioning. However, there are still shortcomings in bridging local to global connections, resulting in occasional loss of positional information.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Center for Technology Experience, AIT - Austrian Institute of Technology, Vienna, Austria.
Int J Comput Assist Radiol Surg
October 2024
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Purpose: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance.
View Article and Find Full Text PDFPLoS One
June 2024
Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Language is rooted in our ability to compose: We link words together, fusing their meanings. Links are not limited to neighboring words but often span intervening words. The ability to process these non-adjacent dependencies (NADs) conflicts with the brain's sampling of speech: We consume speech in chunks that are limited in time, containing only a limited number of words.
View Article and Find Full Text PDFJ Exp Psychol Gen
April 2024
Department of Psychology and Behavioral Sciences, Zhejiang University.
Humans have evolved the sophisticated ability to extract social relations embedded in interactive entities. One typical demonstration is a social chunking phenomenon wherein the cognitive system chunks individual actions into a unified episode basing on perceived interactive actions. However, the mechanisms underlying social chunking remain to be elucidated.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!