VACs in L1 Knowledge and Processing

Abstract

Language LearningVolume 66, Issue S1 p. 99-121 Article VACs in L1 Knowledge and Processing First published: 25 May 2016 https://doi.org/10.1111/lang.4_12177Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher's website: Filename Description lang4_12177-sup-0001-SupMat.pdf1.3 MB Table S4.1 A Contingency Table Showing the Four Possible Combinations of Events Showing the Presence or Absence of a Target Cue and an Outcome Table S4.2 Coefficients for the Multiple Regression Analysis of (i) Expt. 1 log10 Frequencies of Verb Types Generated in a VAC Frame Against (ii) log10 Frequencies of that Verb Type in that VAC Frame in the BNC, (iii) log 10 ΔPcw Association Strength of that Verb Given that VAC in the BNC, (iv) log10 Betweenness Centrality of that Verb in that VAC Semantic Network from the BNC Data, Pooled Across the 17 VACs Analyzed Table S4.3 Coefficients for the Multiple Regression Analysis of (i) log10 Frequencies of Verb Types Generated in 1 Minute for a VAC Frame Against (ii) log10 Frequencies of that Verb Type in that VAC Frame in the BNC, (iii) log 10 ΔPcw Association Strength of that Verb Given that VAC in the BNC, (iv) log10 Betweenness Centrality of that Verb in that VAC Semantic Network from the BNC Data, Pooled Across the 17 VACs Analyzed Figure S4.1 Scatterplot matrix of (i) Experiment 1 log10 frequencies of verb types generated for a VAC frame against (ii) log10 frequencies of that verb type in that VAC frame in the BNC, (iii) log 10 ΔPcw association strength of that verb given that VAC in the BNC, (iv) log10 betweenness centrality of that verb in that VAC semantic network from the BNC data, pooled across the 17 VACs analyzed. Figure S4.2 Experiment 2 log10 verb generation frequency against log10 Verb Frequency in that VAC in the BNC for VACs ‘V across n’ to ‘V into n.’ Verb font size is proportional to overall verb token frequency in the BNC as a whole. Figure S4.3 Experiment 2 log10 verb generation frequency against log10 Verb Frequency in that VAC in the BNC for VACs ‘V like n’ to ‘V with n.’ Verb font size is proportional to overall verb token frequency in the BNC as a whole. Figure S4.4 Scatterplot matrix of (i) Experiment 2 log10 frequencies of verb types generated in 1 minute for a VAC frame against (ii) log10 frequencies of that verb type in that VAC frame in the BNC, (iii) log 10 ΔPcw association strength of that verb given that VAC in the BNC, (iv) log10 betweenness centrality of that verb in that VAC semantic network from the BNC data, pooled across the 17 VACs analyzed. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume66, IssueS1Special Issue: Language Learning Monograph Series: Usage-based Approaches to Language Acquisition and Processing: Cognitive and Corpus Investigations of Construction GrammarJune 2016Pages 99-121 RelatedInformation