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The Role of Prominence Information in the Real‐Time Comprehension of Transitive Constructions: A Cross‐Linguistic Approach
Author(s) -
BornkesselSchlesewsky Ina,
Schlesewsky Matthias
Publication year - 2009
Publication title -
language and linguistics compass
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 44
ISSN - 1749-818X
DOI - 10.1111/j.1749-818x.2008.00099.x
Subject(s) - transitive relation , animacy , argument (complex analysis) , comprehension , linguistics , definiteness , interpretation (philosophy) , focus (optics) , generative grammar , sentence processing , computer science , word order , cognition , psychology , artificial intelligence , mathematics , philosophy , biochemistry , chemistry , physics , combinatorics , neuroscience , optics
Approaches to language processing have traditionally been formulated with reference to general cognitive concepts (e.g. working memory limitations) or have based their representational assumptions on concepts from generative linguistic theory (e.g. structure determines interpretation). Thus, many well‐established generalisations about language that have emerged from cross‐linguistic/typological research have not as yet had a major influence in shaping ideas about online processing. Here, we examine the viability of using typologically motivated concepts to account for phenomena in online language comprehension. In particular, we focus on the comprehension of simple transitive sentences (i.e. sentences involving two arguments/event participants) and cross‐linguistic similarities and differences in how they are processed. We argue that incremental argument interpretation in these structures is best explained with reference to a range of cross‐linguistically motivated, hierarchically ordered information types termed ‘prominence scales’ (e.g. animacy, definiteness/specificity, case marking and linear order). We show that the assumption of prominence‐based argument processing can capture a wide range of recent neurocognitive findings, as well as deriving well‐known behavioural results.