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Updated: May 31, 2026

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Published on: May 9, 2019
Neurophysiology of Hungarian subject-verb dependencies with varying intervening complexity
Hajnal Jolsvai1, Elyse Sussman, Roland Csuhaj
1Psychology Department, Cornell University, Ithaca, NY 14853–7601, USA. jolsvai@cornell.edu
Abstract:
Non-adjacent dependencies are thought to be more costly to process than sentences wherein dependents immediately follow or precede what they depend on. In English locality effects have been revealed, while in languages with rich case marking (German and Hindi) sentence final structures show anti-locality-effects. The motivation of the current study is to test whether locality effects can be directly applied to a typologically different language than those investigated so far. Hungarian is a "topic prominent" language; it permits a variation of possible word sequencing for semantic reasons, including SVO word order. Hungarian also has a rich morphological system (e.g., rich case system) and postpositions to indicate grammatical functions. In the present ERP study, Hungarian subject-verb dependencies were compared by manipulating the mismatch of number agreement between the sentence's initial noun phrase and the sentence's final intransitive verb as well as the complexity of the intervening sentence material, interrupting the dependencies. Possible lexical class and frequency or cloze-probability effects for the first two words of the intervening sentence material were revealed when used separate baseline for each word, while at the third word of the intervening material as well as at the main verb ERPs were not modulated by complexity but at the verb ERPs were enhanced by grammaticality. Ungrammatical sentences enlarged the amplitude of both LAN and P600 components at the main verb. These results are in line with studies suggesting that the retrieval of the first element of a dependency is not influenced by distance from the second element, as the first element is directly accessible when needed for integration (e.g., McElree, 2000).
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