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Related Experiment Video

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A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
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Working memory differences in long-distance dependency resolution.

Bruno Nicenboim1, Shravan Vasishth1, Carolina Gattei2

  • 1Department of Linguistics, University of Potsdam Potsdam, Germany.

Frontiers in Psychology
|April 9, 2015
PubMed
Summary
This summary is machine-generated.

Sentence processing differs in SOV languages, showing antilocality effects. Individual working memory capacity influences these effects, supporting a combined activation- and prediction-based model for dependency resolution.

Keywords:
DLTSpanishactivationantilocalityexpectationindividual differenceslocalityworking memory capacity

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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Locality effects, where increased distance between words increases processing effort, are typically linked to working memory constraints.
  • However, SOV languages exhibit antilocality effects, challenging traditional models.
  • Existing theories include expectation-based and activation-based models of sentence processing.

Purpose of the Study:

  • To investigate the factors underlying antilocality effects in Spanish, an SOV language.
  • To differentiate between expectation-based and memory-based accounts of sentence processing.
  • To examine the role of individual differences in working memory capacity.

Main Methods:

  • Eye-tracking study
  • Self-paced reading study
  • Analysis of individual differences in working memory capacity

Main Results:

  • Antilocality effects were observed primarily in high-capacity readers, aligning with expectation-based accounts.
  • Facilitation from increased dependency length was greater when intervening material modified the head, supporting the activation-based model.
  • Low-capacity readers showed increased regressive saccades or a speedup, suggesting memory load impacts processing strategies.

Conclusions:

  • Individual differences in working memory capacity significantly influence dependency resolution.
  • The findings suggest a hybrid model, integrating the activation-based model with predictive mechanisms, best explains sentence processing phenomena.
  • The study highlights the complexity of language processing and the interplay between memory and prediction.