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Information and learning in processing adjective inflection.

Dušica Filipović Đurđević1, Petar Milin2

  • 1Department of Psychology, Faculty of Philosophy, University of Novi Sad, Novi Sad, Serbia.

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Summary
This summary is machine-generated.

This study reveals that Serbian adjective processing relies on both information theory and discrimination learning principles. These two frameworks explain complex morphology through basic learning mechanisms, highlighting language processing efficiency.

Keywords:
AdjectivesDiscrimination learningInflectional morphologyPrepositional phrasesRelative entropy

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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Morphological Processing

Background:

  • Inflected Serbian adjective forms present a complex morphological system.
  • Quantitative linguistic measures can be derived from information theory and discrimination learning frameworks.
  • Understanding these processing mechanisms is key to psycholinguistics and computational linguistics.

Purpose of the Study:

  • To investigate the processing of inflected Serbian adjective forms.
  • To integrate quantitative linguistic measures from information theory and discrimination learning.
  • To model processing latencies using regression analysis.

Main Methods:

  • Elicited processing latencies using a visual lexical decision task with inflected adjectival forms.
  • Derived quantitative descriptions of the inflectional system from information theory and discrimination learning.
  • Fitted two separate regression models to the processing latencies.

Main Results:

  • The information theory model showed sensitivity to syntagmatic and paradigmatic variation, with paradigmatic information modulated by lemma frequency.
  • The discrimination learning model revealed a complex pattern involving learning-based variables.
  • Both models showed strikingly similar patterns, with 85.83% shared variance in predictions.

Conclusions:

  • Complex morphological phenomena can emerge from basic discrimination learning principles.
  • Discriminative learning about inflectional paradigms, classes, and contextual embedding explains language processing efficiency.
  • Findings shed light on the complexity of naturally emerged language systems.