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The role of transitional probabilities in word holistic processing.

Paulo Ventura, Francisco Cruz1, Alexandre Pereira2

  • 1Universidade de Lisboa, Portugal.

Perception
|September 17, 2024
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Summary

This study investigated how sublexical word properties, like bigram transition probabilities, influence visual word recognition. Findings suggest these properties play a role in the holistic processing of words, enhancing reading expertise.

Keywords:
bigramscomposite taskholistic processingtransitional probabilitiesvisual words

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Area of Science:

  • Cognitive psychology
  • Psycholinguistics
  • Visual word recognition

Background:

  • Visual word recognition relies on a holistic attentional strategy, optimal for processing distributed information.
  • Perceptual expertise enhances this holistic processing with experience.
  • The contribution of sublexical word properties to holistic processing remains underexplored.

Purpose of the Study:

  • To investigate the role of sublexical properties, specifically bigram transition probabilities, in visual word holistic processing.
  • To determine if sublexical information influences the development or efficiency of holistic word recognition.

Main Methods:

  • Utilized the composite task, a standard method in holistic processing research.
  • Employed four-letter disyllabic words designed to manipulate syllable cohesiveness and inter-syllable bigram properties.
  • Analyzed the impact of bigram transition probabilities on word recognition performance.

Main Results:

  • Preliminary evidence suggests sublexical properties, such as bigram transition probabilities, contribute to holistic word processing.
  • The cohesiveness of bigrams within and between syllables appears to influence the holistic strategy.

Conclusions:

  • Sublexical properties are not merely passive information but actively participate in the holistic processing of visual words.
  • This finding extends the perceptual expertise framework by incorporating fine-grained linguistic information into word recognition models.