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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Related Experiment Video

Updated: Apr 15, 2026

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
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Linguistic Markers of Inference Generation While Reading.

Virginia Clinton1,2, Sarah E Carlson3, Ben Seipel4

  • 1University of North Dakota, 319 Harvard St., Grand Forks, ND, 58202, USA. vclinton@wisc.edu.

Journal of Psycholinguistic Research
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Summary

Students

Keywords:
Cognitive processesCoherenceInferenceLinguistic markers

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Area of Science:

  • Psycholinguistics
  • Educational Psychology
  • Computational Linguistics

Background:

  • Words serve as linguistic markers for psychological constructs.
  • Understanding text connections, or inference generation, is crucial for reading comprehension.
  • Previous research has not fully explored the link between specific word categories and inference types.

Purpose of the Study:

  • To investigate the relationship between word usage and inference generation during reading.
  • To determine if specific word categories predict elaborative and bridging inferences.
  • To explore the utility of computational text analysis in studying reading processes.

Main Methods:

  • Collected think-aloud data from 3rd-5th grade students reading narrative texts.
  • Hand-coded data to identify instances of inference generation.
  • Utilized Linguistic Inquiry and Word Count (LIWC) to analyze word use in categories like cognitive mechanisms, nonfluencies, and function words.

Main Results:

  • Cognitive mechanism words positively predicted elaborative inference generation (connecting to background knowledge).
  • Nonfluencies negatively predicted bridging inference generation (connecting within the text).
  • Function words did not significantly predict either type of inference generation.

Conclusions:

  • Word use, particularly cognitive mechanisms and nonfluencies, is associated with distinct types of reading inference generation.
  • Findings support a cognitive reflection model of reading comprehension.
  • Results have implications for developing intelligent tutoring systems and automated inference identification tools.