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Investigating variability in morphological processing with Bayesian distributional models.

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This study on word processing in non-native speakers found that inflectional priming increases reaction time variability more than derivational priming. Analyzing performance variability offers deeper insights into language processing models.

Keywords:
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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Processing morphologically complex words is crucial for language comprehension.
  • Non-native speakers exhibit significant variability in language performance.
  • Traditional analyses often focus on average effects, potentially obscuring important performance differences.

Purpose of the Study:

  • To investigate masked morphological priming effects for derived and inflected English words in non-native speakers.
  • To explore how different word forms influence reaction time variability.
  • To demonstrate the value of analyzing performance variability beyond mean effects in language processing research.

Main Methods:

  • Utilized masked morphological priming with derived ('printer') and inflected ('printed') words priming their stems ('print').
  • Employed Bayesian distributional models to analyze reaction times using a shifted-lognormal distribution.
  • Assessed the impact of priming on both the mean (mu) and standard deviation (sigma) of response times.

Main Results:

  • Both derived and inflected primes showed similar effects on mean reaction times.
  • Inflectional priming significantly increased response time variability (sigma) compared to derivational priming.
  • Results align with prior research indicating greater variability in non-native processing of inflected forms.

Conclusions:

  • Performance variability is a critical factor in understanding language processing, particularly in non-native speakers.
  • Analyzing distributional parameters beyond the mean (e.g., standard deviation) can reveal distinct effects of linguistic manipulations.
  • This approach enhances psycholinguistic models by disentangling subtle processing differences and informing theories of word recognition.