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This study explored how grammatical gender is assigned in French word production. Findings show that word properties influence gender assignment, especially for language learners, suggesting models need to account for variable access to gender information.

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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Linguistic Theory

Background:

  • Grammatical gender assignment is a complex process in language production.
  • The role of lexical and sublexical cues in this process is not fully understood.
  • Previous models may not fully account for differential access to gender information.

Purpose of the Study:

  • To investigate the influence of sublexical and lexical cues on grammatical gender assignment during word production in French.
  • To compare the effects of these cues on native French speakers and English-speaking learners of French.
  • To examine the relationship between gender classification speed in recognition and production.

Main Methods:

  • Experiment 1: Native French speakers classified the gender of mentally evoked picture names.
  • Experiment 2: Participants classified the gender of translated English words, using an improved methodology.
  • Analysis involved comparing the influence of word-based cues across different participant groups and tasks.

Main Results:

  • Experiment 1 showed no significant effect of sublexical or lexical cues on gender assignment.
  • Experiment 2 revealed that English-speaking learners of French were strongly influenced by these cues, while native speakers showed a weaker effect.
  • Both groups exhibited a correlation between slow gender classification in recognition and production, and similar error rates across tasks.

Conclusions:

  • Grammatical gender information is not equally or instantly available for all words upon lemma access.
  • The formal properties of a word significantly impact the speed of accessing its grammatical gender.
  • Current language production models may require revision to include mechanisms for differential access to gender information based on word characteristics.