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<p>Disfluency can occur when a speaker faces difficulty in language production, but it is also as a strategy to stall for time and create an illusion of continuity in speech. To better understand the origin of disfluency, the current study used a drift diffusion model (DDM) in a context of semantic competition. We developed a speeded version of the picture–word interference paradigm, in which participants name pictures while ignoring distractor words. We applied the DDM approach to data from related vs. unrelated distractors but also from fluent versus disfluent answers in the related condition (e.g., “apple” vs. “hm...&#xa0;a-apple”). All differences between conditions were mapped onto the drift rate parameter. Unrelated distractor words resulted in a higher drift rate compared with semantic distractors, and correct fluent answers resulted in a higher drift rate compared with correct disfluent answers. Our findings suggest that semantic interference taps into the process of spreading activation through the lexical-semantic system. Most importantly, disfluency in the picture-naming task reflects competition between response options and is not a strategy from the speaker to stall for time to accurately name the picture.</p>

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Disfluencies reflect a... uh... competition between response options: Evidence from a drift diffusion analysis

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • Aurélie Pistono,
  • Mehdi Senoussi,
  • Robert J. Hartsuiker

摘要

Disfluency can occur when a speaker faces difficulty in language production, but it is also as a strategy to stall for time and create an illusion of continuity in speech. To better understand the origin of disfluency, the current study used a drift diffusion model (DDM) in a context of semantic competition. We developed a speeded version of the picture–word interference paradigm, in which participants name pictures while ignoring distractor words. We applied the DDM approach to data from related vs. unrelated distractors but also from fluent versus disfluent answers in the related condition (e.g., “apple” vs. “hm... a-apple”). All differences between conditions were mapped onto the drift rate parameter. Unrelated distractor words resulted in a higher drift rate compared with semantic distractors, and correct fluent answers resulted in a higher drift rate compared with correct disfluent answers. Our findings suggest that semantic interference taps into the process of spreading activation through the lexical-semantic system. Most importantly, disfluency in the picture-naming task reflects competition between response options and is not a strategy from the speaker to stall for time to accurately name the picture.