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Task demands and segment priming effects in the naming task.

Bryan B Holbrook1, Alan H Kawamoto1, Qiang Liu1

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Summary
This summary is machine-generated.

Speech production may occur sequentially or in parallel. This study found that the "number of segments primed effect" depends on the participant

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Area of Science:

  • Psycholinguistics
  • Speech Production
  • Phonological Encoding

Background:

  • The debate on sequential versus parallel phonological encoding is central to speech production research.
  • The number of segments primed effect, where response latencies decrease with more primed segments, is often cited as evidence for sequential encoding.
  • However, response criteria and initial segment characteristics may influence this effect.

Purpose of the Study:

  • To investigate whether the number of segments primed effect is driven by response criteria (segment vs. syllable) and initial segment manner (plosive).
  • To differentiate between sequential and parallel phonological encoding models.
  • To clarify the underlying mechanisms of the number of segments primed effect.

Main Methods:

  • Participants named monosyllabic words in a 0, 1, or 2 segments primed naming task.
  • Instructions were varied to induce either a segment-based or syllable-based response criterion.
  • Acoustic latency, articulatory latency, and initial segment duration were analyzed.

Main Results:

  • Shorter acoustic latencies with 2 segments primed over 1 were observed only when participants adopted a segment criterion and the initial segment was a plosive.
  • Similar acoustic latencies across priming conditions were found when participants adopted a syllable criterion.
  • These findings suggest that the number of segments primed effect is contingent on specific task conditions.

Conclusions:

  • The number of segments primed effect is not solely evidence for sequential phonological encoding but is influenced by response criteria and phonetic properties.
  • Adopting a syllable criterion supports parallel phonological encoding.
  • Distinguishing between encoding models requires nuanced analysis beyond simple response latencies.