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Psychological Research

, Volume 81, Issue 5, pp 990–1003 | Cite as

Familiar units prevail over statistical cues in word segmentation

  • Bénédicte Poulin-CharronnatEmail author
  • Pierre Perruchet
  • Barbara Tillmann
  • Ronald Peereman
Original Article

Abstract

In language acquisition research, the prevailing position is that listeners exploit statistical cues, in particular transitional probabilities between syllables, to discover words of a language. However, other cues are also involved in word discovery. Assessing the weight learners give to these different cues leads to a better understanding of the processes underlying speech segmentation. The present study evaluated whether adult learners preferentially used known units or statistical cues for segmenting continuous speech. Before the exposure phase, participants were familiarized with part-words of a three-word artificial language. This design allowed the dissociation of the influence of statistical cues and familiar units, with statistical cues favoring word segmentation and familiar units favoring (nonoptimal) part-word segmentation. In Experiment 1, performance in a two-alternative forced choice (2AFC) task between words and part-words revealed part-word segmentation (even though part-words were less cohesive in terms of transitional probabilities and less frequent than words). By contrast, an unfamiliarized group exhibited word segmentation, as usually observed in standard conditions. Experiment 2 used a syllable-detection task to remove the likely contamination of performance by memory and strategy effects in the 2AFC task. Overall, the results suggest that familiar units overrode statistical cues, ultimately questioning the need for computation mechanisms of transitional probabilities (TPs) in natural language speech segmentation.

Keywords

Artificial Language Word Segmentation Exposure Phase Speech Stream 2AFC Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors are grateful to Pascal Morgan and Cédric Foucault for help with collecting the data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Université Bourgogne Franche-ComtéLEAD-CNRS UMR5022DijonFrance
  2. 2.CNRS UMR5292, INSERM U1028, Lyon Neuroscience Research Center, Auditory Cognition and Psychoacoustics Team, Université of Lyon ILyonFrance
  3. 3.Univ. Grenoble AlpesCNRS UMR5105, LPNCGrenobleFrance

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