Beyond Categorical Perception: Gradient Lexical Tone Processing Revealed by Visual Analog Scale.

Purpose: While the Visual Analog Scale (VAS) has revealed gradient perception in segmental speech sounds, its application to lexical tones, a critical yet understudied suprasegmental feature, has been absent. This study investigated lexical tone categorization using VAS, directly comparing it with t...

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Publicado en:Journal of Speech, Language & Hearing Research Vol. 69; no. 7; pp. 2961 - 2979
Autores principales: Cheng, Bing, Xiang, Xi, Dai, Xiangrong, Zou, Yu, Zhang, Xiaojuan, Zhang, Yang
Formato: Artículo
Publicado: American Speech-Language-Hearing Association Jul2026
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Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2026
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      pub: American Speech-Language-Hearing Association
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        10.1044/2026_JSLHR-25-00789
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        atl: Beyond Categorical Perception: Gradient Lexical Tone Processing Revealed by Visual Analog Scale.
      aug:
        au:
          Cheng, Bing
          Xiang, Xi
          Dai, Xiangrong
          Zou, Yu
          Zhang, Xiaojuan
          Zhang, Yang
        affil:
          English Department & Shaanxi Key Laboratory of AI-Empowered Language and Culture Research, School of Foreign Studies, Xi’an Jiaotong University, China.
          Department of Speech-Language-Hearing Sciences and Center for Neurobehavioral Development, University of Minnesota, Twin Cities, Minneapolis.
      su:
        Dialects
        Task performance
        Data analysis
        T-test (Statistics)
        Research funding
        Phonological awareness
        Visual analog scale
        Descriptive statistics
        Speech evaluation
        Statistics
        Speech perception
        Comparative studies
        Data analysis software
        Algorithms
      sug:
        subj:
          Dialects
          Task performance
          Data analysis
          T-test (Statistics)
          Research funding
          Phonological awareness
          Visual analog scale
          Descriptive statistics
          Speech evaluation
          Statistics
          Speech perception
          Comparative studies
          Data analysis software
          Algorithms
      ab: Purpose: While the Visual Analog Scale (VAS) has revealed gradient perception in segmental speech sounds, its application to lexical tones, a critical yet understudied suprasegmental feature, has been absent. This study investigated lexical tone categorization using VAS, directly comparing it with traditional two-alternative forced-choice (2AFC). Method: Eighty-four native speakers of Mandarin Chinese categorized an 11-step fundamental frequency (F0) continuum from Mandarin Tone 1 to Tone 2 in both tasks. Four-parameter logistic functions yielded slope (categorization sharpness) and response variability. Within-category sensitivity was quantified from VAS responses. Results: Paired Wilcoxon signed-ranks test showed significantly shallower slopes (p < .001, r = .76) and lower variability (p < .001, r = .87) in VAS versus 2AFC. One-sample t tests confirmed that listeners discriminated fine-grained differences within categories, with within-category sensitivity reliably exceeding zero; left: M = 0.043, SD = 0.026, t(83) = 15.13, p < .001; right: M = 0.040, SD = 0.022, t(83) = 17.02, p < .001. Crucially, slope and response variability were weakly correlated in VAS (ρ = .27, p < .05) but strongly negatively correlated in 2AFC (ρ = −.67, p < .001). Moreover, response variability correlated significantly across tasks (ρ = .40, p < .001), while slopes did not. Conclusions: These findings provide the converging evidence that gradient aspects of perception are also observable at the suprasegmental level and illustrate how VAS can serve as a sensitive tool for probing lexical tone categorization. The dissociation between task-dependent gradiency and stable response variability helps reconcile apparent conflicts in the categorical perception literature, suggesting that these conflicts may stem from methodological constraints rather than genuine theoretical disagreements.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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