2018
DOI: 10.1016/j.wocn.2018.02.003
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Lexical frequency co-determines the speed-curvature relation in articulation

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Cited by 24 publications
(27 citation statements)
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“…This effect can be explained by opposing forces onto the movement of the tongue body. On the one hand, greater proficiency allows the tongue to execute articulatory trajectories in a more skilled way, as we already have shown in Tomaschek et al (2018c) and Tomaschek et al (2018a). On the other hand, greater proficiency results in stronger anticipation of upcoming targets and gestures, which is in line with findings for hand movements (Sosnik et al, 2004).…”
Section: Articulatory Trajectories In the [I:] Vowelsupporting
confidence: 88%
“…This effect can be explained by opposing forces onto the movement of the tongue body. On the one hand, greater proficiency allows the tongue to execute articulatory trajectories in a more skilled way, as we already have shown in Tomaschek et al (2018c) and Tomaschek et al (2018a). On the other hand, greater proficiency results in stronger anticipation of upcoming targets and gestures, which is in line with findings for hand movements (Sosnik et al, 2004).…”
Section: Articulatory Trajectories In the [I:] Vowelsupporting
confidence: 88%
“…The more practical experience with a given speech goal in various phonetic environments (e.g., a lingual constriction gesture for the vowel /i/ in different consonantal environments), the more proficient the anticipatory patterns are likely to be. For instance, in adults, frequent words have been associated with greater articulatory practice (Tomaschek, Arnold, Bröker, & Baayen, 2018) and pseudowords produced repeatedly were found to increase movement speed and decrease in variability (Tiede, Mooshammer, Goldstein, Shattuck-Hufnagel, & Perkell, 2011).…”
mentioning
confidence: 99%
“…This became possible due to the adoption of novel statistical tools, such as Generalized Additive Mixed Modelling (Wood, 2006; van Rij, Wieling, Baayen, & van Rijn, 2015; Wieling, 2018). These types of analyses have been recently applied to data from EMA (Tomaschek, Arnold, Bröker, & Baayen, 2018), ultrasound (e.g., Strycharczuk & Sebregts, 2018), MRI (Johnson et al, 2019), nasal and oral airflow (Desmeules‐Trudel & Brunelle, 2018), and EPG and air pressure (Ünal‐Logacev et al, 2018). Overall, this development reflects a general increase in computational power and analytical sophistication manifested in phonetic research (and quantitative linguistic research in general) over the last decade.…”
Section: An Overview Of Major Methods and Recent Trendsmentioning
confidence: 99%