Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again
Fecha
2021Autor
Luthra, Sahil
Peraza-Santiago, Giovanni
Beeson, Keia’na
Saltzman, David
Crinnion, Anne Marie
Magnuson, James S.
Metadatos
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Luthra, S., Peraza‐Santiago, G., Beeson, K., Saltzman, D., Crinnion, A.M. and Magnuson, J.S. (2021), Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again. Cognitive Science, 45: e12962. https://doi.org/10.1111/cogs.12962
Resumen
A long-standing question in cognitive science is how high-level knowledge is integrated with sensory
input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech
sound, but do such effects reflect direct top-down influences on perception or merely postperceptual
biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation
for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context
phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target
phoneme (e.g., the initial phoneme of a stimulus from a tapes-capes continuum), consistent with the
influence of an unambiguous context phoneme in the same position. Because this phoneme-to-phoneme
compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would
constitute strong support for top–down interaction. However, results from previous LCfC studies have
been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of
stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme
restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous
context-final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for
coarticulation for a subsequent target phoneme.We observed robust LCfC in a well-powered, preregistered
experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40).
These results provide strong evidence in favor of computational models of spoken word recognition
that include top–down feedback.