Primitive computations in speech processing Export

@article{citeulike:4472300, abstract = {Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress \& Bonatti, 2007; Pe\~{n}a, Bonatti, Nespor, \& Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations.}, author = {Endress, Ansgar D. and Mehler, Jacques}, citeulike-article-id = {4472300}, citeulike-linkout-0 = {http://dx.doi.org/10.1080/17470210902783646}, doi = {10.1080/17470210902783646}, journal = {The Quarterly Journal of Experimental Psychology}, keywords = {artificiallanguage, learning, psy}, number = {11}, pages = {2187--2209}, posted-at = {2009-05-06 17:13:02}, priority = {2}, publisher = {Psychology Press}, title = {Primitive computations in speech processing}, url = {http://dx.doi.org/10.1080/17470210902783646}, volume = {62}, year = {2009} }

TY - JOUR ID - citeulike:4472300 L3 - citeulike-article-id:4472300 N2 - Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007; Peña, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations. IS - 11 JF - The Quarterly Journal of Experimental Psychology EP - 2209 TI - Primitive computations in speech processing PB - Psychology Press VL - 62 SP - 2187 KW - artificiallanguage KW - learning KW - psy AU - Endress, Ansgar AU - Mehler, Jacques PY - 2009/// UR - http://dx.doi.org/10.1080/17470210902783646 ER -