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1
Mixed-effects design analysis for experimental phonetics ...
Kirby, James; Sonderegger, Morgan. - : PsyArXiv, 2018
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2
Effects of delayed auditory feedback on gesture-speech synchrony: Pre-registration and exploratory study ...
Pouw, Wim; Dixon, James. - : PsyArXiv, 2018
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3
Word final schwa is driven by intonation – the case of Bari Italian ...
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4
Phonetic category formation is perceptually driven during the early stages of adult L2 development ...
Casillas, Joseph. - : PsyArXiv, 2018
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5
Researcher degrees of freedom in phonetic sciences ...
Roettger, Timo. - : PsyArXiv, 2018
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6
Preprint Are Tims hot and Toms not? ...
Klenovsak, Daniel; Hartung, Franziska; Santiago, Luenya. - : Open Science Framework, 2018
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7
Are Tims hot and Toms not? ...
Hartung, Franziska. - : PsyArXiv, 2018
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8
Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German ...
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9
Learning distributions as they come: Particle filter models for online distributional learning of phonetic categories ...
Kleinschmidt, Dave. - : PsyArXiv, 2018
Abstract: Human infants have the remarkable ability to learn any human language. One proposed mechanism for this ability is distributional learning, where learners infer the underlying cluster structure from unlabeled input. Computational models of distributional learning have historically been principled but psychologically-implausible computational-level models, or ad hoc but psychologically plausible algorithmic-level models. Approximate rational models like particle filters can potentially bridge this divide, and allow principled, but psychologically plausible models of distributional learning to be specified and evaluated. As a proof of concept, I evaluate one such particle filter model, applied to learning English voicing categories from distributions of voice-onset times (VOTs). I find that this model learns well, but behaves somewhat differently from the standard, unconstrained Gibbs sampler implementation of the underlying rational model. ...
Keyword: Cognition and Perception; Cognitive Psychology; Computational Linguistics; FOS Languages and literature; FOS Psychology; Linguistics; Phonetics and Phonology; Psychology; Social and Behavioral Sciences
URL: https://dx.doi.org/10.17605/osf.io/dymc8
https://psyarxiv.com/dymc8/
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10
Nonword repetition depends on the frequency of sublexical representations at different grain sizes: evidence from a multi-factorial analysis ...
Szewczyk, Jakub; Marecka, Marta; Chiat, Shula. - : Open Science Framework, 2018
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11
Reading Fluency Matters: NIH R21 HD090460-01A1 ...
Braze, David; Gong, Tao; Nam, Hosung. - : PsyArXiv, 2018
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12
EEG-derived phoneme confusion matrices show the fit between phonological feature systems and brain responses to speech ...
McCloy, Daniel; Lee, Adrian. - : PsyArXiv, 2018
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13
Cohort and rhyme priming emerge from the multiplex network structure of the mental lexicon ...
Stella, Massimo. - : PsyArXiv, 2018
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14
Do You Name Speedy Objects Faster Than Slow Objects: SPEEDED NAMING OR NAMING SPEED? THE AUTOMATIC EFFECT OF OBJECT SPEED ON PERFORMANCE ...
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15
Quantifying gesture-speech synchrony: Exploratory data report and pre-registration ...
Pouw, Wim; Dixon, James. - : PsyArXiv, 2018
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16
Non-Manual Articulators in Irish Sign Language Verbs: An Analysis with Data Mining Association Rules
In: Conference Papers (2018)
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17
Structure in talker variability: How much is there and how much can it help? ...
Kleinschmidt, Dave. - : PsyArXiv, 2018
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18
Speaker gender and sound change in sociolinguistic perception: GOOSE-fronting in British English ...
Alderton, Roy. - : PsyArXiv, 2018
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19
Substance matters: A reply to Jardine 2016
In: Joe Pater (2018)
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