↓ Skip to main content

A unified account of categorical effects in phonetic perception

Overview of attention for article published in Psychonomic Bulletin & Review, May 2016
Altmetric Badge

Mentioned by

twitter
7 X users

Readers on

mendeley
85 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A unified account of categorical effects in phonetic perception
Published in
Psychonomic Bulletin & Review, May 2016
DOI 10.3758/s13423-016-1049-y
Pubmed ID
Authors

Yakov Kronrod, Emily Coppess, Naomi H. Feldman

Abstract

Categorical effects are found across speech sound categories, with the degree of these effects ranging from extremely strong categorical perception in consonants to nearly continuous perception in vowels. We show that both strong and weak categorical effects can be captured by a unified model. We treat speech perception as a statistical inference problem, assuming that listeners use their knowledge of categories as well as the acoustics of the signal to infer the intended productions of the speaker. Simulations show that the model provides close fits to empirical data, unifying past findings of categorical effects in consonants and vowels and capturing differences in the degree of categorical effects through a single parameter.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Canada 1 1%
Unknown 80 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 28%
Researcher 10 12%
Student > Master 10 12%
Student > Bachelor 8 9%
Student > Doctoral Student 7 8%
Other 15 18%
Unknown 11 13%
Readers by discipline Count As %
Linguistics 27 32%
Psychology 16 19%
Neuroscience 10 12%
Social Sciences 3 4%
Agricultural and Biological Sciences 3 4%
Other 11 13%
Unknown 15 18%