| Title |
What Comes After /f/? Prediction in Speech Derives From Data-Explanatory Processes
|
|---|---|
| Published in |
Psychological Science, November 2015
|
| DOI | 10.1177/0956797615609578 |
| Pubmed ID | |
| Authors | |
| Abstract |
Acoustic cues are short-lived and highly variable, which makes speech perception a difficult problem. However, most listeners solve this problem effortlessly. In the present experiment, we demonstrated that part of the solution lies in predicting upcoming speech sounds and that predictions are modulated by high-level expectations about the current sound. Participants heard isolated fricatives (e.g., "s," "sh") and predicted the upcoming vowel. Accuracy was above chance, which suggests that fine-grained detail in the signal can be used for prediction. A second group performed the same task but also saw a still face and a letter corresponding to the fricative. This group performed markedly better, which suggests that high-level knowledge modulates prediction by helping listeners form expectations about what the fricative should have sounded like. This suggests a form of data explanation operating in speech perception: Listeners account for variance due to their knowledge of the talker and current phoneme, and they use what is left over to make more accurate predictions about the next sound. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
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| United States | 2 | 29% |
| United Kingdom | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 2 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley demographics
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|---|---|---|
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| Japan | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 54 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 23% |
| Researcher | 12 | 20% |
| Professor | 7 | 11% |
| Professor > Associate Professor | 6 | 10% |
| Student > Master | 5 | 8% |
| Other | 7 | 11% |
| Unknown | 10 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 24 | 39% |
| Linguistics | 13 | 21% |
| Social Sciences | 4 | 7% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Agricultural and Biological Sciences | 1 | 2% |
| Other | 6 | 10% |
| Unknown | 12 | 20% |