Phonological Prediction Data
2023
- 187Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage187
- Downloads96
- Views91
Dataset Description
The eye-tracking technique and printed-word version of the visual world paradigm were used to examine whether the phonological forms of incoming words would be predicted during speech comprehension. Participants were instructed to listen to Mandarin Chinese spoken sentences while looking at two single-character printed words on the screen, with one being the critical word and the other being an unrelated distractor word. Each of the spoken sentences includes a highly predictable target word. The phonological relationship between the predictable target word and the critical word was systematically manipulated. Four types of the printed critical words were included: predicted Target Word itself, Homophone Competitor (sharing the same pronunciation “consonant-vowel-lexical tone” with the target word), Tonal Competitor (only sharing the same lexical tone with the target word), or an Unrelated Word. One group of participants were required to perform a “word judgment” task (whether the sp...
Bibliographic Details
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