{"title":"使用触控笔和软键盘打字速度的理论上限和下限","authors":"R. W. Soukoreff, I. Mackenzie","doi":"10.1080/01449299508914656","DOIUrl":null,"url":null,"abstract":"Abstract A theoretical model is presented to predict upper-and lower-bound text-entry rates using a stylus to tap on a soft QWERTY keyboard. The model is based on the Hick-Hyman law for choice reaction time, Fitts law for rapid aimed movements, and linguistic tables for the relative frequencies of letter-pairs, or digrams, in common English. The model's importance lies not only in the predictions provided, but in its characterization of text-entry tasks using keyboards. Whereas previous studies only use frequency probabilities of the 26 × 26 digrams in the Roman alphabet, our model accommodates the space har—the most common character in typing tasks. Using a very large linguistic table that decomposes digrams by position-within-words, we established start-of-word (space-letter) and end-of-word (letter-space) probabilities and worked from a 27 × 27 digram table. The model predicts a typing rate of 8.9wpm for novices unfamiliar with the QWERTY keyboard, and 30.1wpm for experts. Comparisons are drawn with em...","PeriodicalId":280506,"journal":{"name":"Behav. Inf. Technol.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"235","resultStr":"{\"title\":\"Theoretical upper and lower bounds on typing speed using a stylus and a soft keyboard\",\"authors\":\"R. W. Soukoreff, I. Mackenzie\",\"doi\":\"10.1080/01449299508914656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A theoretical model is presented to predict upper-and lower-bound text-entry rates using a stylus to tap on a soft QWERTY keyboard. The model is based on the Hick-Hyman law for choice reaction time, Fitts law for rapid aimed movements, and linguistic tables for the relative frequencies of letter-pairs, or digrams, in common English. The model's importance lies not only in the predictions provided, but in its characterization of text-entry tasks using keyboards. Whereas previous studies only use frequency probabilities of the 26 × 26 digrams in the Roman alphabet, our model accommodates the space har—the most common character in typing tasks. Using a very large linguistic table that decomposes digrams by position-within-words, we established start-of-word (space-letter) and end-of-word (letter-space) probabilities and worked from a 27 × 27 digram table. The model predicts a typing rate of 8.9wpm for novices unfamiliar with the QWERTY keyboard, and 30.1wpm for experts. Comparisons are drawn with em...\",\"PeriodicalId\":280506,\"journal\":{\"name\":\"Behav. Inf. Technol.\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"235\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behav. Inf. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/01449299508914656\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behav. Inf. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/01449299508914656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Theoretical upper and lower bounds on typing speed using a stylus and a soft keyboard
Abstract A theoretical model is presented to predict upper-and lower-bound text-entry rates using a stylus to tap on a soft QWERTY keyboard. The model is based on the Hick-Hyman law for choice reaction time, Fitts law for rapid aimed movements, and linguistic tables for the relative frequencies of letter-pairs, or digrams, in common English. The model's importance lies not only in the predictions provided, but in its characterization of text-entry tasks using keyboards. Whereas previous studies only use frequency probabilities of the 26 × 26 digrams in the Roman alphabet, our model accommodates the space har—the most common character in typing tasks. Using a very large linguistic table that decomposes digrams by position-within-words, we established start-of-word (space-letter) and end-of-word (letter-space) probabilities and worked from a 27 × 27 digram table. The model predicts a typing rate of 8.9wpm for novices unfamiliar with the QWERTY keyboard, and 30.1wpm for experts. Comparisons are drawn with em...