Quantifying Proactive and Reactive Button Input
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Quantifying Proactive and Reactive Button Input. / Kim, Hyunchul; Hornbæk, Kasper; Lee, Byungjoo.
CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2022. 40.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Quantifying Proactive and Reactive Button Input
AU - Kim, Hyunchul
AU - Hornbæk, Kasper
AU - Lee, Byungjoo
N1 - Publisher Copyright: © 2022 ACM.
PY - 2022
Y1 - 2022
N2 - When giving input with a button, users follow one of two strategies: (1) react to the output from the computer or (2) proactively act in anticipation of the output from the computer. We propose a technique to quantify reactiveness and proactiveness to determine the degree and characteristics of each input strategy. The technique proposed in this study uses only screen recordings and does not require instrumentation beyond the input logs. The likelihood distribution of the time interval between the button inputs and system outputs, which is uniquely determined for each input strategy, is modeled. Then the probability that each observed input/output pair originates from a specific strategy is estimated along with the parameters of the corresponding likelihood distribution. In two empirical studies, we show how to use the technique to answer questions such as how to design animated transitions and how to predict a player's score in real-time games.
AB - When giving input with a button, users follow one of two strategies: (1) react to the output from the computer or (2) proactively act in anticipation of the output from the computer. We propose a technique to quantify reactiveness and proactiveness to determine the degree and characteristics of each input strategy. The technique proposed in this study uses only screen recordings and does not require instrumentation beyond the input logs. The likelihood distribution of the time interval between the button inputs and system outputs, which is uniquely determined for each input strategy, is modeled. Then the probability that each observed input/output pair originates from a specific strategy is estimated along with the parameters of the corresponding likelihood distribution. In two empirical studies, we show how to use the technique to answer questions such as how to design animated transitions and how to predict a player's score in real-time games.
KW - anticipation
KW - Button input
KW - reaction
KW - temporal pointing
UR - http://www.scopus.com/inward/record.url?scp=85130580979&partnerID=8YFLogxK
U2 - 10.1145/3491102.3501913
DO - 10.1145/3491102.3501913
M3 - Article in proceedings
AN - SCOPUS:85130580979
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Y2 - 30 April 2022 through 5 May 2022
ER -
ID: 309121085