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Since the FFitts law is originally a modified version of the\n            <jats:italic toggle=\"yes\">effective width method<\/jats:italic>\n            to normalize speed-accuracy biases, the model fit would improve if multiple biases were mixed together and the throughputs would be more stable than using the nominal target width. In this study, we conduct an experiment in which participants tap 1D-bar and 2D-circular targets under three subjective biases: balancing the speed and accuracy, emphasizing speed, and emphasizing accuracy when they perform the tasks. The results showed that applying the effective width to Ko et al.'s refined FFitts law, which represents the touch ambiguity with a free parameter, was the most successful in normalizing biases. Reanalyzing another dataset on ray-casting pointing also led to the same conclusion. We thus recommend using Ko et al.'s model with effective width when researchers compare several experimental conditions such as devices and user groups.\n          <\/jats:p>","DOI":"10.1145\/3676532","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T13:17:48Z","timestamp":1727183868000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Verifying Finger-Fitts Models for Normalizing Subjective Speed-Accuracy Biases"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9807-120X","authenticated-orcid":false,"given":"Shota","family":"Yamanaka","sequence":"first","affiliation":[{"name":"LY Corporation, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7192-7231","authenticated-orcid":false,"given":"Hiroki","family":"Usuba","sequence":"additional","affiliation":[{"name":"LY Corporation, Tokyo, 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