{"id":154189,"date":"2008-04-01T00:00:00","date_gmt":"2008-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/an-error-model-for-pointing-based-on-fitts-law\/"},"modified":"2018-10-16T20:27:29","modified_gmt":"2018-10-17T03:27:29","slug":"an-error-model-for-pointing-based-on-fitts-law","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/an-error-model-for-pointing-based-on-fitts-law\/","title":{"rendered":"An error model for pointing based on Fitts&#8217; law"},"content":{"rendered":"<div class=\"asset-content\">\n<p>For decades, Fitts\u2019 law (1954) has been used to model pointing time in user interfaces. But as with any rapid motor act, faster movement times come at the cost of increased errors. Although prior work has examined accuracy as the \u201cspread of hits,\u201d no work has formulated a predictive model for error rates (0-100%) based on Fitts\u2019 law parameters. We show that Fitts\u2019 law mathematically implies a predictive error rate model, which we derive. We then describe an experiment where target size, target distance, and movement time are manipulated. Our results show a strong model fit: a regression analysis of observed vs. predicted error rates yields a correlation of R2 = .959 for N = 90 points. Furthermore, we show that the effect on error rate of target size W is greater than that of target distance A, indicating a departure from Fitts\u2019 law which maintains that W and A contribute proportionally to index of difficulty (ID). Our error model can be used with Fitts\u2019 law to estimate and predict error rates along with speeds, providing a framework for unifying this dichotomy.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For decades, Fitts\u2019 law (1954) has been used to model pointing time in user interfaces. But as with any rapid motor act, faster movement times come at the cost of increased errors. Although prior work has examined accuracy as the \u201cspread of hits,\u201d no work has formulated a predictive model for error rates (0-100%) based [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems","msr_editors":"","msr_how_published":"","msr_isbn":"978-1-60558-011-1","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"1613\u20131622","msr_page_range_start":"1613","msr_page_range_end":"1622","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM's Digital Library --http:\/\/www.acm.org\/dl\/.","msr_conference_name":"CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Jacob O. Wobbrock, Edward Cutrell, Susumu Harada, I. 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