naa a22 4500 510776868 CHVBK 20180411083233.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.3758/s13428-013-0364-8 doi (NATIONALLICENCE)springer-10.3758/s13428-013-0364-8 A signal detection analysis of gist-based discrimination of genetic breast cancer risk [Elektronische Daten] [Christopher Fisher, Christopher Wolfe, Valerie Reyna, Colin Widmer, Elizabeth Cedillos, Priscilla Brust-Renck] Pervasive biases in probability judgment render the probability scale a poor response mode for assessing risk judgments. By applying fuzzy trace theory, we used ordinal gist categories as a response mode, coupled with a signal detection model to assess risk judgments. The signal detection model is an extension of the familiar model used in binary choice paradigms. It provides three measures of discriminability—low versus medium risk, medium versus high risk, and low versus high risk—and two measures of response bias. We used the model to assess the effectiveness of BRCA Gist, an intelligent tutoring system designed to improve women's judgments and understanding of genetic risk for breast cancer. Participants were randomly assigned to the BRCA Gist intelligent tutoring system, the National Cancer Institute (NCI) Web pages, or a control group, and then they rated cases that had been developed using the Pedigree Assessment Tool and also vetted by medical experts. BRCA Gist participants demonstrated increased discriminability for all three risk categories, relative to the control group; the NCI group showed increased discriminability for two of the three levels. This result suggests that BRCA Gist best improved discriminability among genetic risk categories, and both BRCA Gist and the NCI website improved participants' ability to discriminate, rather than simply shifting their decision criterion. A spreadsheet that fits the model and compares parameters across the conditions can be downloaded from the Behavior Research Methods website and used in any research involving categorical responses. Psychonomic Society, Inc., 2013 Signal detection theory nationallicence Fuzzy-trace theory nationallicence Risk assessment nationallicence Genetic breast cancer risk nationallicence Fisher Christopher Miami University, Oxford, OH, USA aut Wolfe Christopher Miami University, Oxford, OH, USA aut Reyna Valerie Cornell University, Ithaca, NY, USA aut Widmer Colin Miami University, Oxford, OH, USA aut Cedillos Elizabeth Miami University, Oxford, OH, USA aut Brust-Renck Priscilla Cornell University, Ithaca, NY, USA aut Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 613-622 45:3<613 2013 45 13428 https://doi.org/10.3758/s13428-013-0364-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.3758/s13428-013-0364-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fisher Christopher Miami University, Oxford, OH, USA aut NATIONALLICENCE 700 1- Wolfe Christopher Miami University, Oxford, OH, USA aut NATIONALLICENCE 700 1- Reyna Valerie Cornell University, Ithaca, NY, USA aut NATIONALLICENCE 700 1- Widmer Colin Miami University, Oxford, OH, USA aut NATIONALLICENCE 700 1- Cedillos Elizabeth Miami University, Oxford, OH, USA aut NATIONALLICENCE 700 1- Brust-Renck Priscilla Cornell University, Ithaca, NY, USA aut NATIONALLICENCE 773 0- Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 613-622 45:3<613 2013 45 13428 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer