Rating scales (such as, Likert scales, Guttman scales, Feelings thermometers, etc.) represent simple tools for measuring attitudes, judgements and subjective preferences in human rating contexts. Because rating scales show some useful properties (e.g., measurement uniformity, considerable flexibility, statistically appealing), they represent popular and reliable instruments in socio-behavioral sciences. However, standard rating scales suffer also from some relevant limitations. For example, they fail in measuring vague and imprecise information and, above all, they are only able to capture the final outcome of the cognitive process of rating (i.e., the rater's response). To overcome these limitations, some fuzzy versions of these scales (e.g., fuzzy conversion scales, fuzzy rating scales) have been proposed over the years. However, also these more sophisticated scales show some important shortcomings (e.g., difficulty in fuzzy variables construction and potential lack of ecological validity). In this paper, we propose a novel methodology (DYFRAT) for modeling human rating evaluations from a fuzzy-set perspective. In particular, DYFRAT captures the fuzziness of human ratings by modeling some real-time biometric events that occur during the cognitive process of rating in an ecological measurement setting. Moreover, in order to show some important characteristics of the proposed methodology, we apply DYFRAT to some empirical rating situations concerning decision making and risk assessment scenarios. 
Dynamic Fuzzy Rating Tracker (DYFRAT): a novel methodology for modeling real-time dynamic cognitive processes in rating scales