Generalized assessment functions based on Grice representations

Understanding how human performance changes as the amount of information available varies is of particular interest across many basic and applied research topics in psychology. One approach to quantifying these changes is with the assessment functions. Briefly, the assessment functions are a family of non-parametric measures that compare observed performance to a baseline derived from a model predicting how changes in information influence the system. Although less commonly used than the capacity coefficient, a similar measure based only on response-time data, the assessment functions are a promising tool because it accounts for response-time and accuracy, and hence is applicable in conditions in which speed-accuracy trade-offs can vary. Two potential hinderances to the wider use of the assessment functions are the specific assumptions needed to derive the baseline model and the lack of associated inferential statistics. In this talk, we demonstrate how a fixed accumulator model with a random threshold (i.e., Grice model) representation of the choice/RT data can be leveraged to derive generalized assessment functions and, potentially, for deriving inferential statistical tests.