Referenzsemantik in der Partnermodellierung

How can we explain that an assertion on something perceived can be understood in the same manner by somebody who cannot perceive that scene? This problem bases the interest in computational linguistics in how listener modeling could possibly be harmonized with reference semantics. Mental images substituting real perception appear as a way out. The architecture of the listener model has to be adapted to the creation and use of such pictorial data structures. Furthermore, the relation between the latter and a verbal (i. e., propositional) representation must be understood. The resulting architecture of a listener model with reference semantics can be employed to solve communicational problems from three general classes in a better way, as is demonstrated by an example implementation.     

Statistical significance tests for binormal ROC curves

Statistical significance tests are derived and evaluated for measuring apparent differences between an obtained and an expected binormal ROC curve, between two independent binormal ROC curves, and among groups of independent binormal ROC curves. A binormal ROC curve is described by two parameters which represent the spread of the means and the ratio of the standard deviations of the two underlying Gaussian decision variable distributions. To test the significance of apparent differences between or among ROC curves, approximate χ² statistics for each of the three tests were constructed from maximum likelihood estimates of the two parameters defining the binormal ROC curve. The performance of each test statistic was evaluated by simulating five-category rating scale data with equal numbers of noise and signal-plus-noise trials (set at 50, 250, and 500) for each of three typical ROC curves. For the significance test involving only one ROC curve, rating scale data were generated from the chance diagonal of the ROC space also. Although test performance was found to be somewhat dependent on the number of trials and on the location of the ROC curve in the ROC space, comparisons of the obtained and expected fractions of (falsely) significant results at various α levels showed the proposed statistical significance tests to be reliable under practical experimental conditions.