To be optimally useful, novel metrics of anomia must be validated within a rigorous psychometric framework to (i) efficiently provide information about both overall severity and the underlying cognitive deficits in a unified framework, (ii) support repeated assessments without threatening internal validity or measurement precision, (iii) target both object and action naming, and (iv) easily integrate into computerized adaptive testing platforms. All current anomia tests lack at least one of these features. This project pursues three aims:

First, to ease sharing of information across clinics and labs, and enhance the ability to conduct robust meta-analytic studies, five object picture-naming tests will be equated within an item response theory framework, and their scores will be expressed on the same metric. Further, to obviate the need for large samples in future item development, a model for predicting the difficulty of most picturable nouns will be refined and cross-validated. Just as the measurement of length does not depend on which ruler is used, measurement of anomia severity need not depend on which naming test is given.

Second, to quantify the underlying cognitive deficits of anomia, a cognitive-psychometric model of anomia will be refined and evaluated to produce a measurement model that balances clinical utility, model-data fit, and fidelity to current theory.

Third, given the increasingly recognized importance of action naming, a computer adaptive action naming test will be developed using item response theory by modeling action naming responses; regressing verb item difficulty parameters on relevant target properties; and, conducting real-data simulations to assess the precision of the computer adaptive test engine. PIs: Hula & Fergadiotis