Anomia is a core feature of aphasia, a disorder affecting at least 2.5 million Americans. Assessment of naming ability is foundational to both research and clinical practice in aphasiology, but currently available naming tests have largely failed to capitalize on important advances in psychometric theory and practice over the past 50 years. Sensitive metrics are needed to support investigations of the cognitive mechanisms underlying anomia, clinical decision making, and treatment research. To be optimally useful, these metrics 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. To remedy this gap, 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. This work will produce a powerful and flexible anomia assessment tool with utility for both research and clinical practice. Depending on user specifications, it will maximize diagnostic information and measurement precision while minimizing response burden.
The aims of this project directly address goals identified in the NIDCD Strategic Plan for 2017-2021, including developing and refining techniques, technology, and instrumentation for improved diagnosis to aid in treatment and improve clinical outcomes.
Patients who have experienced a stroke or other brain injury frequently experience anomia - a condition in which they are unable to produce words when speaking. One major challenge in this area is the lack of theoretically driven and psychometrically robust metrics to support treatment efficacy research, and to provide a unified measurement framework for rehabilitation, reimbursement, and policy decisions. Our goal is to create a computerized system for measuring relevant aspects of an individual?s anomia that will link existing tests to a universal metric, thereby opening the door to new ways of treating anomia and reducing clinical workload.
