Lexical diversity (LD) has been used in a wide range of applications, producing a rich history in the field of speech-language pathology. However, for clinicians and researchers identifying a robust measure to quantify LD has been challenging. Recently, sophisticated techniques have been developed that assert to measure LD. Each one is based on its own theoretical assumptions and employs different computational machineries. Therefore, it is not clear to what extent these techniques produce valid scores and how they relate to each other. Further, in the field of speech-language pathology, researchers and clinicians often use different methods to elicit various types of discourse and it is an empirical question whether the inferences drawn from analyzing one type of discourse relate and generalize to other types. The current study examined a corpus of four types of discourse (procedures, eventcasts, storytelling, recounts) from 442 adults. Using four techniques (D; Maas; Measure of textual lexical diversity, MTLD; Moving average type token ratio, MATTR), LD scores were estimated for each type. Subsequently, data were modeled using structural equation modeling to uncover their latent structure. Results indicated that two estimation techniques (MATTR and MTLD) generated scores that were stronger indicators of the LD of the language samples. For the other two techniques, results were consistent with the presence of method factors that represented construct-irrelevant sources. A hierarchical factor analytic model indicated that a common factor underlay all combinations of types of discourse and estimation techniques and was interpreted as a general construct of LD. Two discourse types (storytelling and eventcasts) were significantly stronger indicators of the underlying trait. These findings supplement our understanding regarding the validity of scores generated by different estimation techniques. Further, they enhance our knowledge about how productive vocabulary manifests itself across different types of discourse that impose different cognitive and linguistic demands. They also offer clinicians and researchers a point of reference in terms of techniques that measure the LD of a language sample and little of anything else and also types of discourse that might be the most informative for measuring the LD of individuals.

Emotion recognition through facial expression plays a critical role in communication. Review of studies investigating individuals with traumatic brain injury (TBI) and emotion recognition indicates significantly poorer performance compared to controls. The purpose of the study was to determine the effects of different media presentation on emotion recognition in individuals with TBI, and if results differ depending on severity of TBI. Adults with and without TBI participated in the study and were assessed using the The Awareness of Social Inferences Test: Emotion Evaluation Test (TASIT:EET) and the Facial Expressions of Emotion-Stimuli and Tests (FEEST) The Ekman 60 Faces Test (E-60-FT). Results indicated that individuals with TBI perform significantly more poorly on emotion recognition tasks compared to age and education matched controls. Additionally, emotion recognition abilities greatly differ between mild and severe TBI groups, and TBI participants performed better with the static presentation compared to dynamic presentation.