Leveraging relatedness-based measures in people with language disorders: A scoping review

Understanding lexico-semantic processing is crucial for dissecting the complexities of language and its disorders. Relatedness-based measures, or those which investigate the degree of relatedness in meaning between either task items or items produced by participants, offer the opportunity to harness novel computational and analytical techniques from cognitive network science. Recognizing the need to deepen our understanding of lexico-semantic deficits through diverse experimental and analytical approaches, this review explores the use of such measures in research into language disorders. A comprehensive search of four electronic databases covering publications from the last 11 years (October 2013-September 2024) identified 38 original experimental studies employing relatedness-based measures in populations with language disorders or other neurological conditions. Articles were examined for the types of tasks used, populations studied, item selection methods and analytical approaches. The predominant use of category fluency tasks emerged across studies, with a notable absence of relatedness judgement tasks or comparable paradigms. Commonly discussed populations included individuals with post-stroke aphasia, mild cognitive impairment and schizophrenia. Analytical methods varied significantly, ranging from more traditional approaches of clustering and switching to more sophisticated computational techniques. Despite the evident utility of category fluency tasks in research and clinical settings, the review underscores a critical need to diversify experimental paradigms and probe lexico-semantic processing in a more multifaceted manner. A broadened approach in future language disorder research should incorporate innovative analytical techniques, investigations of neural correlates and a wider array of tasks employing relatedness-based measures already present in healthy populations.