Timed image naming evaluation for adults (TIME) using BOSS images - Sabine Heuer; Corey Briska; Priyanka Shah-Basak; Sara Pillay (PLoS ONE, 2026)
Normative data for naming photographs are essential in psycholinguistic research. However, image naming norms are typically derived from young adults, limiting their relevance for older populations, who are at greater risk for language impairments due to neurological conditions such as stroke, traumatic brain injury or dementia. Further, lexical retrieval declines also in healthy aging, making it essential to establish norms for older adults to distinguish normal from impaired word retrieval. This study provides normative data for 600 photographs of the Bank of Standardized Stimuli (BOSS) focusing on three age cohorts (40–50, 51–65, and 66+). We examined naming accuracy, name agreement, H values, and response times (RT) to explore age-related differences in image naming. Participants completed a web-based oral picture naming task via video conferencing. Results revealed overall high naming accuracy (mean = 80.5%) and name agreement (mean = 87.4%) across the full sample, with modest variability across the range of adults self-reportedly free of neurological deficits. The 51–65 cohort showed the highest accuracy and fastest RTs. Significant correlations between RT and name agreement and H value support the inclusion of RT as key indices of naming difficulty. We discuss the implications of these findings considering psycholinguistic norms, demographic influences, and methodological differences from previous image norming studies. Novel contributions of this study include normative data for a large sample of middle to older age adults including RT and alternative names, expanding the utility of the BOSS image set for examining aging-related changes in lexical access. The study underscores the importance of including RT measures alongside traditional naming norms for improved characterization of visual stimuli. Open access to the updated dataset aims to facilitate future research into age-related language processing and supports personalized applications in cognitive and clinical settings.