Tools and Data
In 2018 and 2019, the Adult Cognition collected norming data for common categories using responses collected via Amazon’s Mechanical Turk (MTurk) program. Participants ages 18+ were recruited to participate in the study and compensated for their time.’
Categories were randomly presented to each subject. Categories lists were pseudo-randomly organized to 1) avoid similar or related categories within a list (e.g., football related categories are not within the same list) and 2) to ensure that each list contains approximately the same number of exemplars to be rated across all categories (i.e., same amount of time expected to complete each list).
Castro, N., Curley, T., & Hertzog, C. (2020). Category norms with a cross-sectional sample of adults in the United States: Cohort, age, and historical effects on semantic categories. Behavior Research Methods, Advance online publication. doi: 10.3758/s13428-020-01454-9.
Speech error and tip-of-the-tongue diary for mobile devices. (2015)
The data collection of speech errors can be both time consuming and difficult to execute. This also causes difficulties in finding data pools or resources consisting of speech errors. A website called SpEDi was developed in order to improve the accessibility of this data, allowing for users to document errors they encounter in their life and download the available data.
Vitevitch, M. S., Siew, C. S. Q., Castro, N., Goldstein, R., Gharst, J. A., Kumar, J. J., & Boos, E. B. (2015). Speech error and tip-of-the-tongue diary for mobile devices. Frontiers in Psychology, 6:1190. doi: 10.3389/fpsyg.2015.01190. Link to Article
Castro, N., & Vitevitch, M. S. (2022). Using network science and psycholinguistic megastudies to examine the dimensions of phonological similarity. Language and Speech, 00238309221095455.