The verbal overshadowing effect, a phenomenon where verbal descriptions of an encoded face hinder subsequent recognition, has been linked to the similarity in facial image sets used in recognition tasks. However, the specific aspects of similarity that influence this effect remained underexplored. This study, therefore, employed the Stable Diffusion image-generation model to create image sets that are similar either verbally or nonverbally. Experimental results using these sets revealed the presence of the verbal overshadowing effect in the verbally-similar set, but it was not evident in the nonverbally-similar set. These findings align with existing explanations of the verbal overshadowing effect and contribute to enhancing its predictability.

People often interact with environments that can provide only a finite number of items as resources. Eventually a book contains no more chapters, there are no more albums available from a band, and every Pokémon has been caught. When interacting with these sorts of environments, people either actively choose to quit collecting new items, or they are forced to quit when the items are exhausted. Modeling the distribution of how many items people collect before they quit involves untangling these two possibilities, We propose that censored geometric models are a useful basic technique for modeling the quitting distribution, and, show how, by implementing these models in a hierarchical and latent-mixture framework through Bayesian methods, they can be extended to capture the additional features of specific situations. We demonstrate this approach by developing and testing a series of models in two case studies involving real-world data. One case study deals with people choosing jokes from a recommender system, and the other deals with people completing items in a personality survey.