To what extent do people attribute meanings to “nonsense” words? How general is such attribution of meaning? We used a set of words lacking conventional meanings to elicit drawings of made‐up creatures. Separate groups of participants rated the nonsense words and the drawings on several semantic dimensions and selected what name best corresponded to each creature. Despite lacking conventional meanings, “nonsense” words elicited a high level of consistency in the produced drawings. Meaning attributions made to nonsense words corresponded with meaning (...) attributions made by separate people to drawings that were inspired by the name. Naïve participants were able to recover the name that inspired the drawing with greater‐than‐chance accuracy. These results suggest that people make liberal and consistent use of non‐arbitrary relationships between forms and meanings.Open Research Badges This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/8juyc/. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. (shrink)

When a person is characterized categorically with a label (e.g., Linda is a feminist), people tend to think that the attributes associated with that person are central and long lasting (S. Gelman & G. D. Heyman, 1999). This bias, which is related to category-based induction and stereotyping, has been thought to arise because a category label (e.g., feminist) activates the dominant properties associated with the representation of the category. This explanation implies that categorical information influences inferential processes mainly by conjuring (...) up main attributes or instances represented in the category. However, the present experiments reveal that this attribute-based explanation of induction does not provide a complete picture of inferential processes. The results from 3 experiments suggest that category information can affect inferences of attributes that are not directly related to the category, suggesting that categories not only activate likely attributes but also help integrate unlikely or even unrelated attributes. (shrink)