From infancy, we recognize that labels denote category membership and help us to identify the critical features that objects within a category share. Labels not only reflect how we categorize, but also allow us to communicate and share categories with others. Given the special status of labels as markers of category membership, do novel labels (i.e., non‐words) affect the way in which adults select dimensions for categorization in unsupervised settings? Additionally, is the purpose of this effect primarily coordinative (i.e., do (...) labels promote shared understanding of how we categorize objects)? To address this, we conducted two experiments in which participants individually categorized images of mountains with or without novel labels, and with or without a goal of coordination, within a non‐communicative paradigm. People who sorted items with novel labels had more similar categories than people who sorted without labels only when they were told that their categories should make sense to other people, and not otherwise. We argue that sorters' goals determine whether novel labels promote the development of socially coherent categories. (shrink)

Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining (...) the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory (RFT) which is rooted in a philosophical world view called functional contextualism (FC). This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research. (shrink)

The aspects of facial attractiveness have been widely studied, especially within the context of evolutionary psychology, which proposes that aesthetic judgements of human faces are shaped by biologically based standards of beauty reflecting the mate quality. However, the faces of primates, who are very similar to us yet still considered non-human, remain neglected. In this paper, we aimed to study the facial attractiveness of non-human primates as judged by human respondents. We asked 286 Czech respondents to score photos of 107 (...) primate species according to their perceived “beauty”. Then, we analyzed factors affecting the scores including morphology, colors, and human-likeness. We found that the three main primate groups were each scored using different cues. The proportions of inner facial features and distinctiveness are cues widely reported to affect human facial attractiveness. Interestingly, we found that these factors also affected the attractiveness scores of primate faces, but only within the Catarrhines, i.e., the primate group most similar to humans. Within this group, human-likeness positively affected the attractiveness scores, and facial extremities such as a prolonged nose or exaggerated cheeks were considered the least attractive. On the contrary, the least human-like prosimians were scored as the most attractive group. The results are discussed in the context of the “uncanny valley”, the widely discussed empirical rule. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)