Selected publications
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[abstract]
The historical utility of nudity within the arts has communicated a dichotomy of moral ideology such that nude depictions can be interpreted as embodying philosophical goodness and immoralism. Evaluations of such art may vary based on moral foundations, particularly morals which emphasize autonomy (individualizing morals) and community (binding morals). In two studies, we examined how a viewer’s moral foundations influence aesthetic evaluations of paintings depicting nudity ranging from the 16th to 20th century. Participants rated 50 paintings of nude posed women (Study 1) and 50 paintings of nude posed men (Study 2) and evaluated each painting’s beauty, how pornographic the artwork was, their interest in the artwork, discomfort felt, and openly reported on the emotional tone of each painting. Participants then completed the Moral Foundations Questionnaire. In both studies, results indicated that people who endorsed higher individualizing morals were associated with evaluating the artwork as more beautiful and more interesting. Conversely, higher endorsement of binding morals was associated with perceiving the artwork less beautiful, feeling higher discomfort with viewing, and considering the artwork more pornographic. These findings provide initial empirical support for philosophical conceptualizations of morality in the depictions of nudity within the domain of visual art. (PsycInfo Database Record (c) 2024 APA, all rights reserved)
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[abstract]
The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study, human participants (N = 151) and GPT-4 provided responses for the Alternative Uses Task, Consequences Task, and Divergent Associations Task. We found that AI was robustly more creative along each divergent thinking measurement in comparison to the human counterparts. Specifically, when controlling for fluency of responses, AI was more original and elaborate. The present findings suggest that the current state of AI language models demonstrate higher creative potential than human respondents.
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[abstract]
This chapter explores the relationships between attention, affect, and creativity, including a discussion of creativity in the context of mindfulness and mind-wandering. First, we discuss the effects of different forms of attention on different types of creativity, such as divergent and convergent thinking, and real-world creative achievements. We then follow with a discussion of the relationship between creativity, emotional functioning, and the power of positivity on fostering creative ideas. The chapter concludes with a review on how mindfulness meditation and mind-wandering, both separately and jointly, impact creative thinking. This includes a discussion of the effects of different types of mindfulness meditation on creativity, as well as the core facets thought to constitute mindfulness. Overall, this chapter provides an engaging overview of the various attentional and emotional states thought to be implicated in creativity, as well as an intriguing look at how mindfulness and mind-wandering work independently and in tandem to influence creative thinking.
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[abstract]
The Aesthetic Responsiveness Assessment (AReA) is a self-report scale for measuring individual differences in the strength of responses to art objects and events. Its 14 items sort into three subscales: aesthetic appreciation (AA), intense aesthetic experience (IAE), and creative behaviour. The present research evaluated the dimensionality of the AReA using tools from network psychometrics, particularly bootstrap exploratory graph analysis. Using a sample of English-speaking adults (n = 1071), the network analyses closely replicated the number and item composition of the AReA subscales, but one of the items—a cross-loaded item proposed to be in both the AA and IAE subscales—was not replicated. Using the bootstrap sampling distributions, we suggest item assignments that avoid awkward dual-loadings and provide a simpler dimensional structure. Taken together, the results reveal clear strengths of the AReA and illustrate how emerging network tools can illuminate and guide psychometric decisions.