Cecilia Theirs

The effort heuristic suggests that perceived effort enhances evaluations of products and artworks. While well-documented for human creators, it remains unclear whether this principle extends to artificial agents. We investigated whether longer generation times increase effort attribution to AI and, consequently, improve aesthetic evaluations of AI-generated artworks. Two pre-registered studies manipulated generation time: Study 1 (N = 207) used a between-subjects design with reported times (30 seconds vs. 4–8 minutes vs. 30+ minutes), while Study 2 (N = 209) employed a within-subjects design with experienced wait times (4 vs. 20 seconds). Results confirmed that longer generation times increased perceived effort in both studies. However, this heightened effort attribution did not translate into more favorable aesthetic evaluations across any dimension (liking, beauty, depth, quality, complexity, or monetary value). Positive attitudes toward AI and attribution of consciousness emerged as the strongest predictors of aesthetic appreciation. These findings suggest that the effort heuristic does not operate uniformly across human and artificial agents: while people recognize AI effort, they do not reward it with enhanced aesthetic valuations. This dissociation highlights that effort cues lose their normative value as quality signals in the absence of human agency, reinforcing identity-based biases against AI-generated art.

The COVID-19 pandemic has posed significant challenges to mental health worldwide, raising the need to identify stable psychological resources that promote sustainable well-being. This longitudinal study examined whether character strengths predict well-being, post-traumatic growth, and mental health over a 13-month period. Participants (N = 146) completed online measures of character strengths, mental health, life satisfaction, affect, and post-traumatic growth at two time points. First, we tested whether a single general factor of character predicted later mental health and whether life satisfaction, affect, and post-traumatic growth mediated this relationship. Then, we repeated this model but with five different character strengths factors as predictors. Results showed that character predicted all mediators and mental health over time, but only the affective components of well-being mediated the relationship between character and mental health, especially positive affect. When looking at the five character strengths factors, although the majority predicted higher well-being and better mental health over time, goodness and interpersonal and fortitude strengths yielded the strongest effects. These findings suggest that character strengths contribute to sustainable well-being by fostering affective resilience in the face of adversity, aligning with the goals of Sustainable Development Goal 3 (Good Health and Well-being).

This study developed and validated the Fears Towards Generative Artificial Intelligence scale, a novel instrument assessing individuals' concerns about emerging generative AI technologies, which are increasingly integrated into daily life. Drawing on qualitative data from three focus groups and subsequent quantitative validation with 303 participants, we initially derived 37 items that captured diverse fears, including concerns about job displacement, social inequalities, and loss of human autonomy commonly associated with generative AI systems. Exploratory factor analyses supported a unidimensional structure of the scale, demonstrating strong reliability and content validity. Regression models revealed that negative attitudes towards AI and perceived threats to employment were key predictors of heightened fear, while greater usage and familiarity were linked to reduced fear. We also present a short 4‐item version of the scale generated by a genetic algorithm and tested with 101 new participants, which presents good psychometric properties. The FTGAI scale addresses a critical measurement gap and offers a comprehensive tool for researchers and policymakers seeking to understand and mitigate fears towards generative AI's growing societal impact.

Public toilets are a critical public health issue with a significant negative impact on people's lives. Unfortunately, the effect of negative experiences caused by public toilets on people's quality of life and life satisfaction is unknown. In this study, participants (n = 550) were asked to fill in a scale‐based survey about their negative experiences with public toilets, quality of life, and life satisfaction. We found that people with toilet‐dependent illnesses (36% of the sample) reported more negative experiences with public toilets than their counterparts. These negative experiences are also related to lower scores in some areas of participants' quality of life, such as their environmental, psychological, and physical health and life satisfaction, even after controlling for relevant socio‐economic variables. Additionally, toilet‐dependent individuals had particularly negative experiences in terms of life satisfaction and physical health than non‐toilet‐dependent people. We conclude that the impoverishment of quality of life linked to public toilets as an environmental inadequacy is traceable, estimable, and meaningful. This association is not only negative for ordinary people, but it is significantly negative for people with toilet‐dependent illnesses. These results highlight that public toilets are essential to ensure collective well‐being, particularly when considering those affected by their presence or lack thereof.