María Luisa Martínez-Martí

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.

This study investigates (a) the relationship between character strengths, passion and resilience in athletes in a sample of Spanish passionate athletes (elite, federated and recreational), and (b) if passion for sport mediates the relationship between character strengths and resilience. Using a cross-sectional design, participants (N = 480) completed measures of character strengths, passion, and resilience. Both Principal Components and Confirmatory Factor Analyses were conducted with character strengths, and five factors were extracted. All character strengths factors related positively to resilience. Harmonious passion (but not obsessive passion) partially mediated the relationship between all character strengths factors and resilience. Strengths and limitations of the study, as well as its implications are discussed. Este estudio investiga (a) la relación entre las fortalezas del carácter, la pasión y la resiliencia de una muestra de atletas apasionados españoles (élite, federados y recreativos), y (b) si la pasión por el deporte media la relación entre las fortalezas del carácter y la resiliencia. Mediante un diseño transversal, los participantes (N = 480) completaron medidas de fortalezas del carácter, pasión y resiliencia. Se llevaron a cabo un análisis de componentes principales y un análisis factorial confirmatorio con las fortalezas de carácter y se extrajeron cinco factores. Todos los factores de fortalezas del carácter se relacionaron positivamente con la resiliencia. La pasión armoniosa (pero no la pasión obsesiva) medió parcialmente la relación entre todos los factores de fortalezas del carácter y la resistencia. Finalmente, se discuten las fortalezas y limitaciones del estudio, así como sus implicaciones. Este estudo investiga (a) a relação entre forças de caráter, paixão e resiliência de uma amostra de atletas espanhóis apaixonados (elite, federados e recreativos) e (b) se a paixão pelo esporte medeia a relação entre forças de caráter e resiliência. Usando um desenho transversal, os participantes (N = 480) concluíram as medidas de força, paixão e resiliência do personagem. Uma análise de componentes principais e uma análise fatorial confirmatória com força de caráter foram realizadas e cinco fatores foram extraídos. Todos os fatores de força do caráter foram positivamente relacionados à resiliência. A paixão harmoniosa (mas não a obsessiva) mediou parcialmente a relação entre todos os fatores de força e resistência do caráter. Finalmente, são discutidos os pontos fortes e as limitações do estudo, bem como suas implicações.

This study examines whether character strengths predict resilience (operationalized as stable or higher mental health and subjective well-being despite an adverse event) over a period of approximately 1 month during the COVID-19 pandemic lockdown in Spain. Using a longitudinal design, participants (N = 348 adults) completed online measures of sociodemographic data, information regarding their situation in relation to the COVID-19, character strengths, general mental health, life satisfaction, positive affect and negative affect. All variables were measured at Time 1 and Time 2, except for sociodemographic and most COVID-related information (Time 1 only). Time 1 data collection was conducted between March 21, 2020 and April 2, 2020, i.e., approximately the second week of lockdown in Spain. Time 2 data collection was conducted between April 24, 2020 and May 18, 2020, after the Spanish government announced its intention to progressively release the lockdown. A principal component analysis of character strengths was conducted. Five character strength factors were extracted: fortitude, goodness, intellectual, interpersonal, and restraint. Factor structures at Times 1 and 2 were highly consistent. All character strength factors at Time 1 correlated positively with life satisfaction and positive affect, and negatively with negative affect and mental health at T2 (higher scores in the mental health measure indicate poorer mental health). Fortitude strengths showed the highest correlations. We conducted a series of regression analyses with strength factors at Time 1 as predictors, and mental health, life satisfaction, and positive and negative affect as dependent variables, controlling for their baseline levels. To test the directionality of the relationship between strengths and the dependent variables, all analyses were reversed. All character strength factors predicted an increase in mental health. They also predicted positive affect, with the exception of strengths of restraint. Fortitude, intellectual, and interpersonal strengths predicted an increase in life satisfaction. Finally, fortitude strengths, interpersonal strengths, and strengths of restraint, predicted a decrease in negative affect. None of the reversed analyses yielded significant effects. Limitations, implications, and possible character strengths-based interventions aimed at promoting mental health in the COVID-19 pandemic are discussed.

Within the framework of the Sustainable Development Goals, it is necessary to move towards quality education that promotes opportunities based on the principles of equity and equality. For this reason, the environment where the teaching–learning process occurs plays a fundamental role. Our research shows the results of the effects of the environment in the learning processes of university students (N = 33). Using a method of sampling experiences, the students assessed how the new environment learning (NEL) compared to the traditional classroom (TC) encouraged their attention, participation in class, creativity, curiosity, critical thinking, motivation to learn and mood (hedonic tone and activation level). In addition, the students assessed to what extent the class seemed visually appealing to them. The design was of repeated measures, so that the experiences of the same subjects in both classrooms were evaluated over a period of 53 days. Over this period of time, after finishing each of the classes selected for the study, the participants received a message on their mobile phones with a link to a short ad hoc questionnaire that evaluated their experience in relation to the learning environment of the classes they had just attended. In total, we recorded 359 responses in relation to the TC and 209 in relation to the NEL. The results show statistically significant differences in the degree of participation and visual appeal, with higher levels in the NEL. These elements are vital in achieving the education for sustainable development, oriented towards critical thinking, responsibility and social transformation.

The Scale of Positive and Negative Experience (SPANE) aims to measure affect with high transcultural validity. The bifactor model is the best theoretical option to represent affective balance, although it is not typically used in validation studies. The objectives of this research were to test a bifactor model vis-à-vis the traditional model composed of two correlated factors, to prove its invariance across sexes, and to provide evidence of concurrent validity. A nonprobability sample composed of 600 Mexican students of psychology and medicine was recruit. One-group and multigroup confirmatory factor analyses were carried out. The SPANE and the scales selected to assess depression, perceived stress, and satisfaction with life were applied. The bifactor model showed better goodness-of-fit indices than the two correlated factors model: Δχ ² (11) = 121.436, p  <   .   001, Δχ ² /Δ df  =   11.04 > 5, Δ GFI  =   .034, Δ NFI  =   .025, Δ NNFI  =   .022, and Δ CFI  =   .023 >.01. The internal consistency for the general factor as well as for the factor of positive affect was excellent, whereas it was good for the factor of negative affect. The measurement model was valid across sexes. The general factor of affective balance had a very high correlation with depression, high with perceived stress, and medium with satisfaction with life. It is concluded that SPANE is reliable and shows evidence of validity among Mexican student of psychology and medicine, and the bifactor model is adequate to represent affective balance.

El presente trabajo muestra los resultados preliminares de una evaluación piloto de los efectos del aula inteligente en los procesos de aprendizaje de alumnos universitarios (N = 33). Mediante un método de muestreo de experiencias se evaluó en qué medida los alumnos valoraban que las clases en el aula inteligente, frente al aula convencional, fomentaban su atención, participación en clase, creatividad, curiosidad, pensamiento crítico, motivación por aprender y estado de ánimo (tono hedónico y nivel de activación). Asimismo, los alumnos valoraron en qué medida la clase les parecía visualmente atractiva. El diseño es de medidas repetidas. Se evaluó la experiencia de los mismos sujetos en ambas aulas durante 53 días. Nada más terminar cada una de las clases seleccionadas para el estudio, los participantes recibieron en sus teléfonos móviles un mensaje con un link a un breve cuestionario sobre su experiencia en relación a la clase que acababan de tener. En total se obtuvieron 359 respuestas en relación al aula convencional y 209 en relación al aula inteligente. Los resultados preliminares muestran una diferencia estadísticamente significativa en el grado de participación y de atractivo visual, con puntuaciones más altas en el aula inteligente. No se observaron más diferencias estadísticamente significativas. Martínez-Martí, María Luisa; Moraleda Ruano, Álvaro; Galán Casado, Diego; Pérez Nieto, Miguel Ángel