The present study investigated the relationship among different kinds of immersive or involving activities: videogaming (VG), psychological absorption, and daydreaming styles. Involvement with VG was ascertained through a new measure, the Videogame Experience Questionnaire (VEQ). Participants (N = 74) also completed the Tellegen Absorption Scale and the Short Imaginal Processes Inventory. Factor analysis of the VEQ yielded four factors. Psychological absorption is not related to level of self-reported engagement in VG, but specific daydreaming styles are related to specific factors of the VEQ. Positive/constructive daydreaming is related to VG Engagement. Guilt/fear/failure daydreaming is related to Social/Emotional Interference from VG. Interference with performance of responsibilities attributed to VG is related to poor attentional control daydreaming. Implications of the findings are discussed.

Introduction: Artificial intelligence (AI) has tremendous potential for use in psychology. Among the many applications that may benefit from development of AI applications is narrative-personality assessment. Use of these tools and research methods is notably time-consuming and resource intensive. AI has potential to address these issues in ways that would greatly reduce clinician and researcher burden. Nonetheless, it is unclear if current AI models are sufficiently sophisticated to perform the complex downstream tasks, such as narrative assessment.Methodology: The purpose of this study is to explore if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the Social Cognition and Object Relations scales – Global Rating Method (SCORS-G). Experts generated prompt inputs by engaging in a detailed review of SCORS-G training materials. Prompts were then improved using an systematic process in which experts worked with Llama-2-70b to refine prompts. The utility of the prompts was then tested on two AI-empowered chatbots, ChatGPT-4 (OpenAI, 2023) and CLAUDE-2-100k, that were not used in the prompt refinement process.Results: Results showed that the refined prompts allowed chatbots to reliably rate narratives at the global level, though accuracy varied across subscales. Averaging ratings from two chatbots notably improved reliability for the global score and all subscale scores. Experimentation indicated that expert-refined prompts outperformed basic prompts regarding interrater reliability and absolute agreement with gold standard ratings. Only the expert-refined prompts were able to generate acceptable single-rater interrater reliability estimates.Discussion: Findings suggest that AI could significantly reduce the time and resource burdens on clinicians and researchers using narrative rating systems like the SCORS-G. Limitations and implications for future research are discussed.

Professional and nonprofessional communities have raised numerous concerns about the potential deleterious effects modern technology can have upon children and adolescents. Although such concern is understandable, it is important for mental health professionals to be aware of their own resistances and fears about such technology. Modern technology, as do all technologies, holds the potential to disrupt established power relationships between the young and the not so young. This can be humbling at best. This article cautions those who work with children to be mindful that we risk overpathologizing children's investment in technology out of our fears of losing power and of our envy of the ever-expanding technological advantages the young enjoy and will continue to enjoy after we are gone. The better informed we become about the semantics and syntax of this new world, the better we can understand and communicate with the children we are entrusted to help.

Affective computing presents significant challenges, particularly in the context of human-machine interactions. Traditional approaches often involve developing data-driven models that rely on physiological and image-based data to infer the emotional states of individuals during interactions with machines. However, these models frequently lack contextual relevance and perform poorly in complex scenarios involving conflicting cues. Additionally, they suffer from weak generalization due to the absence of structured methodologies that disentangle interconnected external factors. To address these limitations, we propose leveraging the Affective Quality of Representations (AFF) score, a standardized metric for assessing human affective responses across varied contexts. The AFF score is derived from expert ratings of participant narratives elicited by questions or visual cards, following established SCORS-G guidelines. The primary challenge of this approach is its reliance on trained psychology experts for scoring. To mitigate this, we developed the AffectLLM, a large language model designed to predict AFF scores efficiently and support the analysis of human-machine interactions. A strong contribution of AffectLLM is providing a structured metric that can guide the design of humanmachine interaction environments that promote more positive affect quality outcomes.