Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, including emotions, beliefs, and intentions. With the advent of ...artificial intelligence (AI) and the prominence of large language models in mental health applications, questions persist about their aptitude in emotional comprehension. The prior iteration of the large language model from OpenAI, ChatGPT-3.5, demonstrated an advanced capacity to interpret emotions from textual data, surpassing human benchmarks. Given the introduction of ChatGPT-4, with its enhanced visual processing capabilities, and considering Google Bard's existing visual functionalities, a rigorous assessment of their proficiency in visual mentalizing is warranted.
The aim of the research was to critically evaluate the capabilities of ChatGPT-4 and Google Bard with regard to their competence in discerning visual mentalizing indicators as contrasted with their textual-based mentalizing abilities.
The Reading the Mind in the Eyes Test developed by Baron-Cohen and colleagues was used to assess the models' proficiency in interpreting visual emotional indicators. Simultaneously, the Levels of Emotional Awareness Scale was used to evaluate the large language models' aptitude in textual mentalizing. Collating data from both tests provided a holistic view of the mentalizing capabilities of ChatGPT-4 and Bard.
ChatGPT-4, displaying a pronounced ability in emotion recognition, secured scores of 26 and 27 in 2 distinct evaluations, significantly deviating from a random response paradigm (P<.001). These scores align with established benchmarks from the broader human demographic. Notably, ChatGPT-4 exhibited consistent responses, with no discernible biases pertaining to the sex of the model or the nature of the emotion. In contrast, Google Bard's performance aligned with random response patterns, securing scores of 10 and 12 and rendering further detailed analysis redundant. In the domain of textual analysis, both ChatGPT and Bard surpassed established benchmarks from the general population, with their performances being remarkably congruent.
ChatGPT-4 proved its efficacy in the domain of visual mentalizing, aligning closely with human performance standards. Although both models displayed commendable acumen in textual emotion interpretation, Bard's capabilities in visual emotion interpretation necessitate further scrutiny and potential refinement. This study stresses the criticality of ethical AI development for emotional recognition, highlighting the need for inclusive data, collaboration with patients and mental health experts, and stringent governmental oversight to ensure transparency and protect patient privacy.
Skillful responsive support facilitates coping with stressors and overcoming challenges. We posit that support responsiveness is best understood through the prism of psychological need fulfillment ...and as varying along two dimensions. The horizontal dimension speaks to the specificity and breadth of support (i.e., which needs, and how many, are addressed by it, respectively). The vertical dimension speaks to the degree to which support is enacted, or perceived to be enacted, in ways that touch on self-coherence needs for meaning and identity, needs tied most strongly to recipients' core selves. Empathic identification of psychological needs and of their deeper structure, often achieved through good listening, is argued to be the key for effective responsive support.
Hope is a mental resource and a robust predictor of well-being, which allows individuals to better cope with hardship. Little is known about dyadic hope - i.e., hope serving as a joint resource ...within intimate relationships. We examined dyadic hope in a sample of 100 couples in early parenthood, a challenging though common phase in couples' lives. Three months after becoming parents, both partners completed daily diaries for 3 weeks, reporting their daily hope, stressors, and three types of outcomes: individual, relational, and parental. Using multilevel actor-partner interdependence models, we found that greater hope (both daily and person-level) was positively associated with better actor and partner outcomes of all three kinds. Additionally, hope buffered various daily stressors. Our results show that hope is a personal and shared resource for couples in this pivotal juncture, and thus may constitute a target for future interventions.
