In two studies, we examine the relationship between self-reported everyday activities and SWB, while allowing individuals to express their activities freely by allowing open-ended responses that were then analyzed with state-of-the-art (transformers-based) Natural Language Processing.
We show that using a recent break-through in artificial intelligence –transformers–, psychological assessments from text-responses can approach theoretical upper limits in accuracy, converging with standard psychological rating scales..
This tutorial introduces how to use state-of-the-art AI techniques in both custom research analyses as well as in completely end-to-end analytic processes in R.
The SWLS-3 and the HILS-3 can efficiently be used together with one shared instruction, without compromising (and in most aspects even yielding small improvements) the psychometric soundness of the scales.
We discuss the implications concerning the differences between maladaptive daydreaming and mind wandering and the possibility of targeting sleep for mind wandering interventions.
The study provides preliminary evidence for the MBFP’s cross-cultural validity, and strengthens previous claims for its efficacy as a new, accessible alternative for enhancing wellbeing.