The role of testing predictions on state curiosity and learning in older adults

Curiosity drives humans to gain information. In a predictive processing framework, curiosity can be considered to reflect a drive to form good predictive models in order to reduce prediction errors over time (Becker & Cabeza, 2024; Gruber & Ranganath, 2019; Poli et al., 2024; Schwartenbeck et al., 2019). In other words, we may be intrinsically motivated to gain information as it allows for better predictions. The role of actively testing predictions has also been emphasized within learning and memory. For example error-based learning is argued to be driven by a prediction-formation process that then gives rise to an error signal (den Ouden et al., 2009; Holland & Schiffino, 2016; Waller et al., 2024). Other work has found that actively committing to a prediction improves memory and increases epistemic curiosity (Brod et al., 2018; Brod & Breitwieser, 2019; Theobald & Brod, 2021). However, the value of prediction-testing on curiosity and memory may vary with age. Aging is associated with a decline in (subjective) curiosity and exploratory behaviour (Sakaki et al., 2018). Moreover, the motivators of curiosity and learning shift, from novelty and uncertainty seeking in younger ages to information-gathering in older age. On the other hand, studies show that the kind of information being obtained is especially important in older adults, with reported lower trait epistemic curiosity but higher state curiosity when the information to be gained is semantically or personally relevant (Whatley et al., 2025; McGillivray et al., 2015). Moreover, curiosity in older adults has been considered a driving motivator to learn, mitigating against age-related memory impairment and protecting against age-related cognitive decline (Sakaki et al., 2018; Whatley et al., 2025). Here, we aim to investigate the role of testing internal predictions on curiosity and learning in aging. Building on previous work in younger adults, we will explore how older adults differ in the factors motivating their curiosity and learning, such as expected information gain and subjective uncertainty. The task used in this current preregistration has been previously preregistered at (https://osf.io/ckaqd), and data from a younger age group (ages 18-35) has been collected and analyzed. The current study aims to recruit an older cohort (ages 60-80) to examine age-related differences in curiosity and learning in predictable vs. unpredictable settings.