Abstract
Older adults struggle in dealing with changeable and uncertain environments across several cognitive domains. This has been attributed to difficulties in forming adequate task representations that help navigate uncertain environments. Here, we investigate how, in older adults, inadequate task representations impact on model-based reversal learning. We combined computational modeling and pupillometry during a novel model-based reversal learning task, which allowed us to isolate the relevance of task representations at feedback evaluation. We find that older adults overestimate the changeability of task states and consequently are less able to converge on unequivocal task representations through learning. Pupillometric measures and behavioral data show that these unreliable task representations in older adults manifest as a reduced ability to focus on feedback that is relevant for updating task representations, and as a reduced metacognitive awareness in the accuracy of their actions. Instead, the data suggested older adults' choice behavior was more consistent with a guidance by uninformative feedback properties such as outcome valence. Our study highlights that an inability to form adequate task representations may be a crucial factor underlying older adults' impaired model-based inference.