Healthy aging can lead to impairments in learning that affect many laboratory
and real-life tasks. These tasks often involve the acquisition of dynamic
contingencies, which requires adjusting the rate of learning to environmental
statistics. For example, learning rate should increase when expectations are
uncertain (uncertainty), outcomes are surprising (surprise) or contingencies
are more likely to change (hazard rate). In this study, we combine
computational modelling with an age-comparative behavioural study to test
whether age-related learning deficits emerge from a failure to optimize
learning according to the three factors mentioned above. Our results suggest
that learning deficits observed in healthy older adults are driven by a
diminished capacity to represent and use uncertainty to guide learning. These
findings provide insight into age-related cognitive changes and demonstrate
how learning deficits can emerge from a failure to accurately assess how much
should be learned
