Abstract
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.
Highlights
Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks
One possible explanation for these findings is that age-related deficits in error-driven learning do not reflect changes in how prediction errors are computed but rather how they are regulated according to environmental statistics[13,14,15,16]
The data to support this hypothesis are somewhat contradictory and point to a more complicated scenario[34]. We examined one such scenario: deficits in learning result from differences in how older individuals assign influence to new information according to environmental statistics
Summary
Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. While uncertainty can provide a reasonable prescription for learning during periods of relative stability, efficient learning in dynamic environments requires online detection of abrupt shifts in a latent state, such as might occur for a company with the announcement of a costly settlement[24]. Such abrupt shifts are referred to as change points. Surprise is greatest when outcomes deviate most substantially from predictions and can be measured by the absolute magnitude of prediction errors[25]
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