Intact reinforcement learning in healthy ageing

Lin, W.-H.; Clarke, A.; Pilz, K. S.; Herzog, M. H.; Kunchulia, M.

doi:10.1101/2023.05.25.542104

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bioRxiv · 10.1101/2023.05.25.542104

Intact reinforcement learning in healthy ageing

Lin, W.-H.·Clarke, A.·Pilz, K. S.·Herzog, M. H.·Kunchulia, M.

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Abstract

What does age in ageing? Results in reinforcement learning (RL) are mixed. Some studies found deteriorated performance in older participants compared to younger controls whereas other studies did not. Daniel et al. (2020) suggested that task demand can explain these differences, with less demanding tasks showing no effect of age. Here, we increased the task demand of previous studies turning them into a classic navigation task. We extracted 4 behavioral parameters and 2 parameters (learning and exploration rates) of a classic Q-learning model. Except for one specific parameter, all other parameters showed no group differences, i.e., RL turned out to be intact in older individuals also with higher task demands. It is important to publish such null results to avoid the stigmatizing impression of an overall performance deficit among older people.

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Lin, W.-H., Clarke, A., Pilz, K. S., Herzog, M. H., Kunchulia, M.. 2023-05-26. Intact reinforcement learning in healthy ageing. https://doi.org/10.1101/2023.05.25.542104

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