Characteristic learning process in insomnia
Insomnia is a risk factor for various mental and physical diseases. Understanding the information processing that is unique to this disorder will help in its treatment. This study explores whether the severity of insomnia relates to any unique characteristic learning process distinguished from other symptoms. For this purpose, we used a decision-making task that can dissociate the influence of positive from negative outcomes on choice behavior by estimating dual learning rates. We recruited general participants using a crowdsourcing service. They performed the task online and completed self-report measures on insomnia, anxiety, and depression. The data gathered from 391 participants were analyzed. First, we found a strong correlation between the self-report measures, as predicted. Next, to explore unique learning processes associated with insomnia, we applied the reinforcement learning model to the data from the decision-making task and estimated the model parameters. The higher learning rate of positive outcomes over negative outcomes is a feature observed as a whole and can be used as an index of biased information processing in the learning process. Analyses using linear models revealed that this index is higher in those with higher insomnia scores, which implies that insomnia is related to attention to positive outcomes. Interestingly, higher anxiety scores were predicted in the opposite direction. Possible explanations for the results may be differences in cognitive resources and attention biases. We also report other findings on the association between learning processes and mental health.