A group of researchers led by Prof. LI Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences revealed how working memory resources influence the balance between habitual and goal-directed strategies, with their findings published in the Journal of Cognitive Neuroscience (JoCN).
In everyday life, decision-making often involves a series of choices to reach a goal, like picking a restaurant and deciding on the route. People differ in their decision-making: some stick to habits, while others adapt to new information and goals. The key question is: what decides the balance between habitual and goal-directed strategies? This is the core challenge of meta-control in cognitive neuroscience.
By combining behavioral experiments with computational modeling, the study revealed that the balance between habitual (model-free reinforcement learning) and goal-directed (model-based reinforcement learning) strategies is determined by the availability of working memory resources. The team introduced a novel Hybrid-WM reinforcement learning model, which incorporates working memory limitations into the decision-making framework.
"Unlike older models, this new framework takes into account the brain' s memory limits," said Prof. LI Hai, "It offers a much more accurate depiction of how we make decisions under varying conditions—like stress, distractions, or mental fatigue."
This research quantifies the role of working memory constraints in meta-control and provides novel insights into sequential decision-making. "The findings hold promise for applications in human-computer interaction," said Prof. LI.
The simulation results demonstrate the superiority of the Hybrid-WM model over the classical Hybrid-PRE model. (Image by YANG Lizhuang)