Individual differences in the cognitive mechanisms of planning strategy discovery
Ruiqi He, Falk Lieder

TL;DR
This study explores how individual differences and cognitive mechanisms influence the discovery of planning strategies in humans, enhancing existing models with factors like effort valuation and pseudo-rewards to better match human performance.
Contribution
The paper introduces additional cognitive mechanisms into metacognitive reinforcement learning models to better account for individual differences in planning strategy discovery.
Findings
A significant proportion of participants used at least one new mechanism.
Mechanisms like pseudo-rewards and effort valuation facilitated strategy discovery.
Models with these mechanisms still did not fully match human performance.
Abstract
People employ efficient planning strategies. But how are these strategies acquired? Previous research suggests that people can discover new planning strategies through learning from reinforcements, a process known as metacognitive reinforcement learning (MCRL). While prior work has shown that MCRL models can learn new planning strategies and explain more participants' experience-driven discovery better than alternative mechanisms, it also revealed significant individual differences in metacognitive learning. Furthermore, when fitted to human data, these models exhibit a slower rate of strategy discovery than humans. In this study, we investigate whether incorporating cognitive mechanisms that might facilitate human strategy discovery can bring models of MCRL closer to human performance. Specifically, we consider intrinsically generated metacognitive pseudo-rewards, subjective effort…
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Taxonomy
TopicsReinforcement Learning in Robotics · Behavioral and Psychological Studies · Decision-Making and Behavioral Economics
