Modelling Human Information Processing Limitations in Learning Tasks with Reinforcement Learning

In behavioral economics, `rational inattention' (C. A. Sims, 2010) has been proposed as a theory of human decision-making subject to information processing limitations. This theory hypothesizes that decision-makers act so as to optimize a trade-off between the utility of their behavior, and the information processing effort required to reach a good decision. Shannon information has been proposed as a means of quantifying this information processing cost. However, existing models in the rational inattention framework do not account for the learning dynamics that underlie human decision-making. In order to incorporate the impact of cognitive limitations on learning, we extend the traditional reinforcement learning objective to incorporate a bound on the Shannon information of the learned policy (see also Lerch & Sims, 2019). Using experimental data from a previously-studied learning paradigm (Niv et al 2015). we show that our method can be used to represent differences in participants' performance as resulting in part from utilizing different capacities for storing and processing information.