Including cognitive biases and distance-based rewards in a connectionist model of complex problem solving

We present a cognitive, connectionist-based model of complex problem solving that integrates cognitive biases and distance-based and environmental rewards under a temporal-difference learning mechanism. The model is tested against experimental data obtained in a well-defined and planning-intensive problem. We show that incorporating cognitive biases (symmetry and simplicity) in a temporal-difference learning rule (SARSA) increases model adequacy—the solution space explored by biased models better fits observed human solutions. While learning from explicit rewards alone is intrinsically slow, adding distance-based rewards, a measure of closeness to goal, to the learning rule significantly accelerates learning. Finally, the model correctly predicts that explicit rewards have little impact on problem solvers’ ability to discover optimal solutions. ► We present a temporal-difference-based model of human problem solving. ► The model includes distance-based rewards (DBR), a measure of closeness to goal. ► With DBR, environmental rewards are not necessary for learning the task. ► Incorporating cognitive biases (symmetry and simplicity) increases model adequacy.