Stochastic Resonance in Human Cognition: ACT-R Versus Game Theory, Associative Neural Networks, Recursive Neural Networks, Q-Learning, and Humans
Sanjay Chandrasekharan, Christian J. Lebiere, Terrence C. Stewart, Robert L West · eScholarship (California Digital Library) · 2005
We examined the effect of cognitive noise on human game playing abilities.Human subjects played a guessing game against an ACT-R model set at different noise levels.Counter to the normal effect for noise (i.e., to increase randomness) increasing noise over certain ranges increased the win rate in both the ACT-R model and in the humans.We then attempted to model the human results using ACT-R, Q-Learning, neural networks, and Simple Recursive Neural Networks.Overall, ACT-R produced the best match to the data.However, none of these models were able to reproduce a secondary counter intuitive human win rate effect.