Before revision:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
After revision:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning and memory . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .