The emergence of executive functions by the evolution of second-order learning

Abstract

Mental representation (MR) is regarded as part of sophisticated cognitive processes. It has been argued that under selection pressure for second-order learning (learning how to learn), first-order learning evolves to facilitate second-order learning within lifetime by capturing inherent structures of changing environment as MR. Two hypotheses derive from this theory: (1) solving MR-dependent tasks should involve second-order plasticity at the neural level. (2) Solving MR-dependent tasks should involve internalization of structural features of environment into corresponding features of the cognitive system. In this paper, constructive approach was taken and the result was analyzed from the viewpoint of these two hypotheses. Executive functions, a collection of cognitive processes necessary for good performance on complex tasks, are the theme of our model. They are considered to be related to theory of mind, which is a typical example of MR. We conducted an evolutionary simulation where agents with recurrent neural networks tackled the Wisconsin card sorting test (WCST), a widely used task to measure abilities of executive functions. The results showed some agents were successfully able to achieve ideal scores in the WCST, hence the emergence of executive functions. In addition, we also discussed the hypotheses based on one of the evolved neural networks.