Scientific ideas are difficult to teach, difficult to learn, and difficult to accept as true because they contradict our intuitive theories of the world, constructed in childhood but retained across the lifespan, influencing our thinking even as adults. In this chapter, I discuss what intuitive theories are, where they come from, and why they blind us to more accurate theories of the world. I explore two case studies – projectile motion and evolutionary adaptation – to illustrate how intuitive theories are historically entrenched, culturally widespread, resistant to counterevidence, maladaptive for behavior, and seemingly inerasable. I conclude by considering the impact of intuitive theories on human belief and behavior more generally.

Previous chapters have explored the capacity of neural networks for modeling cognition. This chapter looks at applications to infant cognitive development. The first section reviews the trajectory of infants' understanding of object permanence and their ability to engage in physical reasoning, and how the symbolic representation theory can interpret the phenomenon. The second section introduce examples showing that neural networks can accommodate infant reasoning development without explicit rules and symbolic representations. The third section considers the relationship between symbolic models and neural network models, exploring an argument from Fodor and Pylyshyn trying to show that artificial neural networks are not genuine competitors to symbolic accounts.