Previous chapters have all developed in different ways the core idea that cognition is information processing. This chapter looks at a very different approach, using dynamical systems theory's mathematical and conceptual tools to model cognitive skills and abilities. The first section explains how how dynamical systems theory can describe cognitive skills and abilities without using the framework of representation and information processing. The second section examines how dynamical systems theory explains two examples of child development, with particular attention to the time-sensitive nature of the dynamic system theory in these examples.

Building on the discussion of neuroanatomy in chapter 3, this chapter explores how the brain is wired. The first section looks at brain maps developed to clarify the relationship between structure and function in the brain and based on anatomical connectivity research. The second section introduces neurophysiological techniques, including EEG, MEG, PET, and fMRI, which allow cognitive scientists to map brain functions and connectivities. Then we discuss these techniques' temporal and spatial resolution to see their different strengths and weaknesses in cognitive neuroscience studies. In the following two sections, we look at two cases combining multiple techniques to explore the mechanism of visual attention in the brain. Finally, the last section discusses some reasons for caution when interpreting neural imaging data.

This chapter introduces the implementations of artificial agents in robotics. The first section looks at the early development of robotics in GOFAI (Good Old-Fashioned AI). SHAKEY is a representative example designed to operate and perform simple tasks in the real world, which illustrates the physical symbol system hypothesis. The second section introduces alternative ideas from situated cognition theorists. The ideas are inspired by studies on simple cognitive systems such as those of insects, to pursue simple architecture robotics that can solve complex problems. The third section reviews how these theoretical ideas have been translated into particular robotic architectures, focusing on subsumption architectures and some examples of behavior-based robotics.

This chapter introduces mindreading (the ability to understand others' thoughts and to interact with them socially). The first section looks at childrens' pretend play behavior and how it is explained by Leslie's metarepresentation models. The second section addresses the false belief test, which was developed to detect whether young children can understand that other people might hold misleading information about their environment. The third section introduces Baron-Cohen's model of the mindreading system, explaining data from different paradigms, such as the false belief test in normal or autistic children. The fourth section looks at an alternative approach -- the simulation theory, which hypothesizes that we predict other people by simulating how we would react if we received the same information. The last section reviews recent neural evidence on mindreading mechanisms.

As the last chapter of this book, we introduce some exciting and under-explored areas of future cognitive science. The first section reviews current large brain imaging databases elicited from the Human Connectome Project (HCP) movement. The second section focuses on the brain's resting state, called the default mode networks (DMN). The third section looks at the development of neuroprosthesis and how cognitive scientists can cooperate with interdisciplinary researchers in robotic engineering and brain--computer interfaces. The fourth section looks at cognitive science and the law, while the last section looks at self-driving vehicles.

This chapter explores the recent shift in cognitive science toward the brain. The first two sections introduce the rudiments of brain anatomy and then explore Ungerleider and Mishkin's two visual systems hypothesis. Their work provides neural evidence of the two visual pathways (ventral and dorsal routes) in the brain from animal studies. The third section introduces the parallel distributed processing model of cognition introduced by Rumelhart, McClelland, and the PDP group. This model, and what came to be known as artificial neural networks, provide a powerful theoretical explanation of how the brain might process information. The last three sections are focused on early brain imaging studies on cognitive functions. First, Petersen and his colleagues used PET to detect how different brain regions respond to different stages of lexical processing. Next, Brewer and his colleagues localized the brain regions in memory tasks using event-related fMRI. Finally, Logothetis and his colleagues' exploration of the neural correlates of the BOLD signal suggests that fMRI signals could be a function of the input to neural regions rather than of neural firing.