This chapter shows that there is a crucial anthropological dimension to Immanuel Kant's account of cognition that has been unacknowledged until now. Kant's anthropology of cognition develops along two complementary lines. On the one hand, it studies nature's purposes for the human species, the natural dimension of human cognition. On the other hand, it uses this knowledge to realise the cognitive vocation, the pragmatic dimension of human cognition. This pragmatic dimension consists in spelling out the natural subjective conditions that help or hinder the cognition, thereby enabling one to become more cognitively efficacious. To illustrate this claim, the chapter examines the case of human temperaments. It discusses the idea that Kant's anthropology of cognition has a pragmatic dimension turns out to be problematic. The chapter shows that Kant makes room for a form of control that is sufficient to account for the possibility of a pragmatic anthropology of cognition.

Logic might chart the rules of the world itself; the rules of rational human thought; or both. Husserl had a very broad concept of logic that embraces our usual modern idea of logic as well as something he called pure logic, which we can loosely characterise as something like the fundamental forms of experience. For Husserl, the fundamental forms of pure logic are an in-eliminable part of experience: i.e. experience encompasses direct apprehension of these inferential relationships. The apprehended structures are abstract and platonic; discovered, rather than constructed. Theory, empirical observation, and experience are in this sense fallible: they may or may not get it right and reveal the actual independent structure of logic. Both logic and mathematics as they are characterised by Husserl, should encounter the realist problem of independence, neither are the sort of thing we can simply take as part of human cognition.

This chapter introduces some recent developments in the areas of human reasoning and decision making. Regarding decision making, the chapter focuses on decision-under-risk, using problems which are explicitly described in linguistic or symbolic terms. Human common-sense reasoning is far more sophisticated than any current artificial intelligence models can capture; yet people's performance on, for example, simple conditional inference, while perhaps explicable in probabilistic terms, is by no means effortless and noise-free. It may be that human reasoning and decision making function best in the context of highly adapted cognitive processes such as basic learning, deploying world knowledge, or perceptuomotor control. Indeed, what is striking about human cognition is the ability to handle, even to a limited extent, reasoning and decision making in novel, hypothetical, verbally stated scenarios, for which our past experience and evolutionary history may have provided us with only minimal preparation.

In this chapter, computer models of cognition focusing on the use of neural networks are reviewed. This chapter begins by placing connectionism in its historical context, leading up to its formalization in Rumelhart and Mc-Clelland's two-volume Parallel Distributed Processing. Three important early models illustrating some of the key properties of connectionist systems are discussed, as well as how the novel theoretical contributions of these models arose from their key computational properties. Connectionism offers an explanation of human cognition because instances of behavior in particular cognitive domains can be explained with respect to a set of general principles and the conditions of the specific domains. Connectionist theory has had a widespread influence on cognitive theorizing, and this influence was illustrated by considering connectionist contributions to our understanding of memory, cognitive development, acquired cognitive impairments, and developmental deficit.

This chapter talks about systematization of a particular approach to modeling the mind: declarative computational cognitive modeling. The goal of computational cognitive modeling and the goal of declarative computational cognitive modeling and systematization in logic-based computational cognitive modeling (LCCM) are to understand the kind of cognition distinctive of human persons by modeling this cognition in information processing systems. LCCM is made based on a generalized form of the concept of logical system as defined rather narrowly in mathematical logic. This chapter shows how the problems can be solved in LCCM in a manner that matches the human normatively incorrect and normatively correct responses returned after the relevant stimuli are presented. This chapter explains LCCM as a formal rationalization of declarative computational cognitive modeling. It also presents the attempt to build computational simulations of all, or large portions of, human cognition, on the basis of logic alone.