The introduction of the concept of computation in cognitive science is discussed in this article. Computationalism is usually introduced as an empirical hypothesis that can be disconfirmed. Processing information is surely an important aspect of cognition so if computation is information processing, then cognition involves computation. Computationalism becomes more significant when it has explanatory power. The most relevant and explanatory notion of computation is that associated with digital computers. Turing analyzed computation in terms of what are now called Turing machines that are the kind of simple processor operating on an unbounded tape. Turing stated that any function that can be computed by an algorithm could be computed by a Turing machine. McCulloch and Pitts's account of cognition contains three important aspects that include an analogy between neural processes and digital computations, the use of mathematically defined neural networks as models, and an appeal to neurophysiological evidence to support their neural network models. Computationalism involves three accounts of computation such as causal, semantic, and mechanistic. There are mappings between any physical system and at least some computational descriptions under the causal account. The semantic account may be formulated as a restricted causal account.
The relation between emotion and reason has been a major topic in Western philosophy since its inception. The topic of this article, the relation between emotion and rationality, is a more recent concern, reflecting the fact that the idea of rationality is itself a fairly new one. The article does not explore the distinction between reason and rationality, except to note that while the idea of reason has a normative purpose, that of rationality serves to explain behavior. Strictly speaking, the idea of rationality, too, is primarily normative. It tells an agent what he or she should do to realize his or her aims as well as possible.
Patricia Smith Churchland
This article examines the concept of the so-called inference to the best decision in relation to neurobiology. It explains that the idea of inference to the best decision, often referred to by philosophers as abduction, is also known as in experimental psychology as case-based reasoning. The article discusses the tension that has developed between the sanctity of the “ought/is” dogma and what is known about the neurobiology of social behavior and suggests that the cognitive process that we loosely call inference to the best decision is a solution to this tension. It also expresses optimism that psychology and neuroscience will eventually uncover at least the general principles concerning how neural networks perform these functions and that the two domains of explaining and deciding will have much in common.
This article describes the neural engineering framework (NEF), a systematic approach to studying neural systems that has collected and extended a set of consistent methods that are highly general. The NEF draws heavily on past work in theoretical neuroscience, integrating work on neural coding, population representation, and neural dynamics to enable the construction of large-scale biologically plausible neural simulations. It is based on the principles that neural representations defined by a combination of nonlinear encoding and optimal linear decoding and that neural dynamics are characterized by considering neural representations as control theoretic state variables.
A straightforward way of thinking about perception is in terms of perceptual representation. Perception is the construction of perceptual representations that represent the world correctly or incorrectly. This way of thinking about perception has been questioned recently by those who deny that there are perceptual representations. This article examines some reasons for and against the concept of perceptual representation and explores some potential ways of resolving this debate. Then it analyzes what perceptual representations may be: if they attribute properties to entities, what are these attributed properties, and what are the entities they are attributed to.