Skip to main content
Social Sci LibreTexts

3.1: Chapter Overview

  • Page ID
    40056
  • When cognitive science arose in the late 1950s, it did so in the form of what is now known as the classical approach. Inspired by the nature of the digital electronic computer, classical cognitive science adopted the core assumption that cognition was computation. The purpose of the current chapter is to explore the key ideas of classical cognitive science that provide the core elements of this assumption.

    The chapter begins by showing that the philosophical roots of classical cognitive science are found in the rationalist perspective of Descartes. While classical cognitive scientists agree with the Cartesian view of the infinite variety of language, they do not use this property to endorse dualism. Instead, taking advantage of modern formal accounts of information processing, they adopt models that use recursive rules to manipulate the components of symbolic expressions. As a result, finite devices—physical symbol systems—permit an infinite behavioural potential. Some of the key properties of physical symbol systems are reviewed.

    One consequence of viewing the brain as a physical substrate that brings a universal machine into being is that this means that cognition can be simulated by other universal machines, such as digital computers. As a result, the computer simulation of human cognition becomes a critical methodology of the classical approach. One issue that arises is validating such simulations. The notions of weak 3 56 Chapter 3 and strong equivalence are reviewed, with the latter serving as the primary goal of classical cognitive science.

    To say that two systems—such as a simulation and a human subject—are strongly equivalent is to say that both are solving the same information processing problem, using the same algorithm, based on the same architecture. Establishing strong equivalence requires collecting behavioural evidence of the types introduced in Chapter 2 (relative complexity, intermediate state, and error evidence) to reverse engineer a subject’s algorithm. It also requires discovering the components of a subject’s architecture, which involves behavioural evidence concerning cognitive impenetrability as well as biological evidence about information processing in the brain (e.g., evidence about which areas of the brain might be viewed as being information processing modules). In general, the search for strong equivalence by classical cognitive scientists involves conducting a challenging research program that can be described as functional analysis or reverse engineering.

    The reverse engineering in which classical cognitive scientists are engaged involves using a variety of research methods adopted from many different disciplines. This is because this research strategy explores cognition at all four levels of investigation (computational, algorithmic, architectural, and implementational) that were introduced in Chapter 2. The current chapter is organized in a fashion that explores computational issues first, and then proceeds through the remaining levels to end with some considerations about implementational issues of importance to classical cognitive science.

    • Was this article helpful?