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1 - Introduction

from Part I - Introduction to Modeling

Published online by Cambridge University Press:  05 February 2018

Simon Farrell
Affiliation:
University of Western Australia, Perth
Stephan Lewandowsky
Affiliation:
University of Bristol
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Summary

This introductory chapter pursues three principal goals. First, we show that computational modeling is essential to ensure progress in cognitive science. Second, we provide an introduction to the abstract idea of modeling and its many and varied applications. Third, we survey some of the issues involved in the interpretation of model output, including in particular how models can help constrain scientists’ own thinking.

Models and Theories in Science

Cognitive scientists seek to understand how the mind works. That is, we want to describe and predict people's behavior, and we ultimately wish to explain it, in the same way that physicists predict the motion of an apple that is dislodged from its tree (and can accurately describe its downward path) and explain its trajectory (by appealing to gravity). For example, if you forget someone's name when you are distracted seconds after being introduced to her, we would like to know what cognitive process is responsible for this failure. Was it lack of attention? Forgetting over time? Can we know ahead of time whether or not you will remember that person's name?

The central thesis of this book is that to answer questions such as these, cognitive scientists must rely on quantitative mathematical models, just like physicists who research gravity. We suggest that to expand our knowledge of the human mind, consideration of the data and verbal theorizing are insufficient on their own. This thesis is best illustrated by considering something that is (just a little) simpler and more readily understood than the mind. Have a look at the data shown in Figure 1.1, which represent the position of planets in the night sky over time.

How might one describe this peculiar pattern of motion? How would you explain it? The strange loops in the otherwise consistently curvilinear paths describe the famous “retrograde motion” of the planets – that is, their propensity to suddenly reverse direction (viewed against the fixed background of stars) for some time before resuming their initial path. What explains retrograde motion? It took more than a thousand years for a satisfactory answer to that question to become available, when Copernicus replaced the geocentric Ptolemaic system with a heliocentric model.

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Publisher: Cambridge University Press
Print publication year: 2018

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  • Introduction
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book: Computational Modeling of Cognition and Behavior
  • Online publication: 05 February 2018
  • Chapter DOI: https://doi.org/10.1017/CBO9781316272503.002
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  • Introduction
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book: Computational Modeling of Cognition and Behavior
  • Online publication: 05 February 2018
  • Chapter DOI: https://doi.org/10.1017/CBO9781316272503.002
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Introduction
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book: Computational Modeling of Cognition and Behavior
  • Online publication: 05 February 2018
  • Chapter DOI: https://doi.org/10.1017/CBO9781316272503.002
Available formats
×