eBook - ePub
Computational Modeling in Cognition
Principles and Practice
This is a test
- 376 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Book details
Table of contents
Citations
About This Book
An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science
This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.
Frequently asked questions
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
Both plans give you full access to the library and all of Perlegoâs features. The only differences are the price and subscription period: With the annual plan youâll save around 30% compared to 12 months on the monthly plan.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, weâve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Yes, you can access Computational Modeling in Cognition by Stephan Lewandowsky, Simon Farrell in PDF and/or ePUB format, as well as other popular books in Psychology & Cognitive Psychology & Cognition. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover Page
- Title Page
- Copyright
- Contents
- Preface
- 1. Introduction
- 2. From Words to Models: Building a Toolkit
- 3. Basic Parameter Estimation Techniques
- 4. Maximum Likelihood Estimation
- 5. Parameter Uncertainty and Model Comparison
- 6. Not Everything That Fits Is Gold: Interpreting the Modeling
- 7. Drawing It All Together: Two Examples
- 8. Modeling in a Broader Context
- References
- Author Index
- Subject Index
- About the Authors