Skip to main content Accessibility help
×
Hostname: page-component-7479d7b7d-k7p5g Total loading time: 0 Render date: 2024-07-14T02:37:11.581Z Has data issue: false hasContentIssue false

10 - Encoding Models in Neuroimaging

Published online by Cambridge University Press:  20 April 2023

F. Gregory Ashby
Affiliation:
University of California, Santa Barbara
Hans Colonius
Affiliation:
Carl V. Ossietzky Universität Oldenburg, Germany
Ehtibar N. Dzhafarov
Affiliation:
Purdue University, Indiana
Get access

Summary

Encoding models of neuroimaging data combine assumptions about underlying neural processes with knowledge of the task and the type of neuroimaging technique being used to produce equations that predict values of the dependent variable that is measured at each recording site (e.g., the fMRI BOLD response). Voxel-based encoding models include an encoding model that predicts how every hypothesized neural population responds to each stimulus, and a measurement model that first transforms neural population responses into aggregate neural activity and then into values of the dependent variable being measured. Encoding models can be inverted to produce decoding schemes that use the observed data to make predictions about what stimulus was presented on each trial, thereby allowing unique tests of a mathematical model. Representational similarity analysis is a multivariate method that provides unique tests of a model by comparing its predicted similarity structures to similarity structures extracted from neuroimaging data. Model-based fMRI is a set of methods that were developed to test the validity of purely behavioral computational models against fMRI data. Collectively, encoding methods provide useful and powerful new tests of models – even purely cognitive models – that would have been considered fantasy just a few decades ago.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

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 Dropbox.

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.

Available formats
×