Comparing memory capacity across stimuli requires  maximally dissimilar foils:  Using deep convolutional neural networks to understand visual working memory capacity for real-world objects

Timothy Brady; Viola S. Störmer

doi:None

Title	Authors

Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects

Contributors:

Timothy Brady
Viola S. Störmer

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Project

Projects
Registrations

Results: All Projects Results: My Projects Results: All Registrations Results: My Registrations

Files

Loading files...

Citation

Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.

This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.

Create an Account Learn More Hide this message

Main content

Links to this project

Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects

Link other OSF projects

Files

Citation

Recent Activity

Start managing your projects on the OSF today.