We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
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 .
To save content items to your Kindle, first ensure [email protected]
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.
Computational models of episodic memory provide tools to better understand the latent neurocognitive processes underlying retention of information about specific events from one’s life. This chapter discusses the representations, associations, and dynamics of influential models of episodic memory, with particular emphasis on models of recognition and free recall tasks. In-depth discussion and model-fitting results of four models – the retrieving effectively from memory (REM) model, the bind cue decide model of episodic memory (BCDMEM), the search of associative memory (SAM) model, and the temporal context model (TCM) – are provided to facilitate understanding of these models, as well as similarities and differences between them. Alternative modeling frameworks, including neural network models, are discussed. Throughout, the importance of context in models of episodic memory is emphasized, particularly for free recall tasks.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.