Springer Nature

Dissociating memory accessibility and precision in forgetting [Collection]

Posted on 2019-06-05 - 11:13

Forgetting involves the loss of information over time. While previous research has focussed on the rate of information loss, we know less about the form this loss takes. For example, are memory representations remembered with less precision, or do they instead become less accessible? Here we use ‘precision’ measures of memory, and probabilistic mixture models, to investigate the loss of both precision and accessibility of word-location associations over time. Importantly, we are able to directly compare these two measures by converting precision and accessibility into a common scale – information entropy. Using online testing, we will assess the extent to which forgetting is driven by a loss in either precision or accessibility. Further, we will assess how forgetting is modulated by shared content. Does learning multiple similar experiences decrease information loss for either precision or accessibility?

Item Protocol:
The in-principle accepted Stage 1 Registered Report Protocol.

Item Figure 1:
Figure from in-principle accepted Stage 1 Registered Report Protocol

Item: Supplementary Information:
Supplementary Information file accompanying the Registered Report Stage 1 Protocol.

Item Supplementary Figure 1:
Supplementary Figure accompanying the Registered Report Stage 1 Protocol.

Item Supplementary Information Guide:
Guide to the different supplementary folders and files.

Item Supplementary Code [zipped]:
This item contains a compressed folder which includes all Matlab code that is required to run the experiment.More information is listed in the Supplementary Information Guide.

Supplementary Data [zipped]:
This item contains the word lists used in the experiment. More information is available in the Supplementary Information Guide.

Video Transcripts:
The video transcripts instructing participants. More information is available in the Supplementary Information Guide.


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AJH is funded by the Wellcome Trust (204277/Z/16/Z) and ESRC (ES/R007454/1). BR is funded by a Learning in Machines and Brains Fellowship from the Canadian Institute for Advanced Research and a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-04947). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Date of in-principle acceptance



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