**Manuscript** ---------- This repository includes data and code for the following manuscript: **Joensen, B. H., Harrington, M. O., Berens, S. C., Cairney, S. A., Gaskell, M. G., & Horner, A. J. (2022). Targeted memory reactivation during sleep can induce forgetting of overlapping memories.** **Abstract** ---------- Memory reactivation during sleep can shape new memories into a long-term form. Reactivation of memories can be induced via the delivery of auditory cues during sleep. Although this targeted memory reactivation (TMR) approach can strengthen newly acquired memories, research has tended to focus on single associative memories. It is less clear how TMR affects retention for overlapping associative memories. This is critical, given that repeated retrieval of overlapping associations during wake can lead to forgetting; a phenomenon known as retrieval-induced forgetting (RIF). We asked if a similar pattern of forgetting occurs when TMR is used to cue reactivation of overlapping pair-wise associations during sleep. Participants learnt overlapping pairs; learnt separately, interleaved with other unrelated pairs. During sleep, we cued a subset of overlapping pairs using TMR. While TMR increased retention for the first encoded pairs, memory decreased for the second encoded pairs. This pattern of retention was only present for pairs not tested prior to sleep. The results suggest that TMR can lead to forgetting; an effect similar to RIF during wake. However, this effect did not extend to memories that had been strengthened via retrieval prior to sleep. We therefore provide evidence for a reactivation-induced forgetting effect during sleep. **Repository** ---------- This repository contains the folders ***Analyses*** and ***Data***, and the files ***BehDta.mat*** and ***2LvlDta.xlsx***. - The folder *Analyses* contains all code required to replicate the behavioural (for accuracy see *Analyses\Beh*; for dependency see *Analysis\Dep*) and EEG (*Analyses\EEG*) analyses reported in the manuscript. - The folder *Data* contains subfolders for each participant (e.g., *Data\Sub-01* etc). These subfolders in turn contain the pre-processed EEG data (e.g., *Data\Sub-01’ \FiltEpchR_Sub-01.mat*) needed for the power analyses reported in the manuscript. - The file *BehDta.mat* contains all behavioural data. This data is needed for performing the behavioural analyses reported in the manuscript. - The file ‘2LvlDta.xlsx’ contains means per condition for each participant. These can be imported in JASP, SPSS or other statistical software for analyses. **Code** ---------- **<h5>Behavioural analyses</h5>** All ANOVAs and *t*-tests on retrieval accuracy reported in the manuscript can be replicated using the analysis code *StatBeh.m*. The code and associated functions are stored in *Analyses\Beh*. ANOVAs on retrieval dependency reported in the manuscript can be replicated using the analysis code *EstDep.m*. The code is stored in *Analyses\Dep*. **<h5>EEG analyses</h5>** The code needed for computing estimates and performing analyses of power reported in the manuscript are stored in ‘Analyses\Eeg’ (along with associated functions in ‘Analyses\Eeg\Functions’). - Estimates of time-frequency power can be computed using *EstPow.m*. This code outputs individual .m files (i.e., per participant) that are then used by *StatPow.m*. *EstPow.m* requires the wavelet package by Torrence and Campo (available at http://paos.colorado.edu/research/wavelets/) to convolve the EEG data. - *StatPow.m* performs a cluster-based permutation analysis (implemented in Fieldtrip, available at https://www.fieldtriptoolbox.org/) on time-frequency power, along with follow-up *t*-tests on specific frequency ranges and time windows. **Comments** ------------ Please direct any comments to Bárður H. Joensen, email@example.com. Unfortunately, we cannot provide support for you to adapt the code to your own data.
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