Each day is a flow of events that take place at different times but often in overlapping contexts. Unavoidably, many of the stored memories share similar features, and this overlap poses a major challenge for our memory system (McClelland et al., 1995; Norman and O’Reilly, 2003). The present magnetoencephalography (MEG) study investigates the possibility that the human brain uses a temporal phase code to adaptively separate overlapping memories, enabling the targeted retrieval of goal-relevant information.

Competition between similar memories is considered one of the major causes of forgetting (Anderson and Neely, 1996; Underwood, 1957). A prominent case is proactive interference, where access to a target memory is impaired when overlapping information has been stored prior to target learning (Tulving and Watkins, 1974). This impairment is typically ascribed to the conflict arising from the co-activation of competing memories (Kliegl and Bäuml, 2021). On a neurophysiological level, mid-frontal theta oscillations (3–8 Hz) have been identified as a reliable marker of cognitive conflict in general (Cavanagh and Frank, 2014) and mnemonic conflict specifically (Ferreira et al., 2014; Hanslmayr et al., 2010; Johansson et al., 2007; Staudigl et al., 2010). Increased theta amplitudes may facilitate the emergence of phase separation, where populations of neurons transmitting potentially interfering information are segregated in time along a theta cycle (Buzsáki and Draguhn, 2004).

On a theoretical level, computational models assign a central role to the phase of the hippocampal theta oscillation in the ordered representation of multiplexed information (Lakatos et al., 2005; Lisman, 2005; Lisman and Idiart, 1995; Lisman and Jensen, 2013; Nyhus and Curran, 2010; O’Keefe and Recce, 1993). Empirically, in rodents it has been shown that bursts of gamma power reflecting sequences of spatial information processing are represented at different phases of the hippocampal theta oscillation (Bragin et al., 1995; Colgin et al., 2009). Several recent studies in humans suggest that the neural representations of items held in working memory or encoded into or retrieved from episodic memory (Kerrén et al., 2018; Kunz et al., 2019; Pacheco Estefan et al., 2021) cluster at distinct phases of the (hippocampal) theta rhythm. These findings suggest that low-frequency oscillations provide time windows for the selective processing and readout of distinct units of information (Lisman and Jensen, 2013). This study investigates phase separation as a potential mechanism to avoid interference between multiple competing memories that are simultaneously reactivated by a cue.

The computations by which the human brain achieves a separation of multiple overlapping memories are currently unknown. One computational model leverages different phases of a theta oscillation to iteratively differentiate target from competitor memories (Norman et al., 2006). In this model, which we will henceforth refer to as the oscillating interference resolution model, a retrieval cue will activate associated units, representing target and competitor features, in a phase-dependent manner. In the most desirable output state, at medium levels of inhibition, the cue only activates the target units and no or few competitor features. When inhibition is raised towards the peak of the oscillation, only strong features of the target memory will remain active, and the model learns (via contrastive Hebbian learning) to strengthen through long-term potentiation (LTP) the weaker target nodes that did not survive the higher inhibition levels. Conversely, during the transition from a medium to a lower inhibition state towards the trough of the oscillation, activation spreads to more weakly associated units, including some competitor features. This opposite phase is used to identify and punish overly strong features of the competing memory through long-term depression (LTD). The mechanism (see Figure 1) is repeated across several cycles of an oscillation, which changes the similarity structure of memories into a state in which they are less likely to interfere with each other. This model offers a mechanistic but not yet tested explanation for how the brain solves mnemonic competition.

Figure 1

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A previous study (Kerrén et al., 2018) provided proof-of-principle evidence that the reactivation of a single visual memory is rhythmically modulated by the hippocampal theta rhythm. In the present study, we applied the same analytic approach in an AB-AC associative interference paradigm (Figure 1a and b) to investigate the phase modulation of competing memories. Hypotheses for the experiment were pre-registered on OSF. Based on the oscillating interference resolution model (Norman et al., 2006) and related theta-gamma models (e.g., Lisman and Idiart, 1995), we expected that target and competitor memories become active, and can thus be decoded, at different phases of the hippocampal theta oscillation, with only target features active at higher inhibition phases, and competitors surfacing as inhibition ramps down to medium and low levels (see Figure 1d). The model also assumes that across several repetitions of the theta cycle, weak features of target memories are strengthened while overly strong features of competing memories are punished to make them less interfering on future cycles. We hypothesised that the effects of these dynamics will become visible across several repetitions of competitive retrieval and lead to an increasing phase separation because over time target memories become more likely to activate in the high-inhibition phase whereas competing memories require increasingly lower levels of inhibition to activate. Lastly, in line with previous literature (Wimber et al., 2015), the strengthening of target and weakening of competing memories should also be reflected in corresponding changes in their decodability.

Participants (n = 24) completed an associative memory task including one proactive interference condition and two control conditions (Figure 1a and b). In each learning trial, participants were instructed to form a novel association between a unique verb and an image, which could be either an object or a scene. In the critical competitive condition (CC), the word was associated with two images, one object and one scene image, learned on different trials. Participants were asked to continuously update their memory to remember the most recently encoded associate of each word, thus allowing us to investigate the extent to which previously encoded associations interfered with newly encoded information (proactive interference). In the single-exposure non-competitive condition (NC1), a word was associated with only one image, and never reappeared during learning. This condition served as a baseline for behavioural interference effects. In the dual-exposure non-competitive condition (NC2), a word was also associated with one image only, but this association was learned twice. This condition was included as a control for neural (cue) repetition and order effects. Both non-competitive conditions were also used to train and validate our multivariate classifiers. CC trials were pseudo-randomly intermixed with NC1 and NC2 trials during learning and recall blocks.

In a subsequent memory test, participants were asked to remember the most recent image when prompted with the cue word, and cued recall was repeated three times per word–image association, in a spaced fashion, irrespective of condition. Once participants indicated, via button press, that they had the associated picture back in mind, two follow-up questions were asked, one about the supraordinate (object/scene) and one about the subordinate (animate/inanimate for objects, indoor/outdoor for scenes) category. This task design, with its supra- and subordinate categories, is critical because it allowed us to disentangle, on a neural level, the representational evidence for target and competitor reactivation at any given time point, using two independently trained linear decoding algorithms: one to discriminate indoor and outdoor scenes, and one to discriminate animate and inanimate objects (see example in Figure 1c).

Behavioural indices of proactive interference

We first evaluated behavioural evidence for proactive interference, that is, the extent to which cued recall performance suffered from having encoded two different pictures with one word (CC) compared to only one picture (NC1). Only trials where participants correctly responded to both follow-up questions (i.e. they were able to provide the supra- and subordinate category of the target image) were considered a successful recall. A one-factorial repeated-measures ANOVA, with memory accuracy as the dependent variable, and condition (CC, NC1, NC2) as within-subject factor, showed a significant main effect of condition, F(1.453, 46) = 57.99, p<0.05. The main planned comparison of interest showed that memory performance was indeed significantly reduced in the competitive (M = 53.18%, SD = 19.20%) compared to the single-exposure (M = 57.71%, SD = 13.98%) condition, t(23) = 1.80, p<0.05 (Figure 2a). This confirms the first behavioural hypothesis (OSF) that retrieving a memory is more difficult when a previously encoded, now irrelevant associate competes for recall, indicative of proactive interference. For later analyses relating neural and behavioural indices, an interference score for each participant was calculated by subtracting average memory performance in the CC from the NC1 condition. A second post-hoc test showed that a significant difference was also obtained between NC1 and NC2 (M = 78.88%, SD = 10.06%), t(23) = –11.91, p<0.05, demonstrating that, unsurprisingly, a second encoding exposure improved performance compared to a single exposure.

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Another behavioural index of competition is the intrusion score; a participant’s tendency to choose the competitor instead of the target. Our design allowed us to quantify intrusions based on how often participants selected the specific subcategory of the competitor (i.e. ‘outdoor scene’ in the example in Figure 1) during the staged retrieval. On trials where participants made an error (i.e. selected ‘object’ though the target was a scene and vice versa), they showed a significantly above-chance (i.e. above 50%) probability of selecting the subcategory of the competitor (on average, 88.72%, SD = 10.62%; t(23) = 10.48, p<0.05), suggesting that their errors were not random but biased towards the category of the previously learned but now irrelevant associate (Figure 2b). The proportion of intrusions did not change significantly across repetitions (first: 88.08%, SD = 12.04%; second: 89.23%, SD = 10.62%; third: 88.85%, SD = 14.72%, Z = –54, p=0.59; Wilcoxon signed-rank test of linear slope against zero). Similarly, we did not find a decrease across repetitions in behavioural accuracy for CC target items (Z = –0.73, p=0.47; Wilcoxon signed-rank test of linear slope against zero; see Figure 2—figure supplement 1a). Across participants, the average number of intrusions in the CC correlated negatively with memory performance in the non-competitive conditions, r = –0.37, p=0.037, p<0.05 (see Figure 2—figure supplement 1b), indicating that better memory performance was generally associated with fewer intrusion errors.

The average response time for subjective recollection (indicated by button press) was 2.11 s (SD = 0.15 s) when collapsing across all conditions and repetitions. Response times shortened across repetitions in all three conditions (CC: rep1: 2.29 s, rep2: 2.05 s, rep3: 2.04 s; Z = –2.89, p=0.004; NC1: rep1: 2.55 s, rep2: 2.37 s, rep3: 2.26 s; Z = 3.03, p=0.003; NC2: rep1: 2.01 s, rep2: 1.75 s, rep3: 1.68 s; Z = –4.17, p<0.001; Wilcoxon signed-rank tests of linear slope against zero), suggesting that the time point when participants remembered the associate shifted and overall was faster after repeated recalls, as would be expected.

Hippocampal theta oscillation clocks the reactivation of target and competitor memories

The next step was to assess whether the fidelity with which target and competitor memories can be decoded fluctuates in a theta rhythm. The analysis was conducted to determine the dominant frequency in the decoding timelines, to replicate our previous findings for target memories (Kerrén et al., 2018), and to extend the principle to competitor memories. We estimated the oscillatory component of the fidelity values, following our pre-registration and the rationale of our previous study (Kerrén et al., 2018). We used Irregular-Resampling Auto-Spectral Analysis (IRASA) (as implemented in the FieldTrip toolbox version 2021), a method that robustly detects and separates the oscillatory from the fractal signal component in electrophysiological data (Wen and Liu, 2016). IRASA was applied to the single-trial fidelity timecourses in the CC, in a time window from 500 to 3000 ms post-cue onset, and separately for the target and competitor memories. The first 500 ms were excluded to attenuate the influence of early, cue-elicited event-related potentials, and because no neural reinstatement is expected during this early cuing period (Staresina and Wimber, 2019). The analysis identified 3 Hz as the frequency with the highest power for both target and competitor memories (same peak frequency was also observed for the first repetition; Figure 3—figure supplement 1a and b), and power at 3 Hz exceeded the 95th percentile of the empirical chance distribution generated from surrogate-label classifiers (Figure 3a and b, red line indicating significant deviation). For the NC, the peak frequency was also in the low theta range but slightly faster (4–5 Hz; see Figure 3—figure supplement 1c and d). Since our theta-locked analyses were focused on the CC, we used 3 Hz as the modulating frequency in all subsequent analyses.

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Target and competitor reactivations peak at distinct theta phases

The key hypothesis was that over time the reactivated representations of target and competitor memories would become optimally separated along the phase of the theta oscillation. This hypothesis follows from the oscillating interference resolution model motivating our work (Norman et al., 2006) and other empirical studies on retrieval-induced forgetting (RIF; e.g. Anderson et al., 1994; Wimber et al., 2015). The model assumes that target and competitor representations are overlapping initially, such that strong competitor nodes can (incorrectly) activate during the high-inhibition (‘target’) phase of theta, but gradually get weakened and thus require lower levels of inhibition to become active. Meanwhile, the weakest target nodes do not survive high inhibition initially and thus only activate during a lower inhibition phase of the theta cycle; however, with repeated strengthening they will become active at an increasingly early, higher-inhibition phase. Therefore, while early in time the target and competitor features overlap in their reactivation phase, this overlap is reduced by the strengthening and weakening dynamics in the model. Note that the original model (Norman et al., 2006) predicts a gradual increase of target–competitor separation with each oscillatory cycle of excitation–inhibition. In our pre-registration, we instead hypothesised that the phase separation would be measurable when comparing early with late recall repetitions, allowing us to obtain robust trial-by-trial indices of target and competitor reinstatement and avoid confounds with time-on-trial. As also noted in our pre-registration, there is an influential theta phase model (Hasselmo et al., 2002) suggesting that encoding and retrieval operations in the hippocampal circuit are prioritised at opposite phases of the theta rhythm (for evidence in humans, see Kerrén et al., 2018). Taking this model into account, we thus hypothesised that the target–competitor phase segregation would over time and repetitions become optimal within the retrieval portion (e.g. half) of the theta cycle, rather than spread out across the entire cycle (not shown in Figure 1d).

The phase binning method used to calculate the MI (see above) allowed us to determine the theta phase bin at which target and competitor reinstatement was maximal in a given trial and condition. Importantly, the absolute phase of reinstatement in terms of its angle is likely to vary across participants (e.g. due to individual differences in anatomy). We therefore contrasted the phase of maximal target and competitor reinstatement within each individual participant, computing their phase distance as an index of phase separation. This phase distance is expected to be consistent across participants irrespective of the absolute angle of target and competitor reactivation, and can thus be subjected to group-level statistics. We used a Rayleigh test for non-uniformity (i.e. clustering) to test how coherent the phase separation angle was across participants and a circular v-test to establish whether the mean separation angle significantly deviated from zero. Note that without significant clustering it is difficult to interpret the mean angle in such a phase analysis. On the other hand, significant clustering without a significant difference from zero would indicate that consistently across participants, targets and competitors reactivate at a similar theta phase.

Figure 4a shows the results of this phase separation analysis. We found a consistent phase difference between target and competitor reactivation exclusively in the last repetition (Rayleigh test for non-uniformity, z(20) = 5.03, p=0.005), clustered around a mean angular difference of 34°. Furthermore, a test against zero phase shift confirmed that this target–competitor distance was significantly different from zero difference (z(20) = 8.48, p=0.004). This was not the case for all repetitions averaged, (z(20) = 0.84, p=0.44), nor for the first (z(20) = 2.19, p=0.11) or second (z(20) = 1.76, p=0.17) repetition separately (Figure 4a). Furthermore, there was no significant difference in target–competitor phase separation between the first and third repetition when comparing the difference in mean angle (z(20) = 1.09, p=0.29). Note, however, that there was no significant clustering around a stable mean angle in the first repetition (see above), and this statistical comparison is therefore inconclusive. Finding the separation between target and competitor memories only in the last repetition rather than throughout the entire recall phase deviates from our pre-registered hypothesis, although paralleling the phase modulation results reported above. Overall, the phase distance analysis thus partly confirms our hypotheses, indicating that competing memories become increasingly separated along the hippocampal theta rhythm over time, with significant phase separation emerging after several recall cycles.

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The next set of analyses was not pre-registered and tested for a relationship between target–competitor phase distance and behaviour. The sample was split according to each individual’s intrusion score in the third repetition, dividing participants into a low-intrusion and a high-intrusion group (Figure 4b). We found that the high-intrusion group had a mean separation angle of 7° that was not significantly different from zero (Rayleigh test for non-uniformity, z(9) = 1.77, p=0.17). The low-intrusion group, by contrast, showed a mean phase separation of 57° that significantly clustered around this angle (Rayleigh test for non-uniformity, z(9) = 3.74, p=0.02); however, we found no significant deviation from zero phase shift in this subgroup of participants (Rayleigh test for non-uniformity with a specified mean angle of 0, z(9) = 3.32, p=0.069). A statistical comparison of the phase separation angle in the high- and low-intrusion groups (Wilcoxon signed-rank test in non-circular space, see ‘Materials and methods’) did not indicate a difference between the mean of the high- and low-intrusion groups’ separation angle (Z = 43, p=0.85). Note that each subsample in this comparison only contains 10 participants, and the statistical comparison is thus likely underpowered. In sum, this analysis suggests that only low-intrusion participants exhibit significant phase separation by the end of the task, which can be taken as an indication that more neural differentiation of overlapping memories along the theta cycle relates to more successful behavioural differentiation.

Two more analyses were conducted to corroborate the finding of a phase difference between target and competitor reactivation, complementing the pre-registered analysis reported above. First, we tested for a temporal difference (lag) between the timelines of target and competitor reactivation using a cross-correlation. To do so, we filtered the two fidelity timecourses from each participant at the dominant 3 Hz frequency, in the third repetition of the CC. We then quantified a temporal lag between timecourses by using a sliding window (allowing us to see phase lags that evolve over time) and calculating the cross-correlation for each 330 ms time bin (1/3 of the dominant 3 Hz frequency). Note that this analysis does not specifically take the phase of virtual hippocampal sensors into account and instead simply cross-correlates the entire target and competitor fidelity timecourses derived from all MEG sensors. The analysis revealed a significant cluster starting approximately 1 s after cue onset and lasting until the end of the trial, with a maximum lag of 30° phase angle (Figure 4c). This finding suggests that the reactivation of competitor memories is lagging 30° behind the reactivation of target memories.

The second additional analysis used the average target and competitor fidelity timecourse of each participant, filtered at 3 Hz and shown individually in Figure 4—figure supplement 1a. Visual inspection already suggests a temporal shift, and sometimes even phase opposition, between target and competitor decoding in most participants. To formally quantify the phase shift on a group level, we subtracted in complex space, for each time bin, the phase angle of the target from the angle of the competitor. The resulting phase separation (Figure 4—figure supplement 1b) is consistent with the cross-correlation results shown in Figure 4c and suggests a continuous lag of approximately 45° between the two competing memories. The results of these two analyses on the continuous fidelity timecourses are thus consistent with the phase shift identified when looking at the classifier-peak-to-hippocampal-theta locking, and provide further evidence for a temporal segregation between target and competitor reactivations during the last retrieval repetition.

To remember a specific experience, the respective memory often needs to be selected against overlapping, competing memories. The processes by which the brain achieves such prioritisation are still poorly understood. We here tested a number of predictions derived from the oscillating interference resolution model (Norman et al., 2006). In line with its predictions, the present results demonstrate that the neural signatures of two competing memories become increasingly phase separated over time along the theta rhythm. Furthermore, larger phase differences were associated with fewer intrusion errors from the interfering memories. These findings support the existence of a phase-coding mechanism along the cycle of a slow oscillation that adaptively separates competing mnemonic representations, minimising their temporal overlap (Lisman and Idiart, 1995; Norman et al., 2006).

Based on the oscillating interference resolution model (Norman et al., 2006), we hypothesised that target memories would be strengthened and become decodable at an earlier (higher inhibition) phase (see Figure 1d), while competing associates would be weakened, with time windows of their reactivation gradually shifting to a later (low inhibition) phase. The phase separation between the two competing memories should thus increase the more often the competing memories are being reactivated, and we expected to find an observable difference in phase separation after a number of repeated recalls. To further test whether a large phase segregation is beneficial for memory performance, we related phase distance to the number of behavioural intrusion errors. Supporting our hypotheses, in the third and final recall repetition target and competitor memories showed a significant phase shift with an average distance of 34° (Figure 4a and b). Larger phase separation angles in individual participants were related to lower levels of behavioural interference. Participants with low levels of memory intrusions showed a significant phase difference of 57°, while high-intrusion participants showed a non-significant 7° phase shift on average (Figure 4b). Also in the last repetition, a significant phase modulation was found for both target and competitor memories such that the fidelity of their neural reactivation was rhythmically modulated by the dominant 3 Hz rhythm (Figure 3e and f). Finally, a significant cluster of cross-correlation of the continuous target and competitor decoding timecourses also suggested an average phase lag of 30°, complementing the analyses that explicitly relate classification performance to hippocampal theta phase. Though deviating from the pre-registered hypotheses in some respects as discussed below, these results largely confirm our predictions derived from the oscillating interference resolution model (Norman et al., 2006), supporting a central role for slow oscillations in orchestrating memory recall by temporally segregating potentially interfering memories.

Based on the same model (Norman et al., 2006), we also predicted that across repeated retrieval attempts, target memories would be strengthened and competitors become less intrusive. Though not tested behaviourally in this study, repeated recall is known to induce enhancement of the retrieved memories (Karpicke and Roediger, 2008; Rowland, 2014) and forgetting of non-retrieved, competing memories (Anderson et al., 1994). Evidence for the up- and downregulation of neural target and competitor patterns, respectively, has previously been shown in fMRI studies (e.g. Kuhl et al., 2007; Wimber et al., 2015). In this work, we found neural evidence for both target strengthening and competitor weakening within the time window of maximum memory reactivation (Figure 2h). The increase in target evidence was more robust than the decrease in competitor evidence, with the latter not surviving multiple comparisons correction across the entire recall time window. It is possible that competitor decoding across all trials was too noisy in the first place, especially when focusing on trials where participants correctly answered both follow-up questions, and where neural patterns should thus be dominated by retrieval of the target memory. When instead analysing only incorrect trials, we found moderate evidence for the neural reactivation of competitor memories (Figure 2—figure supplement 2d). The relatively stable levels of competitor reactivation over repetitions might also be due to the lack of feedback in our task, allowing little adjustment of incorrect responses, as also reflected in the flat behavioural intrusion index (Figure 2b, Figure 2—figure supplement 1a). Overall, however, and in line with recent EEG work (Bramão et al., 2022), our results suggest that dynamic changes in overlapping memories can be tracked using MEG.

Decoding of target and competitor memories in the high-interference (CC) condition was generally less robust than decoding of target memories in the low-interference (NC) conditions. Generally, one challenge for time-resolved decoding of reactivated memory content is the considerable variance in the timing of memory recall across trials, conditions, and participants, likely affecting the timing in neural pattern reinstatement. Such variance can be rectified, at least to a degree, when aligning the timelines to the button press that indicates subjective recall, as shown by our response-locked analyses leading to more robust target decoding (see Figure 2f and g). There are, however, several reasons why we pre-registered our analysis locked to the onset of the memory cue. Most importantly, previous work suggests that the phase of theta oscillations is reset by a memory cue and remains relatively stationary for a period of time (see Rizzuto et al., 2006; Ter Wal et al., 2021), such that the locking of memory reactivations to the theta rhythm should be most coherent when aligned to cue onset. Note that all crucial analyses comparing phase shifts of target and competitor reactivations were conducted on the level of single trials. Therefore, these analyses are not affected by whether or not the reactivation maxima are consistent in time across trials and participants, even though such inconsistencies will dilute the average cue-locked decoding performance.

The functional relevance of slow oscillations in regulating the excitation–inhibition balance of specific neural assemblies has featured strongly in other computational models too, and these models are supported by our findings at various levels. Most relevant in the present context is the theta-gamma model (Lisman and Idiart, 1995; Lisman and Jensen, 2013), which assumes that the coupling of gamma bursts to specific theta phases provides a timing mechanism for ordering sequences of items in working memory. Here, a gamma burst reflects the firing of a local cell assembly representing a distinct unit of information, while the phase of the slower theta oscillation rhythmically modulates the excitability of these local neural assemblies, and feedback inhibition allows for an organised, non-overlapping firing sequence across the theta cycle. In humans, studies using episodic and working memory tasks have provided evidence for such a theta-gamma code (Bahramisharif et al., 2018; Canolty et al., 2006; Fuentemilla et al., 2010; Griffiths et al., 2021; Herweg et al., 2020; Heusser et al., 2016; Karlsson et al., 2022; Reddy et al., 2021; Ten Oever et al., 2020; Ter Wal et al., 2021). The oscillating interference resolution model (Norman et al., 2006) and theta-gamma phase-coding models share the fundamental assumption that slow oscillations regulate the excitation–inhibition balance of local neural assemblies (Buzsáki, 2006). However, they do differ in their theoretical scopes. Theta-gamma code models were developed to explain how the brain can handle ongoing states of high attentional or working memory load, where multiple distinct items need to be kept separated and organised. The oscillating interference resolution model (Norman et al., 2006) is a learning model and thus explicitly oriented towards optimising future network states. It employs fluctuating inhibition to actively shape memory representations, with the goal to minimise future overlap and interference.

To our knowledge, no study in humans has explicitly tested for phase coding when several overlapping memories compete for retrieval. However, one intracranial study using a virtual navigation paradigm showed that the neural representations of potentially interfering spatial goal locations are coupled to distinct phases of the hippocampal theta rhythm (Kunz et al., 2019). Phase precession and theta coupling of neuronal firing during goal-directed navigation have also been shown in human hippocampal single-unit recordings (Qasim et al., 2021; Watrous et al., 2018). The present results directly support the idea that phase coding can temporally separate co-active, competing memory representations in the human brain. They additionally suggest that such a code adaptively evolves across repeated target retrievals, in line with the idea that oscillating excitation–inhibition can optimise learning with respect to future access to the relevant target memory.

A second prominent computational model focuses on different functional operations within the hippocampal circuit at opposing phases of the theta rhythm, with one half of the hippocampal theta cycle devoted to the encoding of new incoming information, and the other half to the recovery of internally stored information (Hasselmo et al., 2002; Kunec et al., 2005). In humans, previous EEG work Kerrén et al., 2018 and a recent study investigating rhythmic modulations of reaction times Ter Wal et al., 2021 have provided evidence for such process separation. In the present study, target and competitor reactivations peaked at distinct theta phases, with an average separation angle of approximately 35°, and 57° in the low-intrusion group that presumably reached optimal interference resolution. This observation is consistent with a framework where both targets and competitors are reactivated during the retrieval portion of a theta cycle (Hasselmo et al., 2002; Kunec et al., 2005), but with sufficient phase separation to allow for differentiation in time. Note, however, that the oscillating interference resolution model (Norman et al., 2006) dedicates the entire theta cycle to retrieval operations (as depicted in Figure 1d) and would thus predict temporal distances of up to 180° between targets and competitors. The two models may not necessarily be incompatible, especially when considering that encoding and retrieval computations take place in different subcircuits of the hippocampus (Hasselmo et al., 2002). Future modelling work is needed, however, to better understand the learning dynamics across the full theta cycle and reconcile the apparent theoretical conflict between the two frameworks.

Other predictions of the oscillating interference resolution model (Norman et al., 2006) relate to the neural overlap in spatial patterns and have been tested primarily in fMRI studies. One such prediction is the non-monotonic plasticity hypothesis, derived from the same learning dynamics along the oscillatory cycle as described above. If a competitor is sufficiently strong to be co-activated in the high-inhibition (target) phase, it will benefit from synaptic strengthening of common connections and thus be integrated with the target memory. Moderately co-active competitors, on the other hand, will be subject to synaptic depression and as a result become differentiated from the target memory (Ritvo et al., 2019). Evidence for non-monotonic plasticity comes from fMRI work that directly tracks changes in the similarity of target and competitor representations in terms of their spatial patterns (Hulbert and Norman, 2015; Wammes et al., 2022), and the representational changes dependent on the level of (co)activation of overlapping memories (Detre et al., 2013; Lewis-Peacock and Norman, 2014; Poppenk and Norman, 2014; Wang et al., 2019). Indirectly related, fMRI studies investigating representational change resulting from repeated learning or recall have also found evidence for differentiation or integration of overlapping memories in hippocampus, late sensory cortex, and prefrontal cortex (Favila et al., 2016; Hulbert and Norman, 2015; Kim et al., 2017; Kuhl et al., 2010; Schlichting and Preston, 2015; Wimber et al., 2015), and sometimes observed both in the same study but in different regions (Molitor et al., 2021; Schlichting and Preston, 2015). The present study was not designed to track representational changes, for example, in terms of comparing target and competitor representations before and after repeated recall. Arguably, however, there is a straightforward agreement between spatial and temporal separation in this network model. Strong competitors would tend to co-activate in the high-inhibition phase and thus be co-strengthened and integrated via LTP (Bliss and Lomo, 1973; Hebb, 1949), whereas moderately co-active competitor memories would tend to co-activate only at lower levels of inhibition, which according to the model would lead to LTD. Our data support the idea that low temporal separation is related to integration, behaviourally. The high-intrusion group had a mean phase difference of 7° between target and competitor memories, theoretically in line with a time window of strong co-firing hence synaptic strengthening. Integration might thus have led to higher amounts of intrusions (Brunec et al., 2020). In the low-intrusion group, more pronounced phase separation between target and competitor memories (57° on average) might have led to only moderate co-firing and hence promoted differentiation, resulting in lower levels of behavioural interference. Although speculative, temporal separation could be a prerequisite for spatial differentiation. Future studies, combining high temporal with high spatial resolution, and paradigms to track the representational distance of overlapping memories, are needed to fully understand these dynamics.

A priori we included the entire theta band in our pre-registration as previous studies have indicated large variations in the peak theta frequency in humans (Goyal et al., 2020; Jacobs, 2014; Kerrén et al., 2018; Lega et al., 2016; Ter Wal et al., 2021). For example, in our previous study we found that reactivations of target memories were most consistently coupled to a hippocampal 7–8 Hz oscillation, even though a slower 3–4 Hz peak was also present (Kerrén et al., 2018). In another study, Ter Wal et al., 2021 found that slower 2–3 Hz oscillations dominated behaviour and hippocampal signals specifically when responses depended on memory, consistent with other intracranial work reporting pronounced slow theta oscillations in the human hippocampus during memory tasks (Goyal et al., 2020; Lega et al., 2012). In working memory, it has even been demonstrated that theta oscillations slow down with increasing memory load (Fell et al., 2011; Wolinski et al., 2018), in line with the theoretical idea that slower rhythms accommodate better phase separation (Lisman and Idiart, 1995; Lisman and Jensen, 2013). Descriptively, such theta slowing was evident in the present data when comparing the frequency profiles of rhythmic memory reactivation between NC and CC conditions, where conditions in which only one associate needs to be remembered showed slightly faster rhythmic peaks (4–5 Hz) than the condition in which two associates compete for retrieval (3–4 Hz, see Figure 3—figure supplement 1). While offering a possible explanation why slower oscillations are often observed in tasks with high memory demands, this post-hoc interpretation will need to be corroborated by empirical evidence in future studies.

Phase coding offers an elegant theoretical solution to temporally segregate the processing of potentially interfering information. Much empirical evidence for phase sequencing along the hippocampal theta oscillation comes from spatial navigation work, both in rodents and humans (Herweg et al., 2020; Kunz et al., 2019; O’Keefe and Recce, 1993; Reddy et al., 2021). We here demonstrated that phase coding facilitates the separation of overlapping, associatively linked memories. Together with the computational model used to derive these predictions, these findings offer a possible mechanism utilised by the human brain to resolve competition between simultaneously active memories. More generally, they add to a growing literature showing that slow oscillations orchestrate the intricate timing of neural processing with direct, observable effects on behaviour.

All data and code used in this project are made available on OSF and Zenodo.

1. 1. Kerrén C
   2. van Bree S
   3. Griffiths BJ
   4. Wimber M

   (2022) Zenodo

   Phase separation of competing memories along the human hippocampal theta rhythm.

   https://doi.org/10.5281/zenodo.6602054
2. 1. Kerrén C
   2. van Bree S
   3. Griffiths BJ
   4. Wimber M

   (2022) Open Science Framework

   ID 10.17605/OSF.IO/EVGAW. Target and competitor reactivation in episodic memory.

   https://osf.io/evgaw/?view_only=fbb676ccb2e74ccbb16d5d8aa8f9c58f

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Decision letter after peer review:

Thank you for submitting your article "Phase separation of competing memories along the human hippocampal theta rhythm." for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Laura Colgin as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Ehren L Newman (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

All the 3 reviewers are impressed by the preregistration study, which makes the hypothesis-testing clear and well-grounded. They also appreciate the large consistency between the results and the tested hypotheses and concur that the findings would be of broad interest to memory and learning fields. Meanwhile, they have also raised several major concerns about the results (see major points below and specific comments by each reviewer).

Essential revisions:

(1) Concerns were raised about the lack of significant memory reactivations for the CC condition, even at the 3rd repetition when memories are supposed to increasingly be reactivated. The authors collapsed target and competitors (Supp Figure 3B) aiming to support memory reactivations, but the rationale is unconvincing since target and competitors are claimed to occur at different phases and should not be combined for decoding.

(2) Related to the 1st point, by comparing reactivations of the 3rd and 1st repetition, the authors suggest a reactivation increase over repetitions. The authors could also consider comparing the 3rd repeat and chance level to examine the memory reactivation at the 3rd repetition. Moreover, how do they reconcile the different reactivation latencies for CC and NC conditions?

(3) Regarding the theta rhythm in classifier fidelity and its relationship to midline theta-band signal, the current results support the phase separation only at the 3rd repetition. It would be important to see how the rhythm of classifier fidelity changes over repeats as well as its characteristics for the NC condition. Moreover, is there any way to confirm that the theta-band rhythm emerges from the hippocampus?

(4) There are other behavioral analyses that could be done to relate the neural findings to the behavior in addition to the intrusion effect (for example: probability of intrusion, RT for memory recall, overall accuracy, etc.). Please see details below in the reviewers' comments.

(5) The authors could consider adding representative data examples to illustrate the phase separation findings.

Reviewer #1 (Recommendations for the authors):

1. The decoding performance didn't reach statistical significance for both target and competitor (Figure 2e), which makes me skeptical to what extent the decoding courses truly denote the memory reactivations. If not, how could the phase-based memory reactivation conclusion be supported? I noticed that the effect mainly occurred at the 3rd repetition recall. Thus, I would recommend that the authors should at least provide evidence that the decoding performance was significant for the target and competitor in the 3rd repetition.

2. The target and competitor showed similar profiles instead of an out-of-phase manner (Figure 1d). Their average decoding performance even showed significant reactivation (supplemental figure 3b). The results seem to be not consistent with the major phase separation findings. Moreover, to flesh out the phase separation results, I would recommend the authors provide typical data examples from their results explicitly illustrating the phase separation profiles, e.g., a representative subject, etc.

3. The key hypothesis is that the neural representation of the target would become stronger, and phase separation would become more prominent across repetitions. They did find stronger reactivation and phase separation for the 3rd repetition compared to the 1st repetition. Meanwhile, is there any corresponding behavioral evidence such that the overlapping memories become less distracting across repetitions?

4. There is no test to confirm the significant theta rhythm in the hippocampus detected in the present study. I think the authors should provide neural evidence backing up that the theta-band rhythm analyzed in their study indeed derives from the hippocampus.

Reviewer #2 (Recommendations for the authors):

I found much to appreciate about this manuscript and work. Much of that is summarized in my 'Public review' so I won't repeat it here.

I had no 'substantial concerns' about the quality of the work or interpretations presented. That is to say, none of the following thoughts that I'll share next should be seen as show-stopping. I will share these thoughts nonetheless in case they can support the growth of this work.

– The reasons why the reactivation effects should only become visible in the 3rd repetition were not clear to me. I struggle to come up with a just-so story based on the Norman et al. 2006 model or otherwise to understand these. Some assistance with this could help.

– There is a strange discrepancy in the frequency band of the frontal midline theta (~7 Hz) and the frequency of the phase modulated reactivations. I did not see any attempt to reconcile this. Are they simply two totally different things, the 7Hz, and 3Hz thetas?

– There are other tests that could be done to relate the reactivation dynamics to the behavior than are described. For example, one could compare the degree of reactivation to the probability of intrusion. Newman et al., 2010 – https://pubmed.ncbi.nlm.nih.gov/20181622/ – for example did something similar. At the coarsest level, the probability of an intrusion should be lowest for trials where there was no evidence of competitor reactivation.

Reviewer #3 (Recommendations for the authors):

There are some suggestions that could improve the overall conclusions one can draw from the manuscript.

The manipulation of repeating the retrieval period is interesting and allows for some novel hypotheses and questions. The authors use a measure of intrusions (by assessing how often subjects select the competitor subordinate category) and find that this is independent of the recall trial number. How about accuracy for the CC conditions?

The authors find an increase in fontal theta that lateralizes to the right for the competitive condition (CC). While the authors find the lateralization surprising, and instead expected a greater increase over midline structures, the lateralization here may be more consistent with recent literature implicating the right DLPFC in action inhibition. In this case, the inhibited action may be related to the competitor's memory. Alternatively, if there is a conflict signal that is relevant for this task, although the authors look for this conflict signal during retrieval it may also be helpful to identify whether a conflict signal is present during the encoding portion of the task when the competitor memory is introduced.

One of the advantages of the task design is that the subordinate categories allow for classifiers to be built that can decode which memory is being reactivated. The authors use an LDA-based classifier on the MEG sensor amplitudes to construct and test the classifiers. Interestingly, when decoding the retrieval data, the classifiers are significant in the NC conditions a full 2.5 seconds after the cue. This seems like a very long time, as the authors acknowledge. In the description of the task, the authors report that the subjects indicate when they have the picture in mind, and then indicate the supra- and subordinate categories. Presumably, then, this means there is a response time. How does this activation compare to the response time?

The authors report that there is no significant reinstatement of the target category during correct competitive trials as compared to the competitor category, although they claim that the classifier performance peaks at a similar time to the NC condition. This seems a bit concerning because one would expect to see some evidence of reactivation if subjects are making the correct decision. How should this be reconciled? Instead, the authors find that the evidence for target memory increases over recalls, which they offer as evidence that these memories are increasingly being reactivated. However, classifier performance peaks at a different time than when you see reinstatement in NC condition and is in fact earlier. Why?

It would be helpful if the authors could please clarify Supp Figure 3B. What does it mean when they collapse targets and competitors? Do they mean that the classifier can decode up either one?

If the authors are seeing increasing evidence of target information over several repeats, then this raises the question as to whether the classifier of subordinate target category would only work if only looking at the third repeat? The authors test this by comparing classifier performance in the 1st v 3rd repeat. However, how about comparing performance in the 3rd repeat versus chance?

The authors then tie in classifier decoding with the phase of the theta rhythm in the hippocampus. First, the classifier fidelity itself appears to have a 3 Hz rhythm. They then compute the modulation index (MI) of classifier fidelity to the hippocampal 3Hz phase. Classifier fidelity is only modulated by the hippocampal phase during the 3rd retrieval repetition. Similarly, we only see significant phase differences between target and competitor in the last repetition. This would seem to support the hypothesis that repetition leads to greater separation and better memory. However, it would be then helpful to know how the rhythm of classifier fidelity (the underlying 3Hz rhythm of classifier performance) changes across repeats. Is this fixed, or does this also exhibit changes with repetition? Is this also the case in the NC condition? For the NC condition, how do the phases modulation compare with the phases of target and competitors in the CC condition?

Essential revisions:

(1) Concerns were raised about the lack of significant memory reactivations for the CC condition, even at the 3rd repetition when memories are supposed to increasingly be reactivated. The authors collapsed target and competitors (Supp Figure 3B) aiming to support memory reactivations, but the rationale is unconvincing since target and competitors are claimed to occur at different phases and should not be combined for decoding.

We agree that evidence for memory reactivation in the critical CC condition was weak in the previously reported (and preregistered) analyses. We added new analyses showing that robust memory reactivation is present in the CC condition when realigning the timelines to the response (i.e., time point of subjective recollection), rather than the cue onset, which we hope will reassure the readers and reviewers of the validity of our decoding algorithm and approach. We also clarify that the collapsed accuracy for target/competitor decoding only served the particular purpose of identifying a common time window of reactivation.

Regarding the first point of low decoding accuracy in the CC, we reasoned that there is considerable variability in the timing of memory reactivation across trials and participants that will make it difficult to see clear peaks of decoding accuracy when averaging across participants. The revised manuscript now includes several analyses where we realigned decoding to the button press that subjects made on each trial to indicate they recalled the associated memory. This approach (see Author response image 1) revealed significant clusters of classification accuracy preceding the button press by approx. 200ms. In Author response image 1B, we also show that target classification on correct trials is significantly stronger than on incorrect trials, indicating that our method of detecting memory reinstatement is sensitive to behavioural memory success. Lastly, we also find evidence (though not surviving cluster-correction) of competitor reactivation when only using incorrect trials where the competing memory is more likely to dominate, both when doing the analysis response-locked (Author response image 1C) and cue-locked (Figure 2 – supplement 2D; Author response image 1D), the former again in a similar time window around 200ms before response.

Author response image 1

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It is important to emphasise that we preregistered our decoding analyses locked to cue onset in order to maximise our chances to observe theta-locked memory reactivations. Previous work showed that the phase of theta oscillations is reset by a memory cue (Rizzuto et al., 2006; ter Wal et al., 2021), suggesting that the locking of memory reactivations to the theta rhythm will be most coherent for a period of time following cue onset. Since we conducted the crucial analyses of classifier-to-theta locking on the level of single trial fidelity values, comparing phase shifts of target and competitor, the analyses will not be affected by whether or not these reactivation peaks are consistent in time across participants, as they would need to be to reveal robust average cue-locked decoding performance.

Regarding the analysis collapsing of target and competitor decoding, as seen in Supp Figure 3B [now Supp Figure 2E], we understand that this point needs clarification. This analysis was conducted purely for the purpose of obtaining an unbiased time window (i.e., not biased either target or competitor decoding, as suggested as good practice in Cohen (2014)) in which further analyses on the up- and down-regulation of the two memories across repetitions could then be performed (shown in Figure 2 – supplement 2E and Figure 2H). We clarify this rationale in a sentence on p.49 l. 8-12.

For all other analyses, target and competitor decoding was kept separate.

(2) Related to the 1st point, by comparing reactivations of the 3rd and 1st repetition, the authors suggest a reactivation increase over repetitions. The authors could also consider comparing the 3rd repeat and chance level to examine the memory reactivation at the 3rd repetition. Moreover, how do they reconcile the different reactivation latencies for CC and NC conditions?

We followed this suggestion and compared the third repetition of target decoding with chance (50%). The analysis revealed a significant cluster emerging from 1.81 to 2.08 seconds after cue onset. However, this cluster did not survive a more stringent cluster-based permutation correction for multiple comparisons across time (p_corr = .07). We added this information on page p.13 l. 9-10 of the revised manuscript.

Regarding the difference in reactivation latencies, we agree that somewhat surprisingly, decoding performance peaked earlier in the CC than in the NC condition when using the contrast of 3^rd vs 1^st repetition in the CC condition. Note, however, that decoding of the target memory in the NC and the CC condition (Figure 2D compared to Figure 2E blue) followed a very similar timecourse overall when comparing against chance. Moreover, there is an earlier peak around 1-1.5 seconds in the NC condition that does not reach significance when using a stringent cluster-based permutation analysis, and the cluster-corrected results are thus not entirely conclusive. Also see our response to point #4 of reviewer #3.

(3) Regarding the theta rhythm in classifier fidelity and its relationship to midline theta-band signal, the current results support the phase separation only at the 3rd repetition. It would be important to see how the rhythm of classifier fidelity changes over repeats as well as its characteristics for the NC condition. Moreover, is there any way to confirm that the theta-band rhythm emerges from the hippocampus?

We agree that these are important points to clarify with further analyses. To address the first point about the dominant frequency of memory reactivation in the various conditions, we used IRASA, following the same procedure as in the manuscript, on the fidelity timecourses in the NC and the CC conditions, and separated them into individual recall repetitions. The resulting spectra of the fidelity values are shown in Figure 3 – supplement 1. Reassuringly, the frequency profile and peak frequency did not show an obvious change across repetitions in either condition. However, the NC condition has a slightly faster frequency profile than the CC, with a peak around 4-5Hz (NC) compared with 3 Hz (CC). On p.15 l. 4-11 of the revised manuscript, we briefly discuss that the faster rhythm in the NC compared to the CC condition could reflect an increase in memory load in the CC condition, with participants keeping two memories in mind, consistent with reports of a corresponding slow-down of the theta frequency in the working memory literature (Wolinski et al., 2018).

To more thoroughly analyse the regional specificity of the hippocampal theta and to validate our source analysis, we conducted an additional analysis at source level where we compare the frequency spectrum of our hippocampal region of interest (ROI) with two control areas (superior occipital lobe and primary motor cortex) that should not show memory-related theta oscillations. Figure 3 – supplement 2C-D shows the difference in the raw power spectrum between the hippocampal ROI and each of the two control regions, averaged across 0-2 seconds after cue onset, and showing power differences at frequencies from 1 to 30Hz for visualisation purposes. To statistically evaluate differences in the theta band, we averaged over 3-8 Hz (the frequency band consistently used throughout the manuscript) and used a one-sided t-test to compare the spectrum against that of each control region (Bonferroni-correcting the p-level for the two repeated comparisons). We find that theta power in the hippocampus is significantly increased compared to superior occipital cortex (t(20) = 2.37, p_corrected = .0279), with a maximal power difference at 4 Hz, and a qualitatively similar but non-significant increase when contrasting power over hippocampal and precentral virtual channels (t(20) = 1.98, p_corrected = .0622), again with a maximum difference at 4Hz. The new results are described on p. 16 l. 6-9 of the revised manuscript.

We hope these two results will assure the reviewers/readers that (1) our source localization approach is able to isolate distinct frequency profiles in different ROIs, with the expected theta increase in the hippocampus; and (2) that the frequency profile of the classifier is very similar across repetitions and thus unlikely to contaminate the phase-modulation results. Of course, intracranial EEG data would be required to unequivocally pinpoint the exact source(s) of the 2-4Hz theta rhythm.

(4) There are other behavioral analyses that could be done to relate the neural findings to the behavior in addition to the intrusion effect (for example: probability of intrusion, RT for memory recall, overall accuracy, etc.). Please see details below in the reviewers' comments.

We thank the reviewers for pointing out several interesting analyses that provided further understanding of the results. In response to reviewer 2’s point 3, we conducted an analysis relating neural competitor reactivation to the level of behavioural intrusions. We extracted the average decoding accuracy of competitor memories across repetitions from 0 to 1 second after cue onset (following Newman and Norman, (2010) as suggested by the reviewer) and correlated this neural index with the probability of intrusions in the first repetition. A significant positive correlation (r(1,20) = .446, p = .04) was found indicating that stronger neural reactivation of the competitor memory was related to a higher probability of intrusions. See p.13 l. 23 – p. 14 l. 4. This new finding also strengthens the point that classification performance is meaningfully related to behaviour (see Essential Revisions #1 above).

Second, in response to reviewer 3’s point #1, we further investigated potential changes in behavioural accuracy across repetitions in the CC condition, and found no significant change, (Z = -.73, p = .47; Wilcoxon signed-rank test of linear slope against zero). This result is in line with the previously reported result on the intrusion score and is now reported in the revised manuscript on p.8 l.2-5 and as a Figure 2 – supplement 1A.

Lastly, in response to reviewer 3’s point #3, we analysed the timing of the subjective button presses and how it changes across repetitions. Response times significantly shortened in all conditions (NC2: rep1: 2.01 sec, rep2: 1.75 sec, rep3:1.68 sec; Z = -4.17, p <.01; CC: rep1: 2.29 sec, rep2: 2.05 sec, rep3:2.04 sec; Z = -2.89, p = .004; NC1: rep1: 2.55 sec, rep2: 2.37 sec, rep3:2.26 sec; Z = 3.03, p = .003; z-values are based on Wilcoxon signed-rank tests of linear slope against zero). These results are mainly included for descriptive purposes and show that response times roughly coincided with the temporal latency of our decoding timecourses. We report this response time analysis on p.8 l. 10-16 of the revised manuscript.

(5) The authors could consider adding representative data examples to illustrate the phase separation findings.

In response to this comment, and for transparency, we now show in Figure 4 – supplement 1 each participant’s fidelity timecourse of target decoding relative to competitor decoding, filtered at the relevant 3Hz frequency. Visual inspection shows that target and competitor decoding is often phase shifted and does generally not tend to go hand in hand over the average trial timecourse. To quantify this phase shift, we subtracted, for each participant and time bin, the phase angle of the target from the angle of the competitor (in complex space). The resulting phase separation is highly consistent with the one we reported in the original manuscript when relating the peak fidelity values to the hippocampal 3Hz phase.

We also conducted an additional cross-correlation analysis on the fidelity timecourses of target and competitor memories, to further corroborate the phase separation on a single-trial level. This analysis revealed a significant and temporally extended cluster of phase lag starting approximately 1 sec after cue onset and lasting until the end of the trial, suggesting that competitor reactivation followed target reactivation by a lag of on average 30 degrees on the 3Hz theta cycle. These new findings can be found in revised Figure 4C of the main manuscript.

We hope that together, these illustrative changes and complementary analyses will provide further understanding of the phase separation findings. Note that the fidelity timecourses in the original manuscript (previously Figure 1D) served a purely illustrative purpose and showed simulated not real data. We understand that this illustration was more confusing than helpful, and changed this figure panel to more clearly represent the predictions based on the Norman et al., (2006) model of interference resolution.

Reviewer #1 (Recommendations for the authors):

1. The decoding performance didn't reach statistical significance for both target and competitor (Figure 2e), which makes me skeptical to what extent the decoding courses truly denote the memory reactivations. If not, how could the phase-based memory reactivation conclusion be supported? I noticed that the effect mainly occurred at the 3rd repetition recall. Thus, I would recommend that the authors should at least provide evidence that the decoding performance was significant for the target and competitor in the 3rd repetition.

We agree with the reviewer and now include additional evidence demonstrating that our decoding algorithm is able to pick up memory reactivations with significant above-chance accuracy, including in the competitive condition. Note that while this analysis was not pre-registered, it will hopefully assure the reviewers/readers that we are looking at meaningful reactivation peaks when relating classifier performance to theta phase. In the new analysis, we realigned the decoding timelines to the time point of subjective recollection (i.e., button press to indicate the memory was recalled), instead of cue onset as for all pre-registered analyses. When doing so (see Author response image 1A), we do find significant clusters of target classification accuracy preceding the button press by approx. 200ms. In Author response image 1B, we also show that target classification on correct trials is significantly stronger than on incorrect trials, indicating that our method of detecting memory reinstatement is sensitive to behavioural memory success. Lastly, we also find evidence (though not surviving cluster-correction) of competitor reactivation when only using incorrect trials, both response-locked (Author response image 1C) and cue-locked (Author response image 1D), the former again in a similar time window around 200ms before response.

The most likely reason why a cue-locked decoding approach produces less robust decoding accuracy is the considerable variance in the timing of memory recall (and thus neural pattern reinstatement) across trials, conditions, and participants (see also Reviewer #3 point #3, where we show that RTs differ across repetitions). Such temporal variability is rectified to some degree when aligning the timeline to the button press. Having said that, there are several reasons for why we pre-registered our analyses locked to the onset of the cue. The most important reason is that the phase of theta oscillations is thought to be reset by a retrieval cue (see: Rizzuto et al., 2006; ter Wal et al., 2021), and the locking of memory reactivations to the theta rhythm can thus be expected to be most coherent when aligned to cue onset. Importantly, we conducted the crucial analyses of classifier-to-theta locking on the level of single trial classification peaks, comparing phase shifts of target and competitor peaks. These analyses will thus not be affected by whether or not the reactivation peaks are consistent in time across trials and participants, as they would need to be to reveal robust average cue-locked decoding performance. We added the response-locked target decoding to main Figure 2 (panel g,h) in the revised manuscript, and competitor decoding to Figure 2 – supplement 2D; the description of the new results can be found in the main text on p. 12 l. 15 – 23.

2. The target and competitor showed similar profiles instead of an out-of-phase manner (Figure 1d). Their average decoding performance even showed significant reactivation (supplemental figure 3b). The results seem to be not consistent with the major phase separation findings. Moreover, to flesh out the phase separation results, I would recommend the authors provide typical data examples from their results explicitly illustrating the phase separation profiles, e.g., a representative subject, etc.

We thank the reviewer for pointing out these apparent contradictions. First, it is important to note that Figure 1D served a purely illustrative purpose and shows simulated not real data. In the revised figure, we have now made this panel easier to interpret. Also note that, following the same reasoning as in our response to point #1, a trial-by-trial phase shift between target and competitor reactivation peaks would not necessarily be visible in the cross-subject average of decoding performance, because the timing and absolute phase of target and competitor reactivation can vastly differ in each participant. The average shown in the previous manuscript can only roughly indicate the approximate time window where decoding tends to increase when averaging across all subjects.

To flesh out the phase separation results, we followed the reviewer’s recommendation and now show each individual’s fidelity time course of target and competitor decoding in Figure 4 – supplement. Visual inspection shows that target and competitor reactivation is temporally shifted in most participants, often even showing phase opposition. We also conducted a set of new analyses to quantify this phase shift at the level of single trials and single participants. First, we filtered each participant’s decoding fidelity time course at the dominant 3Hz frequency, separately for target and competitor decoding in the 3^rd repetition of the competitive condition. We then subtracted, for each time bin, the phase angle of the target from the angle of the competitor (in complex space), and averaged the phase difference on a group level. The resulting phase separation looks very similar to what we report in the original manuscript when relating the peak fidelity to the hippocampal 3Hz phase. Another way of quantifying the phase lag is to compute a cross-correlation between the two signals, which we did using a sliding-window (to preserve some time information) and calculating the cross-correlation for each 330ms time bin. This analysis revealed a significant cross-correlation cluster starting around 1 sec after cue onset and lasting until the end of the trial, with a maximum lag of 30 degrees (Figure 4C). Together, the two new analyses are highly consistent with the phase shift reported in our pre-registered analysis.

3. The key hypothesis is that the neural representation of the target would become stronger, and phase separation would become more prominent across repetitions. They did find stronger reactivation and phase separation for the 3rd repetition compared to the 1st repetition. Meanwhile, is there any corresponding behavioral evidence such that the overlapping memories become less distracting across repetitions?

This was indeed one of the pre-registered behavioural contrasts and reported on p.8 l.1 of the original manuscript. Contrary to our predictions, we found no significant difference in intrusion scores across repetitions, and this relatively stable pattern of intrusions across repeats might be due to the lack of feedback in our task, as discussed on p.23 l.19-22 (also see Reviewer 3’s comment 2; Figure 5).

4. There is no test to confirm the significant theta rhythm in the hippocampus detected in the present study. I think the authors should provide neural evidence backing up that the theta-band rhythm analyzed in their study indeed derives from the hippocampus.

We agree that the original manuscript was lacking evidence for the specificity of the hippocampal theta. We conducted a new analysis to address this concern, computing the frequency spectrum of the raw (trial) signals extracted from virtual sensors in the hippocampus, and comparing it to the spectrum of two control areas (superior occipital lobe and primary motor cortex) that should not show memory-related theta oscillations. Figure 3 – supplement 2 shows the difference in the power spectrum between the hippocampal ROI and each of the control regions, averaged across 0-2 seconds after cue onset, and showing power differences at frequencies from 1 to 30Hz for visualisation purposes. To statistically evaluate differences in the theta band, we averaged power over 3-8 Hz (the band consistently used throughout the manuscript). We find that theta power in the hippocampus is significantly increased compared to superior occipital cortex (t(20) = 2.37, p_corrected = .0279), with a maximal power difference at 4 Hz. A qualitatively similar but non-significant increase was found when contrasting the spectrum from hippocampal and precentral virtual channels (t(20) = 1.98, p_corrected = .0622), again with a maximum difference at 4Hz. We hope these results will assure the reviewers/readers that our source localization approach is able to isolate distinct frequency profiles in different ROIs, with the expected theta increase in the hippocampus. We added the new results to the result section (p.16 l.5-8) and added two panels to Figure 3 – supplement 2. Of course, MEG source localisation is still an imperfect reconstruction, and we emphasize that while we can show some degree of specificity, intracranial EEG data would be required to unequivocally locate the main source(s) of the 2-4Hz theta rhythm.

Reviewer #2 (Recommendations for the authors):

I found much to appreciate about this manuscript and work. Much of that is summarized in my 'Public review' so I won't repeat it here.

I had no 'substantial concerns' about the quality of the work or interpretations presented. That is to say, none of the following thoughts that I'll share next should be seen as show-stopping. I will share these thoughts nonetheless in case they can support the growth of this work.

– The reasons why the reactivation effects should only become visible in the 3rd repetition were not clear to me. I struggle to come up with a just-so story based on the Norman et al. 2006 model or otherwise to understand these. Some assistance with this could help.

We appreciate this comment and clarify our reasoning in the revised manuscript (p. 4 l.8-21). First, it should be highlighted that in our pre-registration, we hypothesized (i) an increase in target and a decrease in competitor reactivation (i.e., decodability) over repetitions; and (ii) a target-competitor phase difference that exists throughout the task and would increase across repetitions. These hypotheses were only partly confirmed, and we hope we are sufficiently transparent about this in the manuscript. The first hypothesis was motivated by a wealth of empirical studies on retrieval-induced enhancement of target memories and retrieval-induced forgetting of competitor memories (Anderson et al., 1994; Wimber et al., 2015). It can also be deduced indirectly from the Norman et al., (2006) model, which assumes that over time and repetitions of the presumed theta cycle dynamics, weak target nodes are strengthened while overly strong competitor nodes are weakened, leading to better differentiation hence enhanced decodability of the target memory. We now visualise this reasoning in the revised hypothesis graph in Figure 1D, where targets become decodable at increasingly early (higher inhibition) theta phases with gradually less interference from competitors.

The second hypothesis of increasing phase separation over time is based directly on the model’s dynamics. Weak target nodes do not survive high inhibition initially and thus only activate during medium and lower inhibition phases of the theta cycle, however, with repeated strengthening they will become active at an increasingly early, higher-inhibition phase. Therefore, while early in time the target and competitor memories overlap in their peak reactivation phase, this overlap gets reduced by the strengthening and weakening dynamics in the model. Critically, Norman et al. (2006) model these dynamics across several repetitions of a theta cycle, while we instead opted to measure them across recall repetitions, simply because we were not confident that our analysis tools could provide a stable phase estimate for target and competitor reactivation on each theta cycle within a retrieval trial.

– There is a strange discrepancy in the frequency band of the frontal midline theta (~7 Hz) and the frequency of the phase modulated reactivations. I did not see any attempt to reconcile this. Are they simply two totally different things, the 7Hz, and 3Hz thetas?

This is a valid observation and indeed something we did not discuss in the original manuscript. To our knowledge, the relationship between the conflict-related frontal midline theta and the memory-related 2-4Hz theta is still unknown, though both rhythms are consistently found in their respective cognitive domains. The frontal midline theta is typically found at 7-8Hz, like in our own study, and has been related to cognitive conflict/control across domains (Cavanagh and Frank, 2014). In the memory domain, frontal midline theta amplitudes increase with increased competition and decrease with downregulation of the competitor memory (Ferreira et al., 2014; Hanslmayr et al., 2010). Our own finding that power in this frontal theta rhythm increases under conditions of high competition is thus a replication of previous work, and a sanity check that physiological markers of competition increase in the CC compared to the NC condition. A memory-related theta in the human hippocampus is frequently found around 2-3Hz in intracranial recordings (Goyal et al., 2020; Lega et al., 2012; ter Wal et al., 2021), and thus slower than the dominant theta rhythm in the rodent hippocampus. Our findings suggest that the phase separation of competing memories relates to this slower hippocampal rhythm, not the faster frontal rhythm. Beyond this observation, we are not aware of literature linking the two theta rhythms directly, though this would certainly be an interesting field for future studies. Hence, for the time being, we treat these two rhythms as separate phenomena, and clarify our reasoning on p.9 l.11-14 of the revised manuscript.

– There are other tests that could be done to relate the reactivation dynamics to the behavior than are described. For example, one could compare the degree of reactivation to the probability of intrusion. Newman et al., 2010 – https://pubmed.ncbi.nlm.nih.gov/20181622/ – for example did something similar. At the coarsest level, the probability of an intrusion should be lowest for trials where there was no evidence of competitor reactivation.

Thank you for this suggestion. Taking inspiration from the suggested method, we extracted the average decoding across repetitions for competitor memories (using the same trials as in the manuscript) from 0 to 1 second after cue onset (following Newman and Norman, (2010)) and correlated this with the probability of intrusions in the first repetition. This yielded a significant correlation (r(1,20) = .446, p = .04), such that stronger reactivation of the competitor memory was associated with a higher probability of intrusions. This new finding also adds evidence in support of the decoding performance being related to behaviour. See Figure 2 – supplement 1C and p.13 l. 23 – p.14 l.4, of the revised manuscript.

Reviewer #3 (Recommendations for the authors):

There are some suggestions that could improve the overall conclusions one can draw from the manuscript.

The manipulation of repeating the retrieval period is interesting and allows for some novel hypotheses and questions. The authors use a measure of intrusions (by assessing how often subjects select the competitor subordinate category) and find that this is independent of the recall trial number. How about accuracy for the CC conditions?

We thank the reviewer for this suggestion. We did not find a change in behavioural accuracy of CC across repetitions, (Z = -.73, p = .47; Wilcoxon signed-rank test of linear slope against zero). This additional data point is now reported in the revised manuscript on p.8 l.2-5 and as a Figure 2 – supplement 1A. Note that while intrusion scores remain at the same level over recall repetitions, we report new analyses showing that the intrusions do relate to behaviour in a sensible way, such that more neural competitor activation is associated with a higher likelihood of intrusions, see response to Reviewer #2’s point 3.

The authors find an increase in fontal theta that lateralizes to the right for the competitive condition (CC). While the authors find the lateralization surprising, and instead expected a greater increase over midline structures, the lateralization here may be more consistent with recent literature implicating the right DLPFC in action inhibition. In this case, the inhibited action may be related to the competitor's memory. Alternatively, if there is a conflict signal that is relevant for this task, although the authors look for this conflict signal during retrieval it may also be helpful to identify whether a conflict signal is present during the encoding portion of the task when the competitor memory is introduced.

We thank the reviewer for these comments and address them as follows. Regarding the right lateralization of the ‘conflict theta’, we added a section to the main results (p. 9 l. 9-11) to highlight that response conflict and response inhibition are indeed often found to engage a right-lateralised frontal network. Regarding the conflict signal during the encoding phase, we agree that a thorough analysis of the memory (re)activation processes occurring already during encoding is very interesting and we are planning such analyses in the future. We do think that a full analysis of the encoding data is beyond the scope of the current paper. Having said that, in response to this reviewer comment, we analysed theta power when participants encode a second competing memory (CC condition) in the learning phase, compared to encoding the same associate a second time. This analysis indeed reveals a non-significant theta power increase in the CC condition with a maximum around 100-300ms after second picture onset, which could be interpreted as conflict signal (see Author response image 2). Note, however, that during encoding, this contrast is naturally confounded with repetition (NC2) vs no-repetition (CC) of an image, and can therefore not isolate the pure competition component. We decided to not include these analyses in the revised manuscript for the time being, although we would be happy to include them in the supplements if the reviewers/editors consider them central to the conclusions.

Author response image 2

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One of the advantages of the task design is that the subordinate categories allow for classifiers to be built that can decode which memory is being reactivated. The authors use an LDA-based classifier on the MEG sensor amplitudes to construct and test the classifiers. Interestingly, when decoding the retrieval data, the classifiers are significant in the NC conditions a full 2.5 seconds after the cue. This seems like a very long time, as the authors acknowledge. In the description of the task, the authors report that the subjects indicate when they have the picture in mind, and then indicate the supra- and subordinate categories. Presumably, then, this means there is a response time. How does this activation compare to the response time?

We thank the reviewer for highlighting this observation, and we further investigated the temporal relationship between memory reactivation and response times in a number of new analyses. Response times for the subjective recollection button press were on average 2.11 sec, and the peak decodability therefore indeed coincides roughly with the response. Response times significantly shortened in all conditions across the three repetitions (NC2: rep1: 2.01 sec, rep2: 1.75 sec, rep3:1.68 sec; Z = -4.17, p <.01; CC: rep1: 2.29 sec, rep2: 2.05 sec, rep3:2.04 sec; Z = -2.89, p = .004; NC1: rep1: 2.55 sec, rep2: 2.37 sec, rep3:2.26 sec; Z = 3.03, p = .003; Wilcoxon signed-rank tests of linear slope against zero). In response to this reviewer comment and Reviewer #1’s point #1 (see Author response image 2 and the above response), we performed an additional analysis locking the decoding to response onset rather than cue onset. This analysis reveals significant clusters of memory reactivation (i.e. decoding peaks) approximately 200ms before the subjective recollection response. We report these new analyses on p.8 l. 10-16 of the revised manuscript.

The authors report that there is no significant reinstatement of the target category during correct competitive trials as compared to the competitor category, although they claim that the classifier performance peaks at a similar time to the NC condition. This seems a bit concerning because one would expect to see some evidence of reactivation if subjects are making the correct decision. How should this be reconciled? Instead, the authors find that the evidence for target memory increases over recalls, which they offer as evidence that these memories are increasingly being reactivated. However, classifier performance peaks at a different time than when you see reinstatement in NC condition and is in fact earlier. Why?

We appreciate that the evidence for target reactivation in the CC was not sufficiently strong based on the (pre-registered) analyses reported previously. As the reviewer also points out, decoding performance peaked earlier in the CC than in the NC condition, at least when based on the comparison of 3^rd vs 1^st repetition (where on the 3^rd repetition, memory recall might indeed have happened earlier than on the 1^st repetition). When comparing target decoding against chance level in the NC and CC conditions (Figure 2D and 2E), they do follow very similar timecourses. Moreover, an earlier peak around 1-1.5 seconds can also be seen in the NC condition but does not reach significance when using a stringent cluster-based permutation statistic, and the results are thus not entirely conclusive. Addressing the concern about general decoding performance and relationship to behaviour, we conducted the above-mentioned response-locked analyses, showing a clear reactivation peak just before the response when aligning trials to the time point of subjective recollection. Moreover, and relevant to the specific point raised here, we also show that this target reactivation is significantly stronger when participants make a correct vs incorrect decision, suggesting there is more neural evidence for the target class when participants correctly retrieve this class. More generally, response-locked reactivation appears to be more robust suggesting to us that there is considerable variance in the timing of memory recall across trials, conditions, and participants (also see Essential Revisions point #1, Reviewer #1 point #1, and Reviewer #3 point #3), and such variability makes it difficult to see clear cue-locked reactivation peaks on. Corresponding changes in the manuscript occur in the revised Figure 2 and in the main text on p. 12 l. (15 – 23).

It would be helpful if the authors could please clarify Supp Figure 3B. What does it mean when they collapse targets and competitors? Do they mean that the classifier can decode up either one?

We understand that this point needs clarification. Of course, the design of our study and all our main analyses are tailored to separate target and competitor reactivation. This specific analysis collapsing across target and competitor reactivation was conducted purely for the purpose of identifying a time window where on average, across all trials and participants, target and competitor reactivation tend to be high, such that we could then use this unbiased time window (i.e., not biased towards target or competitor decoding) to probe for the hypothesized up and down-regulation of target and competitor memories, respectively, across repetitions (as seen in Figure 2 – supplement 2B and Figure 2F). For all other analyses, we did keep target and competitor decoding separate. We clarify this reasoning now in a sentence on p.49 l. 8-12. See also Essential Revisions point #1.

If the authors are seeing increasing evidence of target information over several repeats, then this raises the question as to whether the classifier of subordinate target category would only work if only looking at the third repeat? The authors test this by comparing classifier performance in the 1st v 3rd repeat. However, how about comparing performance in the 3rd repeat versus chance?

We followed the reviewer’s suggestion, and we do also find significant decoding performance for target memories when comparing against chance (50%) in the third repetition, with a significant cluster emerging from 1.81 to 2.08 seconds after cue onset. However, this cluster does not survive a more stringent cluster-based permutation correction for multiple comparisons across time (p_corr = .07). We added this analysis on page p.13 l. 9-10. See also Essential Revisions point #2.

The authors then tie in classifier decoding with the phase of the theta rhythm in the hippocampus. First, the classifier fidelity itself appears to have a 3 Hz rhythm. They then compute the modulation index (MI) of classifier fidelity to the hippocampal 3Hz phase. Classifier fidelity is only modulated by the hippocampal phase during the 3rd retrieval repetition. Similarly, we only see significant phase differences between target and competitor in the last repetition. This would seem to support the hypothesis that repetition leads to greater separation and better memory. However, it would be then helpful to know how the rhythm of classifier fidelity (the underlying 3Hz rhythm of classifier performance) changes across repeats. Is this fixed, or does this also exhibit changes with repetition? Is this also the case in the NC condition? For the NC condition, how do the phases modulation compare with the phases of target and competitors in the CC condition?

We agree that changes in classifier-to-phase locking over time might be ‘contaminated’ by frequency changes. We thus followed the reviewer’s suggestion to analyse the dominant frequency in the fidelity timecourses in both NC and CC across repetitions. Both conditions show peaks in the theta range, though the NC condition has a slightly faster frequency profile than the CC: 4-5Hz (NC) compared with 3 Hz (CC). Reassuringly, there is no obvious change in the peak frequency across repetitions in either condition. We now show these spectra of the fidelity values in Figure 3 – supplement 1. Note that there is a potentially interesting explanation for the faster rhythm in the NC than the CC condition, such that the increased memory load in the CC (with participants keeping two memories in mind) being adapted to by a slow-down of the theta frequency as previously reported in the working memory literature (Wolinski et al., 2018). We briefly allude to this possibility on p.15 l. 4-11 of the revised result section and p.28 l.23 – p.29 l.4 of the discussion. See also Essential Revisions #3.

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Author details

1. Casper Kerrén

   1. Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
   2. Research Group Adaptive Memory and Decision Making, Max Planck Institute for Human Development, Berlin, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   casper.kerren@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4870-6072

2. Sander van Bree

   Centre for Cognitive Neuroimaging, School of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Benjamin J Griffiths

   Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8600-4480

4. Maria Wimber

   1. Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
   2. Centre for Cognitive Neuroimaging, School of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Validation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   Maria.Wimber@glasgow.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1917-353X

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