Episodic events are flexibly encoded in both integrated and separated neural representations

Domain: neuroscience
Published: 2026-01-19
Editorial outcome: major_revision

Abstract
Remembering everyday events involves noticing what different experiences share and preserving the details that set them apart, yet the neural processes supporting this balance remain unclear. Here, we record EEG while participants view naturalistic movie scenes that introduce episodic events with overlapping elements. Using time-resolved representational similarity analysis, we find that these events evoke both similarities and dissimilarities in neural patterns as new information unfolds. Similarities predict successful inference of information across separate episodes, consistent with integrative encoding. Dissimilarities, by contrast, predict accurate memory for individual events, indicating the formation of distinct event-specific traces. Together, these findings indicate that the brain encodes both integrated and separated neural representations to flexibly support different mnemonic goals and to balance relational inference with detailed recollection.

Introduction
Episodic memory enables the adaptive retrieval of commonalities and unique details across overlapping life events. For example, encountering a woman in the park with your colleague’s daughter may lead to the formation of an integrated memory representation connecting the woman and your colleague. At the same time, you may retain a distinct representation of this specific encounter to later discuss with your colleague the circumstances of meeting her daughter. This study investigates how the brain simultaneously supports both the integration across related events and the preservation of event-specific details.

Previous research has examined how the brain represents memories for related events by analyzing the neural pattern similarities associated with different events. Some memory models suggest that the hippocampus encodes related events into distinct memory representations to minimize interference1–3. However, more recent evidence indicates that the hippocampus also supports the integration of related events4,5, facilitating the inference of indirect associations between elements encountered in separate episodes6,7. To reconcile these apparently conflicting findings, it has been proposed that hippocampal function varies along its axis, with the anterior hippocampus supporting the formation of integrated memory representations and the posterior hippocampus forming distinct, event-specific memory representations8,9.

Despite evidence for the formation of both integrated and separated memory representations, it remains unclear whether these representations can coexist for the same event or if integration compromises event-specific detail. Some findings suggest that memory integration comes at the cost of diminished episodic detail. In particular, Carpenter and colleagues10,11 demonstrated that integrated representations are associated with reduced memory for episodic details, indicating a trade-off between memory integration and event-specific detail. This raises the possibility that the brain cannot simultaneously keep both types of representations. Nevertheless, this trade-off has not been consistently replicated, and some studies even report a positive association between memory integration and episodic detail memory12–14, implying that integrated and separated representations may coexist8,15.

It is conceivable that the brain maintains both types of representations to flexibly support distinct memory functions9. Integrated representations capture generalized information, facilitate inferences across events, and allow the seamless retrieval of overlapping memories. In contrast, separated representations preserve event-specific features, minimize interference, and enable the accurate retrieval of unique event details. Previous fMRI studies have shown that these two types of representations emerge in distinct hippocampal subregions9. However, it remains unclear how these representations are formed during real-time encoding, particularly when new episodic events overlap with prior experiences. Specifically, little is known about how the brain simultaneously integrates novel experiences into existing memory networks while maintaining the uniqueness of individual events. Do integration and separation processes interact dynamically during encoding, and how does the brain determine which aspects of an experience should be merged and which should remain distinct?

To address this gap, we employed time-resolved representational similarity analysis (RSA) of electroencephalographic (EEG) recordings to track the dynamic formation of neural patterns during real-time encoding of related events. This approach allowed us to examine the emergence of integrated and separated representations as they develop in real-time and to investigate how they support different memory functions. Furthermore, by examining how shared and novel information across related events influences the formation of these neural representations, we provide insight into the mechanisms that govern the balance between memory integration and separation during episodic encoding.

Neural activity, particularly in the theta (4 to 7 Hz) and alpha-beta (8 to 20 Hz) frequency bands, has been consistently implicated in the processes underlying memory formation and retrieval16–18. Empirical evidence demonstrates that increases in theta power often accompany successful memory performance, suggesting that these oscillations facilitate the binding of disparate elements of an experience into a coherent memory representation16,19,20. Recent work employing RSA has further indicated that theta-band activity supports the integration of overlapping memories, allowing related experiences to be linked19,21. In contrast, alpha-beta oscillations exhibit an inverse relationship with memory encoding and retrieval: decreases in alpha-beta power have been associated with improved memory performance18,22, potentially reflecting the engagement of cortical networks necessary for active information processing and representation. Conversely, increases in alpha-beta power have been linked to inhibitory control mechanisms23, suggesting that alpha-beta may play a role in suppressing competing or overlapping memory traces during processes that require memory separation.

We employed a naturalistic paradigm, adapted from the classic associative inference memory paradigm5,24,25, in which participants encode videos that simulate real-life events, allowing us to capture the complexity of memory formation in an ecologically valid setting. Participants encoded movies featuring real-world interactions between Sim characters (https://www.ea.com/games/the-sims/the-sims-4). First, they encoded AB movies, in which a Sim A interacted with a Sim B. Later, they encoded BC movies, where a new Sim C interacted with the previously seen Sim B. A control condition featured interactions between two novel Sims (XY movies). During retrieval, participants completed memory tests for both direct (i.e., AB, BC, XY) and indirect (i.e., AC) associations. These tests were interleaved, with the constraint that AC associations were tested before their corresponding AB and BC associations. Each association memory test was followed by a source memory task, where participants indicated if the two characters had directly interacted. Participants also rated their confidence (see Fig. 1A). High AC performance was considered an indicator of the formation of integrated representations across events5,24,25, while high source memory performance reflected preserved event-specific representations (see also 10,26). An additional surprise memory test for episodic details (context and character clothing) showed no systematic effects of AC retrieval and is reported in the supplementary material (see Supplementary Note 1).

EEG data were recorded continuously throughout the experiment. We applied RSA to the EEG signals during the encoding of AB, BC and XY movies, allowing us to track the formation of both integrated and separated memory representations (see Fig. 2). Memory integration was expected to manifest as greater neural similarity between AB and BC movies, whereas memory separation was anticipated to be reflected in increased neural dissimilarity between AB and BC movies relative to control conditions. In addition to RSA, we conducted a univariate time-frequency analysis to examine the relationship between oscillatory activity and observed neural similarities and dissimilarities. We predicted that neural similarities would be associated with increased theta power and decreased alpha/beta power, whereas neural dissimilarities would correspond to increased alpha-beta activity.

Furthermore, we hypothesized that neural similarities would predict AC retrieval, while neural dissimilarities would predict source memory performance. Specifically, if integrated and separated representations coexist, successful AC retrieval should be accompanied by better source memory, indicating that both types of representations, supporting different memory functions, were encoded for the same events. Conversely, if integration disrupts event-specific details, successful AC retrieval should impair source memory, and the neural patterns predictive of AC retrieval would associate with poorer source memory performance.

Results
Associative memory tests
Memory performance for direct and indirect associations is summarized in Fig. 3A. We contrasted memory performance across the four association types (AC vs. AB vs. BC vs. XY) with linear mixed-effects models. This analysis showed significant main effects of association type on accuracy (F(3,3420)=48.068, p<0.001, η2 p=0.04, 95% CI=[0.03, 1.00]), response time (F(3,2587)=71.314, p<0.001, η2 p=0.08, 95% CI=[0.06, 1.00]) and confidence (F(3,2584)=11.921, p<0.001, η2 p=0.01, 95% CI=[0.01, 1.00]). Post-hoc Tukey-corrected comparisons showed that retrieval of AC associations was associated with significantly lower accuracy, slower response times, and reduced confidence compared with all the direct associations, except that the lower confidence of AC compared to XY did not reach statistical significance (accuracy—AB: t(3423)=-11.29, p<0.001, D=-0.212, 95% CI=[-0.318, -0.105]; BC: t(3423)=-9.07, p<0.001, D=-0.170, 95% CI=[-0.256, -0.085]; XY: t(3423)=-5.92, p<0.001, D=-0.111, 95% CI=[-0.167, -0.055]; response time—AB: t(2590)=13.75, p<0.001, D=0.456, 95% CI=[0.226, 0.685]; BC: t(2590)=9.12, p<0.001, D=0.305, 95% CI=[0.152, 0.459]; XY: t(2590)=11.77, p<0.001, D=0.401, 95% CI=[0.199, 0.603]; confidence—AB: t(2590)=-5.47, p<0.001, D=-0.099, 95% CI=[-0.149, -0.049]; BC: t(2590)=-4.66, p<0.001, D=-0.085, 95% CI=[-0.128, -0.042]; XY: t(2590)=-2.43, p=0.072, D=-0.045, 95% CI=[-0.094, 0.004]).

Furthermore, XY associations were retrieved less accurately than both AB (t(3423)=5.37, p<0.001, D=0.101, 95% CI=[0.041, 0.161]) and BC (t(3423)=3.15, p=0.009, D=0.059, 95% CI=[0.015, 0.103]) associations, while no statistical difference was found between the AB and BC retrieval accuracy (t(3423)=2.22, p=0.118, D=0.042, 95% CI=[-0.011, 0.095]). In terms of response times, BC was retrieved slower than both AB (t(2590)=-4.85, p<0.001, D=-0.150, 95% CI=[-0.173, -0.127]) and XY (t(2590)=-2.98, p=0.015, D=-0.096, 95% CI=[-0.174, -0.018]), likely reflecting proactive interference due to the shared Sim B (see references 27,28 for a similar effect). No significant differences were observed between AB and XY associations (t(2591)=-1.73, p=0.310, D=-0.054, 95% CI=[-0.160, 0.052]). Finally, AB associations were retrieved more confidently than XY associations (t(2591)=3.11, p=0.010, D=0.054, 95% CI=[0.014, 0.094]), while the confidence of BC was not significantly different from either AB (t(2590)=0.19, p=0.848, D=0.014, 95% CI=[-0.127, 0.155]) or XY retrieval (t(2590)=1.64, p=0.101, D=0.040, 95% CI=[-0.008, 0.088]).

Source memory test
To evaluate how source memory performance varied as a function of retrieval accuracy across the different associations, we used linear mixed-effects models with Association Type (AC vs. AB vs. BC vs. XY) and Retrieval Accuracy (Correct vs. Incorrect) as factors, see Fig. 3B. The results show a significant main effect of Association Type for accuracy (F(3,36)=5.329, p=0.004, η2 p=0.31, 95% CI=[0.08, 1.00]) but not for confidence (F(3,126)=1.947, p=0.125, η2 p=0.04, 95% CI=[0.00, 1.00]). Specifically, source memory for XY was worse compared with both AC (t(33)=-3.36, p=0.010, D=-0.083, 95% CI=[-0.146, -0.020]) and AB associations (t(30)=-3.23, p=0.015, D=-0.108, 95% CI=[-0.177, -0.039]). All the other contrasts were non-significant (For accuracy: AC-AB: t(29)=-0.85, p=0.832, D=-0.025, 95% CI=[-0.258, 0.208]; AC-BC: t(31)=0.23, p=0.996, D=0.006, 95% CI=[-1.995, 2.007]; AB-BC: t(23)=1.25, p=0.605, D=0.031, 95% CI=[-0.087, 0.149]; BC-XY: t(32)=2.44, p=0.091, D=0.077, 95% CI=[-0.014, 0.168]. For confidence: AC-AB: t(101)=1.27, p=0.582, D=0.045, 95% CI=[-0.115, 0.205]; AC-BC: t(83)=-0.83, p=0.842, D=-0.030, 95% CI=[-0.321, 0.261]; AC-XY: t(78)=0.76, p=0.873, D=0.026, 95% CI=[-0.289, 0.341]; AB-BC: t(369)=-2.22, p=0.120, D=-0.075, 95% CI=[-0.169, 0.020]; AB-XY: t(313)=-0.57, p=0.942, D=-0.019, 95% CI=[-0.519, 0.482]; BC-XY: t(291)=1.72, p=0.317, D=0.056, 95% CI=[-0.054, 0.166]). The main effect of Retrieval Accuracy was also significant for both accuracy (F(1,26)=293.667, p<0.001, η2 p=0.92, 95% CI=[0.86, 1.00]) and confidence (F(1, 2656)=282.903, p<0.001, η2 p=0.10, 95% CI=[0.08, 1.00]), showing that when the associations were correctly retrieved, source memory performance was higher and also associated with higher confidence ratings.

Additionally, a significant interaction between Association Type and Retrieval Accuracy was observed for both source memory accuracy (F(3,34)=11.007, p<0.001, η2 p=0.49, 95% CI=[0.26, 1.00]) and confidence (F(3,2523)=2.802, p=0.039, η2 p=3.32e-3, 95% CI=[0.00, 1.00]). Post-hoc tests confirmed that higher source memory accuracy and confidence were consistently associated with correct association retrieval (AC: t(33)=9.50, p<0.001, D=0.372, 95% CI=[0.295, 0.449]; AB: t(30)=8.16, p<0.001, D=0.486, 95% CI=[0.369, 0.603]; BC: t(31)=10.39, p<0.001, D=0.500, 95% CI=[0.405, 0.595]; XY: t(32)=15.76, p<0.001, D=0.616, 95% CI=[0.539, 0.693]), suggesting that the observed interactions were primarily driven by the effect size differences across Association Types. Monte Carlo sampling further revealed that the differences in source memory accuracy between correct and incorrect retrieved associations were largest for XY associations (Monte Carlo ps<0.001), whereas the other associations showed comparable levels of this difference (Monte Carlo ps>0.128). On the other hand, the effect size of retrieval accuracy on source memory confidence was smallest for BC associations (Monte Carlo ps<0.050), while the effect size showed no significant differences across the other association types (Monte Carlo ps>0.346).

In summary, the behavioral results show that source memory performance is positively associated with AC memory performance, supporting the idea that related events may be simultaneously encoded in both integrated and separated memory representations. These different memory representations may facilitate distinct memory functions and support different mnemonic task demands.

Feature selection reveal time-frequency signatures of AB movie content
The EEG data collected during the encoding of each AB movie were transformed into a time-frequency representation (TFR) for use in the neural representational similarity analysis (RSA). Before conducting the RSA, we identified representative time-frequency features that reliably captured the contents of the AB movie. Those features were expected to remain stable across repetitions of the same AB movie and distinct across different AB movies. To achieve this, we applied a wavelet coherence-based approach, which is highly sensitive to subtle phase shifts in neural signals29, such as those expected across the five repetitions of each movie30. Importantly, wavelet coherence can be computed across all channels, timepoints, and frequency bands, preserving the multidimensional structure of the EEG data. This approach ensured that the features selected for the RSA accurately reflected the dynamic neural representations of each movie while accounting for temporal variability across repetitions. Specifically, we computed the coherence across the five repetitions of each AB movie (within-AB coherence) and contrasted it with the coherence between the different AB movies (between-AB coherence; see Fig. 2B).

Time-frequency features exhibiting significantly higher within-AB coherence than between-AB coherence were considered sensitive to the context of AB and selected for the RSA. Figure 4 presents the results of this feature selection, averaged across all AB movies. A cluster-based permutation test revealed four significant clusters. The first cluster emerged immediately after the movie onset, between 0–0.9 sec, corresponding to the initial presentation of Sim A (t-cluster = 1.229e+4, p=0.003). A second cluster appeared between 2–3 sec, capturing the representation of Sim A within its context (t-cluster = 3.344e+4, p<0.001). The third cluster, occurring between 6-7.1 sec, reflected the distinct neural representation of Sim B (t-cluster = 1.821e+4, p=0.001). Finally, the fourth cluster, observed between 8–9.2 sec, corresponded to when both Sim A and B appeared within a context, capturing the full event representation (t-cluster = 3.251e+4, p<0.001).

These AB movie-specific features were then used as a template to extract the power values from the TFR of AB trials, which were then used to estimate their time-resolved representational similarity with the same TFR features extracted from the XY and BC trials at each time point. Our analysis focused on the similarity between the time window corresponding to the Sim A and B interaction (i.e., the segment of ‘Sim A and B Context’) and each timepoint of the XY and BC movies encoding. This segment was chosen as it encapsulates the complete event structure, including both Sims, their actions, and the context (See Fig. 2A). Additional analyses investigating the representational similarity between the other AB movie segments and XY and BC movies are reported in Supplementary Note 7.

Moreover, to demonstrate that the representational similarity approach employed here successfully captures similarities driven by perceptual input, we compared the neural similarity across repetitions of the same AB movie against that of different AB movies. The results showed clear neural representational similarities driven by perceptual input, providing a prerequisite for investigating memory-related neural similarities (see Supplementary Note 3).

The neural representations of AB and BC movies share both similarities and dissimilarities
To investigate how AB and BC movies are represented at the neural level, we conducted a representational similarity analysis (RSA). First, we extracted time-frequency features sensitive to the content of each AB movie, averaging across the five repetitions to ensure robustness. To account for potential time shifts across repetitions, we used the temporal median of these features31. Next, for each participant and AB movie, we correlated these extracted features with the TFR of the corresponding BC movie at each time point during BC encoding, resulting in a time-resolved representational similarity matrix. This matrix captures the systematic similarities and dissimilarities between the neural representations of AB and the corresponding BC movie. The BC movies began with the segments involving only the novel Sim C, followed by the common Sim B. Thus, when participants were first exposed to the novel Sim C, they could not yet know which corresponding AB movie it was associated with. This may have introduced distinct patterns of similarities and dissimilarities in this initial viewing, which was therefore excluded from the analysis (see Supplementary Note 5 for RSA across all repetitions).

To determine whether the neural representations of AB and BC movies were systematically related, we first contrasted the representational similarity between each AB movie and its corresponding BC movie against the similarity between AB movies and all XY movies. Bayesian statistical analysis revealed systematic similarities and dissimilarities in several time windows (see Fig. 5A). Specifically, increased similarity was observed centered at 1 second (average similarity = 1.1e-2, minimal BF 10 in this time window = 3.166), during the segment of ‘Sim C’, followed by two additional peaks at 2.8 sec (average similarity = 9.0e-3, minimal BF 10 in this time window = 3.151) and 3.7 sec (average similarity = 1.2e-2, minimal BF 10 in this time window = 3.061), during the presentation of ‘Sim C in Context’. In contrast, increased dissimilarities were observed later during BC encoding, at 5.1 seconds (average similarity = -8.9e-3, minimal BF 10 in this time window = 3.164) and 7.4 sec (average similarity = -1.3e-2, minimal BF 10 in this time window = 3.185), immediately before and during the presentation of the segment of ‘Sim B’.

Next, we compared the similarities between the AB movie and its corresponding BC movie with those evoked by the AB movie and all non-corresponding BC movies. While using the XY movies is a common baseline in the literature (for a review, see ref. 8), it does not entirely rule out general, content-unspecific reinstatement. The Sim B might trigger general retrieval processes in which previously encoded events are broadly reactivated in search of the corresponding AB movie. This could influence the observed representational similarity, regardless of whether the movies share a common element. Thus, to differentiate content-specific reinstatement (i.e., reinstatement of the corresponding AB movie) from the general reinstatement (i.e., broader reinstatement of multiple AB movies) we compared the similarity of AB and corresponding BC movie against the similarity between AB and all non-corresponding BC movies. This analysis allowed us to isolate neural patterns reflecting the retrieval of a specific BC movie rather than general memory reinstatement. The results of this analysis replicated the previous findings (see Fig. 5B), with similarities emerging at 1 sec (average similarity = 7.5e-3, minimal BF 10 in this time window = 3.019), 2.8 sec (average similarity = 8.0e-3, minimal BF 10 in this time window = 3.188) and 3.7 sec (average similarity = 9.2e-3, minimal BF 10 in this time window = 3.065) after BC movie onset. Additionally, the same dissimilarity at 7.4 sec was observed (average similarity = -8.8e-3, minimal BF 10 in this time window = 3.164), confirming that our findings are not merely the result of broader retrieval processes but instead reflected content-specific neural similarities and dissimilarities.

In Supplementary Note 4, we examine the topographical distribution of the observed similarities and dissimilarities. The analysis revealed that these effects were broadly distributed across the scalp. In Supplementary Note 5, we further report how the similarities and dissimilarities evolved across the five repetitions of BC movie encoding. Across repetitions, we observed incremental shifts in both, reflecting an adaptive process in which integrated and separated representations changed dynamically over time. Finally, to test for a potential trade-off between integration and separation, we calculated correlations between trial-level similarities and dissimilarities. The results showed that similarities tended to co-occur within the same trial but did not significantly correlate with dissimilarities. Together, at the trial level, there is no statistical evidence of a trade-off between memory integration and separation (Supplementary Note 6).

In summary, the neural representations of AB and BC movies share both similarities and dissimilarities, revealing integrated and separated patterns. The similarities occur when the novel element, Sim C, is presented, suggesting that participants integrate overlapping elements from different experiences into unified representations. In contrast, dissimilarities arise when the common cue, Sim B, is presented, indicating a neural mechanism that preserves distinctions between similar yet distinct events, likely to minimize interference. These findings highlight the brain’s ability to flexibility balance integration and separation, which is fundamental for creating associative memory links between different experiences and for maintaining episodic memory specificity.

Neural similarities relate to associative inference and neural dissimilarity relates to source memory
Next, we investigated how the identified neural similarities and dissimilarities between AB and the corresponding BC movie relate to later memory performance. For each trial, we extracted the similarity between AB and corresponding BC, with the similarity between AB and all non-corresponding BC subtracted as a baseline, within the time windows identified in the previous analysis (see Fig. 5B). Bayesian linear regression was used to compare whether these neural similarities and dissimilarities show differences between trials with correct and incorrect indirect AC association or source memory. Both models controlled for repetition effects, and the source memory model also accounted for indirect AC association performance. To ensure balanced data, we excluded participants with near-ceiling indirect AC association or source memory performance (above 90%), and few incorrect responses. Thus, 30 participants were included in the model of AC association and 28 in the model of source memory.

We hypothesized that higher similarity between AB and corresponding BC movies, reflecting integration across related events, would be associated with higher AC retrieval. Conversely, greater dissimilarities, indicating efforts to separate related memory traces, would relate to better source memory. The observed results support our predictions. The similarity observed at 2.8 seconds was greater in trials with better AC memory performance (β = 0.044, BF 10 = 14.668), suggesting that integrating events facilitates the formation of indirect associations. Additionally, the similarity observed at 3.7 seconds was negatively related to source memory performance (β = -0.036, BF 10 = 5.345), indicating that integration may hinder the ability to remember specific event details. Finally, the dissimilarity observed at 7.4 seconds was associated with source memory performance (β = -0.041, BF 10 = 8.681), showing that memory separation helps to preserve the source of different events (see Fig. 5C). No other effects were statistically associated with AC or source memory performance (see Supplementary Note 8 for more details).

Electrophysiological markers associated with the encoding of related events
A univariate analysis investigated the time-frequency patterns associated with encoding new events that share information with previously encoded experiences. A cluster-based permutation test32 was used to contrast BC encoding against XY encoding, within each BC movie segment, and using the same frequency range (3-30 Hz) as in the previous RSA. To be consistent with the previous analysis, the initial encoding repetition of the BC and XY movies was excluded.

Our results show decreased alpha-beta power in the BC compared with the XY encoding in three different time windows: 1) between 0.0 and 1.5 sec (cluster-t = -2.934e+4, p<.001), during the segment of ‘Sim C’; 2) between 2.8 and 4.5 sec (cluster-t = -3.202e+4, p=.009), during the segment of ‘Sim C in Context’, and between 8.4 and 9.0 sec (cluster-t = -2.114e+4, p=.043), during the segment of the ‘Sim B and C in Context’. Additionally, we observed a cluster indicating higher theta and alpha-beta power during the encoding of BC compared with XY between 6.4 and 8 sec (cluster-t = 2.505e+4, p=.014), during the segment of ‘Sim B’ (see Fig. 6).

Interestingly, the alpha-beta power decreases roughly correspond to the time windows where neural pattern similarities were found, while the increased theta and alpha/beta were found in the time windows associated with neural pattern dissimilarities. To examine if these time-frequency representations are linked to the formation of integrated and separated memory representations, we employed Bayesian linear regression analysis to investigate if these power changes predicted the neural (dis-)similarities observed in the corresponding time windows. The analysis showed that the alpha-beta power decrease observed between 0.0 and 1.5 sec predicted the similarity centered at 1.0 sec (β = -0.202, BF 10 = 3.754e+29) during the segment of ‘Sim C’. Furthermore, the alpha-beta power decrease found between 2.8 sec to 4.5 sec predicted the similarity centered at 2.8 seconds (β = -0.045, BF 10 = 29.831) and ~3.7 sec (β = -0.128, BF 10 = 5.870e+11) during the segment of ‘Sim C in Context’. Additionally, the theta and alpha-beta power increase observed between 6.4 and 8 sec predicted the neural pattern dissimilarity centered at 7.4 sec (β = -0.107, BF 10 = 1.782e+8) during the segment of ‘Sim B’.

Previous studies have consistently reported that decreases in alpha-beta power are associated with successful memory encoding and retrieval17,18,33,34. Here, we provide evidence that alpha-beta power decreases are also related to the formation of integrated representations. Additionally, elevated alpha-beta power is typically linked to inhibitory control mechanisms23. Our findings suggest that increased alpha/beta power may be involved in establishing separated memory representations.

Methods
Participants
Following previous studies9,47, we aimed for a sample size of 35 participants and finally recruited 41 participants. Data from one participant was excluded for not having at least one incorrect AC association trial. Additionally, the data from another four participants were excluded due to poor EEG data quality and/or experimental programming errors. Hence, the final sample consisted of 36 participants (27 female, 9 male, M ± SD = 24.3 ± 2.94).

Participants had normal or corrected-to-normal vision, were right-handed, fluent in English, and had no psychiatric or neurological disorders. Participants provided informed consent and were given a voucher that could be used in various shops for their time spent in the lab (on average 3 h per participant including EEG preparations). The data collection was anonymous and did not involve any potentially identifying demographic information. The data collection was conducted in accordance with the Swedish Act concerning the Ethical Review of Research involving Humans (2003:460) and the Code of Ethics of the World Medical Association (Declaration of Helsinki). As established by Swedish authorities and specified in the Swedish Act concerning the Ethical Review of Research involving Humans (2003:460), the present study does not require specific ethical review by the Swedish Ethical Review Authority due to the following reasons: (1) it does not deal with sensitive personal data, (2) it does not use methods that involve a physical intervention, (3) it does not use methods that pose a risk of mental or physical harm, (4) it does not study biological material taken from a living or dead human that can be traced back to that person. The Ethics Committee at the Department of Psychology, Lund University, has corroborated that the present research protocol follows the research ethics guidelines established by Swedish authorities.

Stimuli material
AB and BC movies were generated using the life-simulation game The Sims 4 by Electronic Arts (www.thesims4.com). In each movie, two Sims were presented. The movies were 14 sec long and started with a fixation cross presented for 1 sec, followed by the presentation of the Sim A/C for 2 sec and the Sim A/C in context, acting, for another 3 sec. After another 1-second fixation cross, the Sim B was presented for 2 sec followed by the Sim A/C interacting with Sim B in the same context for 5 sec (see Fig. 2A). Each movie has a unique context, so the AC association across AB and BC movies can only be made via the overlapping element, i.e., Sim B.

In total, 48 AB movies and 48 BC movies were created. Each AB movie has its own corresponding BC movie. During the experiment, only 24 AB movies were presented during the AB encoding, while all the BC movies were displayed during the BC encoding. As a result, participants could recognize the Sim B in 24 BC movies, while in the other 24 movies, the Sims were new to the participants. These all-new movies are the XY movies, which were used to control for memory processes involved in encoding direct associations without any previous overlapping experience. To control for potential perceptual or narrative similarities between specific AB-BC pairs, the selection and presentation of AB movies were counterbalanced across participants. This ensured that all the movies presented in the second encoding round were equally likely to appear as BC or XY, minimizing confounds due to movie-specific similarities.

In addition to the experimental movies, three videos of a man jogging at different places, also generated in The Sims 4 environment, served as attention-check videos to keep participants concentrated. All participants responded to all attention-check videos.

To evaluate memory performance, 120 pictures of Sims faces (24 of each Sim A, B, C, X, and Y) were used as cues, targets, and distractors in the associative and source memory test. Additionally, to measure detail memory for context (see Supplementary Note 1), 96 pictures of contexts (48 for each AB and BC movie) were taken from the videos (without the Sim character present). One altered copy of each context was made by changing the color and/or texture details of the original context, which resulted in 96 pictures of contexts with altered details. To test detail memory for clothing, 96 full-body pictures of Sims (48 of each Sim A and Sim C) were used. The distractor pictures for the clothing test were generated by changing the color of the original clothing. For each Sim, three altered clothing pictures were generated, resulting in 288 pictures with altered details.

Procedure
The stimuli were presented with PsychoPy48 (v2022.1.0). The experiment consisted of an encoding and a test phase. In the encoding phase, participants first encoded 24 AB movies, each presented five times. When all 24 AB movies had been presented once, the next repetition began. After, participants encoded 24 BC movies intermingled with 24 XY movies, each presented also five times. The second repetition of these movies began when all of them had been presented one time. After the presentation of every 15 movies, the participants were encouraged to take a break. The attention-check movie was played three times during the encoding phase at random intervals. The participants were asked to press ENTER within three seconds each time they saw the attention-check movie. All included participants correctly responded to all attention checks within the given time.

The encoding phase ended with a short distraction task for 30 sec, where participants were asked to consecutively subtract 7 from a random 3-digit number. Then, the test phase started. The test phase began with the direct and indirect memory tests, which were intermixed, with the only constraint that the AC indirect memory test was made before the corresponding AB and BC memory tests. Both the direct and the indirect associative memory test started with a cue, corresponding to the face of the Sim A or C, displayed on screen for 1.5 sec, followed by the display of two faces presented below, the target Sim face C or A and the distractor Sim face drawn from the XY movies. Participants were asked to select which face was associated with the cue by pressing the left or the right key. Immediately after that, memory for source was tested. Participants were asked to indicate if the two Sims had been seen together in the same movie. For both association and source memory tests, participants were asked to rate their confidence on a three-point scale: 1-guessing, 2-maybe, 3-sure. After the association and source memory tests, a surprise episodic detail memory test was also implemented to detect the memory integration-related episodic detail loss (see Supplementary Note 1).

Using Bayesian t-tests, we confirmed that indirect AC association performance was equivalent whether A or C was the cue (Accuracy: difference = 0.039, BF 01 = 3.249; Response Time: difference = 0.027, BF 01 = 4.099 and Confidence: difference = 0.002, BF 01 = 4.113). This was performed with the Bayes Factor package (0.9.12) in R (4.1.2), and BF values were computed to quantify evidence for the null relative to the alternative hypothesis with gamma set to 0.707 by default. Following common conventions, BF values greater than 3 were interpreted as providing moderate support for the null hypothesis49,50.

Behavioral data analysis and statistics
The behavioral data was analyzed in R (4.1.2) using linear mixed models. The packages of lme4 (1.1-34) and emmeans (1.8.7) were used to fit the models and perform statistical tests, and the effect size (0.8.9) was used to estimate the effect sizes. The first level of each model was the trial level, which was clustered in the second level, i.e., the participant level. To rule out potential confounds of random effect51 and simultaneously prevent model overfitting52, the fitting of each model started with participants as the only random intercept. Other factors were thereafter added as random slopes in a step-by-step fashion. If an added random slope improved the model fitting significantly (i.e., p<.05 in chi-square test for model fitting comparison, decreased Akaike Information Criterion and Bayesian Information Criterion), the random slope was kept. The final equations for all models, showing which random factors were considered, can be consulted in the Supplementary Note 2. For each model, the homogeneity of variance of the residuals was assessed by using Levene’s test. If the test indicated heteroskedasticity, we re-ran the model with a restricted variance structure28,53. For inferential statistics, we used two-tailed tests with the significance criterion of p<.05. Correct responses outside a 10-sec time window or with a confidence rating of ‘guessing’ were considered incorrect to prevent responses based on logical reasoning rather than memory from biasing the results. Only correct responses were considered in the response times and confidence analysis. Response times were logarithmic transformed to correct for skewness.

First, behavioral performance for indirect and direct associative memory was contrasted in terms of accuracy, response times (RTs), and confidence ratings, with Association Type (AC vs AB vs BC vs XY) as the predictor for all three models. Next, source memory accuracy and confidence were also examined using Association Type (AB vs BC vs AC vs XY) and Association Accuracy (correct vs incorrect) of the preceding memory test as predictors. To interpret the interaction term observed in the source memory models, Monte Carlo sampling was implemented within-house MATLAB scripts to estimate the effect size differences across all Association Types. For each Association Type, the effect sizes of the Association Accuracy over source memory accuracy and confidence were sampled for 10000 iterations, based on the model estimates obtained above. Then, these effect sizes were compared across Association Types by estimating the distribution of the difference between these samples. The percentage (%) of the sample difference that is larger/smaller than zero was considered an approximate of the one-tail statistical probability, which was then multiplied by two to indicate two-tail p-value, where the 0.05 criterion was applied to determine significance.

The normality of residuals was assessed by inspecting the Q-Q plots of the standardized residuals and Shapiro-Wilk’s tests. The homogeneity of variance of the model residuals was assessed for each model by visually inspecting a plot of the model residuals versus fitted values and using Levene’s test for unequal variance. Significant complex effects were followed up with post hoc tests with Tukey correction. Effect sizes, partial eta squared (η2 p) for F-tests and unstandardized difference (D) for t-tests, and 95% confidence intervals (CI) were also reported together with other statistics.

EEG data collection and preprocessing
EEG was recorded using a SynAmps RT Neuroscan 64-channel amplifier (sampling rate 1 kHz, bandwidth DC-3500 Hz, 24-bit resolution, left mastoid reference) with 62 electrodes attached to an elastic cap (active electrode EasyCap). The cap was placed according to the extended 10–20 system. Furthermore, an electrode was attached to the skin under the participant’s left eye to detect and later filter noise caused by eyeblinks.

To preprocess the data, the EEG waveform was downsampled to 500 Hz, and epochs of 14 sec, ranging from -0.5 to 13.5 sec with respect to the onset of each movie, were created. Preprocessing was implemented with FieldTrip32 accompanied by in-house scripts. The data were low-pass filtered at 200 Hz. A notch filter at 50, 100 and 150 Hz was applied to remove Alternate Current related signal. The data was transformed to linked-mastoid reference and demeaned. Vertical eye movements and blinks were estimated using the electrodes placed under the left eye and Fp1 in the cap, and horizontal eye movements were estimated using the Fp9 and Fp10 electrodes. The data were then manually checked to detect noisy channels and to remove trials dominated by artefacts other than eye blinks and horizontal eye movements. An independent component analysis was then applied to detect and remove components related to blinks and horizontal eye movements. Finally, after interpolating the removed channels, another visual check-up of the data took place for a final rejection of trials with residual artifacts. The cleaned data resulted, on average per participant, in 114 AB trials (ranging from 102 to 120), 114 BC trials (ranging from 100 to 119) and 114 XY trials (ranging from 107 to 119).

Time-Frequency decomposition of EEG data
The time-frequency decomposition was also implemented with in-house MATLAB scripts based on the Fieldtrip package32. The Morlet wavelets54,55 with 5 cycles were used to extract power spectra of frequencies of 3–30 Hz (28 frequencies in total) for each event encoding trial (AB/BC/XY) with a temporal resolution of 0.02 sec.

Feature selection for the time-resolved representational similarity analysis
Before performing the RSA, we aimed to first identify the time-frequency features that were sensitive to AB movie content. To account for potential temporal variations across repeated viewings of the movies30, we employed a wavelet coherence approach. Wavelet coherence measures the correlation between the spectral components of two time-series, which is highly sensitive to subtle phase shifts29. It can be computed at the full channels × timepoints × frequency bands level for both within- and between-movie comparisons. This preserves the multidimensional structure of the EEG data, making it well-suited for a fine-grained feature stability assessment. By evaluating the coherence between all repetitions of the same AB movie and contrasting it against the coherence between different AB movies, we identified the time-frequency features sensitive to AB movie content. These features were later used to assess the representational similarities in the neural data between AB and BC/XY movies.

Wavelet coherence was estimated at the level of individual channels × timepoints × frequency bands. For each participant, we calculated the pairwise wavelet coherence across all five repetitions of each AB movie, resulting in ten unique within-movie repetition pairs per AB movie. As a control, we computed the wavelet coherence between each AB movie and all other AB movies across all five repetitions, resulting in 230 pairs per movie. For each participant, the within-AB movie coherence was obtained by averaging the wavelet coherence across 240 different pairs (24 different AB movies × 10 repetition pairs) and the between-AB movie coherence was obtained by averaging the coherence across 5520 pairs (24 different AB movies × 23 other AB movies × 10 repetition pairs). This yielded two separate 3-dimensional coherence matrices (channel × timepoint × frequency) per participant: one for within-AB movie coherence and one for between-AB movie coherence.

A cluster-based permutation test was used, at group level, to contrast the within-AB movie coherence against the between-AB movie coherence across all participants56. The test was conducted separately for four distinct time windows, corresponding to the different segments of the AB movie, i.e., ‘Sim A’, ‘Sim A in Context’, ‘Sim B’, and ‘Sim A and B in Context’. For all comparisons, t values of significant clusters (two-tail p value ≤ .05), as well as their corresponding p values, are reported. Significant clusters indicated the time-frequency features sensitive to AB movie content, which were then used as templates to extract features (i.e., power at each channel-timepoint-frequency) for the representational similarity analysis.

Time-resolved representational similarity analysis and statistics
The representational similarity between the neural patterns associated with AB encoding and the BC/XY encoding was estimated as a measure of the approximation and differentiation of mental representations57. Accordingly, similarities in the neural representation are indicators of memory integration linking the events, while dissimilarities reveal memory separation between the events9.

The similarities between AB and BC/XY movies were estimated for each participant and movie. TFR was transformed into decibel (dB) values relative to the average power across the epoch. This approach reduces the disproportionate influence of low-frequency power and emphasizes relative power, rather than absolute power, fluctuations in relation to specific movie contents. For each AB movie, a 3-dimensional representative channel-timepoint-frequency representation was obtained by averaging across the five repetitions. Representative features for each AB, BC and XY movie segment were extracted using the template obtained in the wavelet coherence analysis. Considering the time shifts across repetitions30, the medians along the time dimension of the extracted features were used as a stable estimation. This procedure resulted in a 2-dimensional (channel-frequency) feature map for each AB movie segment, which was then reshaped into a feature vector by concatenating the values of each channel head to tail, representing the power distribution in the ‘channel-frequency’ space of a segment. In total, this feature extraction procedure resulted, for each participant, in 96 feature vectors of AB movies (24 AB movies × 4 different AB movie segments) to be used in the similarity analysis contrasting the neural representations of AB and BC/XY movies. Feature vectors of BC and XY movies were extracted with the same template at each timepoint of the whole epoch to obtain the time-resolved similarities.

Pearson correlation coefficients were used to quantify the neural similarity and were Fisher z-transformed for statistical analysis. The correlations between AB movies and BC/XY movies were performed separately for each of the five BC/XY repetitions, which enabled us to account for the progression of the similarities and dissimilarities along repetitions. The feature vectors corresponding to the AB movie contents were correlated with each timepoint of the BC/XY movie, resulting in a time-resolved representational similarity time series. For each BC/XY timepoint, the 2-dimensional matrix (channel-frequency) of power was reshaped in the same way as the extracted AB feature map, resulting in a vector representing the power distribution in the ‘channel-frequency’ space of each timepoint. This vector was correlated with the feature vector of each AB movie segment, resulting in 96 different correlation values (24 AB movies × 4 different segments), indicating the representational similarity between each segment of AB movie and each timepoint of each BC/XY movie. The time-resolved topographical distribution of the similarity effects was assessed by calculating the Pearson correlation of each channel and its neighbors at each timepoint (see references 27,58 for a similar approach). The results of identified time windows are summarized in Supplementary Note 4. In the main paper, we focus on the similarity between the segment when Sim A and B interact in a context, as this segment contains all the relevant information and encompasses the AB movie event. The representational similarity analysis involving the other AB movie segments is presented in Supplementary Note 7.

The statistical evaluation of the RSA was performed with Bayesian statistics for its improved sensitivity, greater robustness to noise, and more effective control of multiple comparisons59–61. To account for false discoveries, we applied an informative Normal-Inverse-Gamma distribution prior (alpha = 15, beta = 15, mu = 0, v=30)27,59,62. The posterior was obtained by updating the prior with the data of all participants consecutively and tested against 0. Following conventional interpretation, a Bayesian Factor (BF 10) exceeding 3 was taken as statistical criterion, indicating moderate support for the alternative hypothesis against null hypothesis49,50. Only similarities and dissimilarities last over 0.1 s were reported to avoid spurious results.

Note that when participants were first exposed to Sim C in the BC movies at repetition zero, we did not expect any memory reactivation of the AB movie. Thus, we excluded the repetition zero of the BC/XY trials from both the representational similarity analysis and the univariate analysis.

Relationship between behavioral performance and representational similarities
To further reveal the behavioral consequences of representational similarities and dissimilarities between AB and BC, we investigated the relationship between the (dis-)similarities and memory performance. A Bayesian linear regression with a Multivariate-Normal-Inverse-Gamma distribution prior (alpha = 15, beta = 15, mu = 0, lambda = 30*I, I is unit matrix) and the data at trial level was used in the present analysis. We estimated standardized regression coefficients to control for individual differences, using a BF 10 > 3 as the statistical criterion49,50. For each trial, the (dis-)similarities within each time window of significance were extracted and averaged as dependent variables. To estimate the (dis-)similarity difference between trials with correct and incorrect AC association, the accuracy of AC association was used as the independent variable, with the repetition order included as the controlled variable. Similarly, to estimate the (dis-)similarity difference between trials with correct and incorrect source memory for AC association, the accuracy of source memory was used as the independent variable, with the repetition order and the AC association accuracy included as the controlled variables. To ensure that the results were not biased by unbalanced data, the present analyses included only the participants with at least 10% incorrect trials for the independent variable (i.e., accuracy for AC association or source memory ≤ 0.9). Hence, the analysis for AC association included 30 participants, while the analysis for the source memory included 28 participants. A similar analysis for the clothing memory was also performed; see Supplementary Note 8 for details.

EEG univariate analysis and statistics
A univariate analysis was used to investigate the neural correlates associated with the encoding of new events that share information with past events. To assess absolute power differences between BC and XY encoding, we averaged the spectra across all BC and XY trials, respectively, and computed their difference through direct subtraction. This difference was then scaled by the power of BC for each participant to control for individual variability and ensure comparability across participants, i.e., (BC-XY)/BC, and the statistical tests were performed by contrasting this index against zero using the cluster-based permutation test56. The comparison was performed separately for each segment of the movie (i.e., ‘Sim C’ vs. ‘Sim Y’, ‘Sim C in Context’ vs. ‘Sim Y in Context’, ‘Sim B’ vs. ‘Sim X’, and ‘Sim C and B in Context’ vs. ‘Sim Y and X in Context’) using a cluster-based permutation analysis implemented in fieldtrip32. For all comparisons, t values of significant clusters (two-tailed p value ≤ .05), as well as their corresponding p values, are reported.

Next, we evaluate if the potential differences between BC and XY encoding were predictive of the similarities and dissimilarities. This was performed using a Bayesian linear regression approach with a Multivariate-Normal-Inverse-Gamma distribution prior (alpha = 15, beta = 15, mu = 0, lambda = 30*I, I is unit matrix) and the data at trial level. The power/similarity values were extracted from the trials according to the cluster / time-window of significance. Standardized regression coefficients were estimated to reduce the influence of individual differences, and the coefficient with BF 10 > 3 was considered as moderate evidence for alternative hypothesis against null hypothesis49,50.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study examines how the brain flexibly encodes episodic events to support different memory functions. Using EEG, the authors recorded neural activity while participants watched short movie clips depicting overlapping social interactions. Memory was later tested for direct, indirect, and source associations. Behaviorally, successful inference of indirect associations was linked to more accurate source memory. Time-resolved representational similarity analysis revealed that neural pattern similarity during encoding supported memory integration and indirect inference, while dissimilarity supported accurate source memory by preserving distinct event representations. These findings suggest that the brain encodes episodes in both integrated and segregated formats to meet different memory demands.\nI found the study to be both interesting and well-executed. It combines several state-of-the-art methodological approaches in a compelling way and offers a valuable opportunity to examine complex memory representational dynamics that are rarely explored in such a temporally rich and ecologically valid context. Overall, I believe the manuscript has the potential to make a meaningful contribution to the field. I offer the following suggestions to enhance methodological clarity and interpretability.\nMajors:\n1. More detail is needed regarding the computation of coherence. It is important to clarify whether coherence was computed at the individual electrode × time × frequency level and then averaged across channels, or whether it was aggregated in a different way. Additionally, it would be helpful to know at what point the permutation analysis was implemented relative to these steps.\n2. The normalization approach used in the time–frequency representations (TFRs) warrants clarification. The RSA was based on power changes relative to the average power across the entire epoch, whereas the univariate power analysis in Figure 6 appears to use a baseline correction method tied to the time window immediately preceding each target segment. This likely explains the presence of NaN values during the baseline period. If different normalization methods were applied to the RSA and univariate power analyses, this should be explicitly justified. Otherwise, it may be advisable to re-evaluate the univariate power analysis using the same normalization strategy applied in the RSA to ensure consistency.\n3. Were the same features extracted from AB movies also used in the BC RSA? I assume so in order to make vector-based correlation feasible, but it should be explicitly stated to avoid confusion.\n4. Relatedly, the type of correlation metric used in the RSA should be reported. Was Pearson or Spearman correlation used? If Pearson, were r-values transformed into z-scores before group-level statistical analyses to ensure comparability across distributions?\n5. On a more conceptual note, the rationale for using coherence as the central neural feature should be addressed more directly. Why was coherence chosen over more standard power-based TFR features? Would the same representational patterns emerge with power instead of coherence? As this appears to be one of the first studies to use coherence in this way, it would be helpful to highlight its novelty and justify its use over other possible measures.\nMinors:\n1. It would be useful to briefly describe the random effects structure of the mixed-effects models (e.g., whether by-subject or by-item intercepts or slopes were included).\n2. There is a typo in the section describing the behavioral data analysis: “Tucky-correction” should be corrected to “Tukey correction.”", "reviewer_2": "The manuscript by Liu and colleagues describes an EEG study in which 36 participants viewed movies simulating real-life interactions. In the experiment, two sets of movies (AB and BC pairs) featuring a shared character were used to investigate the temporal dynamics of forming integrated and segregated memory representations and their relationship with subsequent memory accuracy. Behaviorally, they found that source memory performance was positively correlated with AC memory inference performance. Using time-resolved representational similarity analysis (RSA), they found that the neural representations of AB and BC movies share both similarities and dissimilarities. During the encoding of the novel character C, there were neural similarities to the AB representation, which predicted successful AC memory inference. In contrast, during the presentation of the common character B, neural dissimilarities to the AB representation were observed, and these predicted accurate source memory. Lastly, univariate analyses revealed that changes in alpha-beta power were associated with these neural (dis)similarity patterns. In summary, the authors provide compelling evidence that the brain dynamically and flexibly engages in both memory integration and separation during the encoding of overlapping events. These processes have distinct temporal and electrophysiological signatures and support different, complementary memory functions.\nOverall, I find this study is well-motivated and clearly written. The analyses are appropriately conducted and convincing. The findings expand our understanding of the neural mechanisms for forming and maintaining both integrated and segregated memory representations. In particular, temporally separating the neural processing of integration and separation is novel (to my knowledge) and has broad implications for our understanding of the neural mechanisms underlying overlapping memories. I only have a few comments for the authors, which are mostly about the interpretation of the results:\n1. The neural similarity analysis revealed increased neural similarity between the Sim A&B representation and the Sim C (and Sim C in context) representation. The authors interpret this as evidence of memory integration. I wonder whether it could merely reflect the reactivation of the Sim A&B memory, rather than the formation of an integrated A-B-C representation.\n2. The main analyses focused on the neural similarity between the representation of Sim A&B in context and the BC trials. I am somewhat surprised that there was little to no evidence showing similarity to Sim A or Sim B individually during the Sim C segment. More importantly, there appears to be no Sim B-specific similarity during the Sim B and Sim B&C in context segments, where character B was present on screen. This makes the interpretation of neural similarity ambiguous, as the analysis appears unable to capture common visual inputs.\n3. Related to the previous comment, I wonder what the neural dissimilarity truly reflects. The authors argue that this dissimilarity is evidence of the segregation of AB and BC memories. If that is the case, why would this dissimilarity appear in the Sim B segment instead of the Sim B&C in context segment, where the full BC memory event is arguably most salient?\n4. The results show two significant similarity clusters corresponding to the Sim C and Sim C in context segments. Do they reflect the same underlying neural process? More generally, what is the functional difference between the character segment and the character-in-context segment? After the initial learning, can the character segment (e.g., Sim C) alone trigger memory integration, or is the context an essential component for forming overlapping memories?\n5. The current experimental design nicely separates the memory integration and separation processes in time and examines their relationship to behavior. To bridge the results to the existing literature, it might be worthwhile to discuss the possible mechanism if the BC pair were presented simultaneously (i.e., only a Sim B and C in context segment). Would integration and separation still both occur? If so, would they have different temporal dynamics within that single segment? Or might the two processes occur simultaneously but be supported by different neural circuits?\n6. Given that both integration and separation processes are observed, I wonder if there is any evidence of a trade-off between them. For example, could stronger integration of a BC memory into an AB memory lead to a weaker separation process in the Sim B segment, which might, in turn, affect source memory performance? Conversely, could stronger integration create a greater need for separation, thereby increasing the observed neural dissimilarity? While this is an interesting question, I would understand if the authors find it to be outside the scope of the current paper.\n7. The RSA results showed that the neural similarity and dissimilarity measurements predict AC inference and source memory performance. Are these neural markers also associated with memory for episodic details? For example, might a strongly integrated representation come at the cost of memory for details like the character's clothing? Again, I understand if the authors consider this question beyond the scope of the current paper.", "reviewer_3": "Summary\nThis study makes a timely and valuable contribution to the field of memory research by investigating how the brain encodes episodic events to support multiple mnemonic functions. Leveraging an ABC associative inference paradigm—a framework well-suited to studying memory generalization and inference—the authors employed naturalistic stimuli (simulated real-life movies) and multivariate analysis techniques to explore the flexible encoding of related experiences.\nParticipants first watched a series of short movies depicting interactions between two characters (AB pairs), followed by a second set of scenes involving one familiar and one novel character (BC pairs). This design enabled the testing of indirect (AC) associations, as well as source memory—i.e., whether participants remembered if two characters had ever appeared together. This dual focus allowed the authors to probe two distinct but complementary memory processes: integration, where overlapping events are linked into unified representations, and separation, where distinct experiences are preserved independently.\nThe use of time-resolved representational similarity analysis (RSA) on EEG data allowed the authors to track the neural dynamics underlying memory formation with high temporal precision. Importantly, the results revealed that neural pattern similarity between AB and BC episodes predicted successful inference of indirect (AC) associations—indicating integration. In contrast, neural dissimilarity predicted accurate source memory, pointing to the preservation of separate memory traces.This double dissociation is both conceptually and methodologically interesting. It suggests that the brain does not rigidly encode experiences in one way but instead adapts flexibly, balancing integration and separation depending on the demands of future retrieval. These findings align with and extend theoretical frameworks suggesting that memory systems must support both generalization (e.g., for inferential reasoning) and specificity (e.g., for accurate source recollection).\nThe use of naturalistic stimuli adds ecological validity, enhancing the relevance of the findings for real-world memory processes. Moreover, the combination of behavioral and neural data—analyzed with state-of-the-art multivariate methods—demonstrates a sophisticated experimental approach that provides insights not accessible with traditional univariate or trial-averaged analyses.\nWhile the presented findings add to a growing body of evidence with potential implications for memory disorders, I have some major and a few minor issues that would need to be addressed and clarified before publishing. I detail these concerns below.\nMajor concerns:\n1. My primary concern lies with the introduction, which needs to be substantially rewritten to better frame the study's rationale. As it stands, the introduction heavily emphasizes prior fMRI findings, particularly the role of hippocampal subregions in ABC inference paradigms. While this context is somewhat relevant, it reads more like a retrospective interpretation of the current results than a focused theoretical or empirical motivation for the hypotheses test-ed in this EEG study. Critically, the introduction fails to reference existing M/EEG research that has examined inference and generalization using similar paradigms, including studies employing representational similarity analysis (RSA). These omissions weaken the justifica-tion for using EEG and limit the reader’s understanding of how this work builds on or ex-tends prior findings especially concerning brain oscillations.\n2. Furthermore, although the authors perform both univariate and multivariate time-frequency analyses, the introduction lacks any predictions of relevant frequency bands—such as the-ta—which are known to play a key role in memory integration and retrieval. Without grounding the study in this literature, the introduction does not adequately set up the relevance of frequency-specific dynamics to the research questions. A revised introduction should clearly articulate the motivation for using EEG, specify what temporal or spectral in-sights it can offer beyond fMRI, and situate the work within the existing electrophysiologi-cal literature on memory generalization and inference.\nGenerally, the results seem incomplete and demand deeper analyses:\n3. A major concern relates to the spatial characterization of the reported neural similarity ef-fects. While I appreciate that the RSA was conducted at the whole-brain level, the results as presented lack any indication of where on the scalp these effects are most prominent. Espe-cially given the introduction’s emphasis on specific brain regions and memory-related pro-cessing, I would have expected at least a basic breakdown of whether the observed effects are stronger over frontal, parietal, or other regions.To enhance interpretability, I recommend the authors either (1) report the topographical distribution of the similarity effects or (2) re-run the RSA restricted to functionally meaningful electrode clusters (e.g., frontal, central, parietal). This would provide valuable insight into the potential cognitive and neural sources of the effects and better connect the findings to prior literature on memory integration and segregation.\n4. Closely related to the previous point, it is unclear why no source reconstruction analyses were conducted, especially given that the experiment was designed around RSA with a clear theoretical focus on specific brain regions, notably the hippocampus. While I understand that the RSA was performed at the sensor level, this approach limits anatomical interpretability—particularly in a study aiming to link EEG pattern similarity to memory mechanisms com-monly associated with deep brain structures.Given the relevance of medial temporal regions in memory integration and inference, I strongly recommend that the authors complement their current analyses with source-level RSA or at least conduct source localization of the key effects. This would significantly strengthen the anatomical specificity of the findings and align the EEG results more directly with the fMRI literature that motivated the study.\n5. Given the well-known limitations of detecting hippocampal activity with EEG—even when employing advanced source reconstruction methods such as LCMV beamformers—I believe it would be valuable for the authors to explore functional connectivity of relevant cortical regions, such as the angular gyrus, in relation to the reported effects. Incorporating connectivity analyses would enhance the interpretive depth of the findings and provide a more comprehensive picture of the neural mechanisms underlying the observed behavioral outcomes.\nMinor concerns:\n1. It would be helpful if the authors addressed potential effects of the repeated presentation (5×) of the stimuli. Since neural signals were averaged across repetitions, the role of novelty vs. familiarity—a key factor in memory encoding—should be considered. The neural dynamics of the first versus the fifth presentation may differ substantially, and this could impact both the behavioral and neural findings. A brief analysis or at least a discussion of how repetition may have influenced RSA results are needed.\n2. The behavioral data in Figure 3 appear to exhibit substantial ceiling effects, particularly in accuracy scores. The authors should clarify whether the use of linear mixed models (LMMs) is appropriate given the limited variance. Please elaborate on how this was handled in the analysis and interpretation.\n3. Response times for BC retrieval were notably slower than for AB. This difference deserves further discussion. Could this be due to fatigue, re-encoding demands, or greater cognitive load during BC trials? Including a plausible interpretation would help contextualize this finding.\n4. In the caption of Figure 2, there is a minor typo:\n“(D) Systematic similarities, indicative of memory integration should predict of AC retrieval performance.”\nThis should be corrected to:\n“(D) Systematic similarities, indicative of memory integration, should be predictive of AC retrieval performance.”\n5. Did the authors assess the perceptual or narrative similarity between the SIM movies and stories (AB, BC pairs)? If some stimuli were more similar than others, this could confound RSA results. It would strengthen the study to report whether similarity ratings (e.g., by independent raters) were obtained and whether these were controlled for or included in the analyses."}, "author_response": "RESPONSE LETTER TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study examines how the brain flexibly encodes episodic events to support different\nmemory functions. Using EEG, the authors recorded neural activity while participants\nwatched short movie clips depicting overlapping social interactions. Memory was later tested\nfor direct, indirect, and source associations. Behaviorally, successful inference of indirect\nassociations was linked to more accurate source memory. Time-resolved representational\nsimilarity analysis revealed that neural pattern similarity during encoding supported\nmemory integration and indirect inference, while dissimilarity supported accurate source\nmemory by preserving distinct event representations. These findings suggest that the brain\nencodes episodes in both integrated and segregated formats to meet different memory\ndemands.\nI found the study to be both interesting and well-executed. It combines several state-of-the-\nart methodological approaches in a compelling way and offers a valuable opportunity to\nexamine complex memory representational dynamics that are rarely explored in such a\ntemporally rich and ecologically valid context. Overall, I believe the manuscript has the\npotential to make a meaningful contribution to the field. I offer the following suggestions to\nenhance methodological clarity and interpretability.\nWe thank the reviewer for the positive and constructive remarks about our study and paper. We\ncarefully considered the thoughtful suggestions to enhance methodological clarity and\ninterpretability, and the manuscript has been revised accordingly. We are grateful for these\nvaluable insights, which have significantly strengthened the quality of the work.\nMajors:\n1. More detail is needed regarding the computation of coherence. It is important to clarify\nwhether coherence was computed at the individual electrode × time × frequency level and\nthen averaged across channels, or whether it was aggregated in a different way. Additionally,\nit would be helpful to know at what point the permutation analysis was implemented relative\nto these steps.\nWe thank the reviewer for noticing that information was missing regarding the computation of\ncoherence. The pair-wise coherence of AB movies was estimated at each channel × timepoint\n× frequency level. Specifically, for each participant, the within-AB movie coherence was\nobtained by averaging the coherence across 240 different pairs (24 AB movies × 10 repetition\npairs), while the between-AB movie coherence was obtained by averaging the coherence across\n5520 pairs (24 AB movies × 23 different AB movies × 10 repetition pairs). This provided the\ncoherence estimation for each participant. To assess the overall difference in coherence\nbetween within-AB and between-AB movie conditions across participants, we conducted a\npermutation test to identify clusters of channel × timepoint × frequency showing higher\ncoherence for the within-AB movie condition compared to the between-AB movie condition.\nThese clarifications are now added to the text (see page 29, paragraph 1 & 2).\n2. The normalization approach used in the time–frequency representations (TFRs) warrants\nclarification. The RSA was based on power changes relative to the average power across the\nentire epoch, whereas the univariate power analysis in Figure 6 appears to use a baseline\ncorrection method tied to the time window immediately preceding each target segment. This\nlikely explains the presence of NaN values during the baseline period. If different\nnormalization methods were applied to the RSA and univariate power analyses, this should\nbe explicitly justified. Otherwise, it may be advisable to re-evaluate the univariate power\nanalysis using the same normalization strategy applied in the RSA to ensure consistency.\nWe thank the reviewer for noticing the lack of clarity regarding the normalization approaches\napplied to the data. Importantly, both the RSA and univariate analyses used the same initial\nbaseline correction: a mean subtraction (demeaning) across the entire epoch prior to the time-\nfrequency decomposition. No additional pre-stimulus baseline correction was applied. The\nNaN values observed in the pre-stimulus interval of the univariate time-frequency plots result\nfrom edge effects inherent to the wavelet decomposition and not from baseline correction.\nHowever, due to the distinct analytical goals of the two analyses, we applied different scaling\napproaches, each optimized for its respective purpose: detecting relative power differences\nacross conditions in the RSA, and absolute power differences across conditions in the\nunivariate analysis.\nFor the RSA, to emphasize relative power differences rather than absolute, we applied a decibel\n(dB) transformation relative to the average power across the entire epoch. This approach is\nwidely used in EEG-RSA (e.g., Pacheco Estefan et al., 2019) and mitigates confounds\nintroduced by the 1/f distribution, which would otherwise cause low-frequency power to\ndominate similarity estimates. Both pre-stimulus and whole epoch references have been\nsuggested for such scaling (Grandchamp & Delorme, 2011; Hu et al., 2014), each with\nadvantages and disadvantages. A pre-stimulus window avoids contamination from stimulus-\nevoked activity, but it may suffer from variability across trials, potentially reducing reliability\n(Hu et al., 2014). In contrast, a whole-epoch reference provides greater stability but may\nincorporate evoked responses, potentially biasing absolute power estimates (see also\nGrandchamp & Delorme, 2011). Given our experimental design and trial structure, we selected\nthe whole-epoch average as the reference. Although this choice may include evoked activity,\nRSA is based on Pearson correlations, which demean the feature vectors and therefore\nemphasize relative rather than absolute power. Consequently, any bias in absolute power is\nunlikely to substantially impact the RSA results.\nFor the univariate analysis (Figure 6), the goal was to examine absolute power differences\nacross conditions. Applying the same dB transformation as in the RSA would have distorted\nthese estimates. Instead, we compared conditions by subtracting XY from BC and scaling by\nBC power to account for individual differences.\nImportantly, the correlation analysis (see page 17, paragraph 2) shows that the power\ndifferences observed in the univariate analysis correspond to the similarities and dissimilarities\nobserved in the RSA. This further supports our methodological choice and indicates that the\nobserved time-frequency effects reflect processes related to memory integration and separation\nrather than trivial scaling artifacts.\nThese clarifications have been added to the text (see page 29, METHOD → Time-Resolved\nRepresentational Similarity Analysis and Statistics, paragraph 2; and page 32, METHOD →\nEEG Univariate Analysis and Statistics, paragraph 1).\n3. Were the same features extracted from AB movies also used in the BC RSA? I assume so\nin order to make vector-based correlation feasible, but it should be explicitly stated to avoid\nconfusion.\nWe thank the reviewer for this helpful comment. The reviewer is correct that the features\nextracted from the AB movies were also used in the BC/XY RSA, ensuring that the vector-\nbased correlation was feasible. This has now been explicitly clarified in the manuscript (see\npage 30, top paragraph continuing from the previous page).\n4. Relatedly, the type of correlation metric used in the RSA should be reported. Was Pearson\nor Spearman correlation used? If Pearson, were r-values transformed into z-scores before\ngroup-level statistical analyses to ensure comparability across distributions?\nWe thank the reviewer for noticing that this important methodological detail was absent in the\noriginal text. The correlation metric used in the RSA was the Pearson correlation coefficient,\nand the resulting r-values were Fisher z-transformed prior to subsequent averaging and\nsubtraction. This information has now been added to the manuscript (see page 30, paragraph\n1).\n5. On a more conceptual note, the rationale for using coherence as the central neural feature\nshould be addressed more directly. Why was coherence chosen over more standard power-\nbased TFR features? Would the same representational patterns emerge with power instead\nof coherence? As this appears to be one of the first studies to use coherence in this way, it\nwould be helpful to highlight its novelty and justify its use over other possible measures.\nWe thank the reviewer for raising this point, as it provides an opportunity to clarify our feature\nselection strategy. Since each movie was presented five times, temporal shifts in neural\nresponses are expected to occur across the repeated viewings (Lee et al., 2021). Wavelet\ncoherence is particularly sensitive to subtle phase differences (Lachaux et al., 2002), making it\na well-suited tool to capture and quantify these temporal misalignments.\nAn additional advantage of coherence is that it can be computed at the full channel × timepoint\n× frequency level for both within- and between-movie comparisons, thereby preserving the\nmultidimensional structure of the data. In contrast, power-based TFR features cannot be used\nin this way without losing at least one dimension, which would limit their capacity to capture\nboth within- and between-AB movie consistency.\nAccordingly, we used wavelet coherence to extract features that reflected stable neural\nrepresentations for each AB movie by selecting those features that were consistent across\nrepetitions (within-AB movie) yet distinct across different movies (between-AB movie). This\nrationale has now been explicitly described in the manuscript (see page 11, RESULTS → EEG\nResults → Feature Selection Reveals Time-Frequency Signatures of AB Movie Content,\nparagraph 1; and page 28, METHOD → Feature Selection for the Time-Resolved\nRepresentational Similarity Analysis, paragraph 1).\nMinors:\n1. It would be useful to briefly describe the random effects structure of the mixed-effects\nmodels (e.g., whether by-subject or by-item intercepts or slopes were included).\nWe thank the reviewer for this suggestion. The random effect structure for each model is\ndescribed in detail in the supplementary materials (see Supplementary Note 2). To rule out\npotential confounds of random effect (Ambrosius, 2007) while avoiding model overfitting\n(Matuschek et al., 2017), we adopted a stepwise approach. Model fitting began with\nparticipants as the only random intercept, after which additional factors were sequentially\nadded as random slopes. A random slope was retained only if it significantly improved model\nfit (i.e., p < .05 in chi-square test for model fitting comparison, decreased Akaike Information\nCriterion and Bayesian Information Criterion). For each model, we also assessed the\nhomogeneity of variance of the residuals using Levene’s test. In cases of heteroskedasticity, we\nre-ran the model with a restricted variance structure (Audrain & McAndrews, 2022; Liu et al.,\n2024).\nThese details have now been added both to the supplementary materials (see Supplementary\nNote 2) and to the manuscript (see page 26, METHOD → Behavioural Data Analysis and\nStatistics, paragraph 1).\n2. There is a typo in the section describing the behavioral data analysis: “Tucky-correction”\nshould be corrected to “Tukey correction.”\nWe thank the reviewer for noticing this typo. The text has been corrected.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Liu and colleagues describes an EEG study in which 36 participants\nviewed movies simulating real-life interactions. In the experiment, two sets of movies (AB\nand BC pairs) featuring a shared character were used to investigate the temporal dynamics\nof forming integrated and segregated memory representations and their relationship with\nsubsequent memory accuracy. Behaviorally, they found that source memory performance\nwas positively correlated with AC memory inference performance. Using time-resolved\nrepresentational similarity analysis (RSA), they found that the neural representations of AB\nand BC movies share both similarities and dissimilarities. During the encoding of the novel\ncharacter C, there were neural similarities to the AB representation, which predicted\nsuccessful AC memory inference. In contrast, during the presentation of the common\ncharacter B, neural dissimilarities to the AB representation were observed, and these\npredicted accurate source memory. Lastly, univariate analyses revealed that changes in\nalpha-beta power were associated with these neural (dis)similarity patterns. In summary, the\nauthors provide compelling evidence that the brain dynamically and flexibly engages in both\nmemory integration and separation during the encoding of overlapping events. These\nprocesses have distinct temporal and electrophysiological signatures and support different,\ncomplementary memory functions.\nOverall, I find this study is well-motivated and clearly written. The analyses are appropriately\nconducted and convincing. The findings expand our understanding of the neural\nmechanisms for forming and maintaining both integrated and segregated memory\nrepresentations. In particular, temporally separating the neural processing of integration\nand separation is novel (to my knowledge) and has broad implications for our understanding\nof the neural mechanisms underlying overlapping memories. I only have a few comments\nfor the authors, which are mostly about the interpretation of the results:\nWe sincerely appreciate the reviewer’s positive comments about our study and manuscript. We\nare glad that the reviewer found the work to be well-motivated, clearly written, and of potential\nimpact. We carefully considered the reviewer’s suggestions regarding the interpretation of the\nresults, and the manuscript has been revised accordingly. These insightful comments have\nhelped us improve both the clarity and the quality of the paper.\n1. The neural similarity analysis revealed increased neural similarity between the Sim A&B\nrepresentation and the Sim C (and Sim C in context) representation. The authors interpret\nthis as evidence of memory integration. I wonder whether it could merely reflect the\nreactivation of the Sim A&B memory, rather than the formation of an integrated A-B-C\nrepresentation.\nWe thank the reviewer for this comment, which allows us to clarify our interpretation of the\nobserved neural similarities between movie AB and Sim C. In this time window, the neural\nsimilarity between movie AB and Sim C (as well as Sim C in context) was positively associated\nwith AC inference (at 2.8 s), but negatively associated with source memory (at 3.7 s). We\ninterpret this pattern as reflecting the formation of an integrated ABC memory representation.\nOur reasoning is based on prior evidence showing that the formation of integrated memory\nrepresentations facilitates inference but often comes at the cost of reduced source and detail\nmemory (Carpenter & Schacter, 2017, 2018). If the effect were due to the simple reactivation\nof the AB movie during Sim C, we would expect improved inference and preserved or\nenhanced source memory, as reactivation would provide an additional learning opportunity.\nHowever, this is not what we observed. Instead, the neural similarity was associated with\nincreased inference coupled with reduced source memory.\nWe have clarified our interpretation in the revised DISCUSSION (see page 20, top paragraph\ncontinuing from the previous page).\n2. The main analyses focused on the neural similarity between the representation of Sim\nA&B in context and the BC trials. I am somewhat surprised that there was little to no\nevidence showing similarity to Sim A or Sim B individually during the Sim C segment. More\nimportantly, there appears to be no Sim B-specific similarity during the Sim B and Sim B&C\nin context segments, where character B was present on screen. This makes the interpretation\nof neural similarity ambiguous, as the analysis appears unable to capture common visual\ninputs.\nWe are grateful that the reviewer raised this important observation. We agree that the RSA\napproach should be sensitive to shared visual input, and we conducted additional analysis to\nexplicitly confirm this.\nTo validate the method, we used the AB movies, taking the advantage of their repeated\npresentations to examine neural similarity under identical visual input. Specifically, we\nextracted feature vectors for each AB movie segment across repetitions and correlated them\nwith each timepoint of the entire epoch of the same AB movie (from other repetitions) as well\nas with different AB movies. This analysis used a time-resolved approach consistent with our\nmain RSA analyses (see METHOD in the main text for details).\nAs shown in Figure R1, the results confirm that our method captured common visual input in\ntwo complementary ways: (1) within-segment similarity: Neural similarity was higher across\nrepetitions of the same segment than across different AB movies, reflecting consistent\nresponses to identical visual input; and (2) generalization driven by visual overlap: ‘Sim A’\nshowed similarity to ‘Sim A in Context’ and to ‘Sim A& B in Context’, ‘Sim A in Context’\nshowed similarity to ‘Sim A&B in Context’, and ‘Sim B’ showed similarity to the ‘Sim A in B\nin Context’.\nTogether, these findings validate that the RSA approach here employed is indeed sensitive to\nshared visual input. These additional results have been incorporated into Supplementary Note\n3.\nFigure R1. Neural representational similarity differences between repetitions of the same AB movie\nand different AB movies. Shaded areas indicate time windows with significant neural pattern similarity.\nImportantly, when participants encode the BC movie, neural responses are likely influenced by\ntwo potentially opposing cognitive processes. First, memory reactivation/integration: the\npresentation of overlapping Sim B may trigger reactivation of the previously seen Sim B in the\nAB movie, producing increased neural similarity. Second, interference control/separation: to\navoid confusion with the prior AB movie, participants may actively differentiate the current\nBC movie from the previous AB movie, producing increased neural dissimilarity. Critically,\nwe do not know the exact time points during the movie at which one process dominates over\nthe other. If these processes occur with roughly equal frequency across trials, their effects could\ncancel each other out at the group level, providing a plausible explanation for the absence of a\nsignificant overall similarity effect, even when visual input is shared between the two movies.\n3. Related to the previous comment, I wonder what the neural dissimilarity truly reflects. The\nauthors argue that this dissimilarity is evidence of the segregation of AB and BC memories.\nIf that is the case, why would this dissimilarity appear in the Sim B segment instead of the\nSim B&C in context segment, where the full BC memory event is arguably most salient?\nWe thank the reviewer for this insightful comment. We did not have prior predictions about the\nspecific timing of neural similarities or dissimilarities. Interestingly, our data revealed stronger\ndissimilarities when the shared element between the AB and BC movies, that is Sim B, was\npresented on screen. Previous studies have shown that mnemonic interference increases with\nthe degree of overlap between experiences (Ritvo et al., 2019). As a result, memories tend to\nbe represented more distinctly to resolve such interference (Bakker et al., 2008; Kirwan &\nStark, 2007). Since Sim B is the same segment appearing in both movie AB and movie BC, it\nis reasonable to assume that this is the point at which mnemonic interference is strongest.\nConsequently, memories may be represented more separately at this moment in order to\nmitigate the interference.\nSupporting this interpretation, our univariate analyses revealed increased alpha-beta power\nduring the timing of the dissimilarity for BC compared with XY movies. Increases in alpha-\nbeta power have been consistently related with cognitive control mechanisms underlying\ninterference resolution (Waldhauser et al., 2012). The convergence of RSA and univariate\nfindings therefore strengthens the view that the Sim B segment specifically elicits mnemonic\ninterference and corresponding interference resolution, leading to the observed increase in\nneural dissimilarity.\nThese points are now more explicitly elaborated in the revised discussion (see page 21, top\nparagraph continuing from the previous page).\n4. The results show two significant similarity clusters corresponding to the Sim C and Sim\nC in context segments. Do they reflect the same underlying neural process? More generally,\nwhat is the functional difference between the character segment and the character-in-\ncontext segment? After the initial learning, can the character segment (e.g., Sim C) alone\ntrigger memory integration, or is the context an essential component for forming\noverlapping memories?\nWe thank the reviewer for this thoughtful comment. It is indeed plausible that characters\npresented in isolation and characters presented within a narrative context evoke distinct neural\nrepresentations. Movie segments in which characters interact within vivid contexts are likely\nencoded in a context-specific manner, capturing the broader event structure and supporting\nmemory for the entire episode. In contrast, segments where characters are presented alone and\nin a static format may elicit more abstract representations, reflecting a generalized agent who\ncan appear across multiple interactions. This interpretation is consistent with prior literature\nsuggesting that memory representations derived from naturalistic experiences tend to integrate\nspatiotemporal and relational context (Baldassano et al., 2017, 2018).\nIn our data, neural similarity to ‘Sim A&B in Context’ increased during both the ‘Sim C’ and\n‘Sim C in Context’ segments. However, only the similarity during ‘Sim C in Context’\nsignificantly predicted AC inference performance. This distinction may simply reflect the\nhigher salience of animated, context-rich stimuli compared with static character images,\npotentially yielding a stronger signal-to-noise ratio in the similarity analysis.\nThus, while the distinction between naturalistic and simplified stimuli appears to have\nimportant consequences for memory representations, future studies will be necessary to clarify\nhow this factor influences the balance between memory integration and separation. These\nconsiderations have been incorporated into the revised discussion (see page 21, top paragraph\ncontinuing from the previous page).\n5. The current experimental design nicely separates the memory integration and separation\nprocesses in time and examines their relationship to behavior. To bridge the results to the\nexisting literature, it might be worthwhile to discuss the possible mechanism if the BC pair\nwere presented simultaneously (i.e., only a Sim B and C in context segment). Would\nintegration and separation still both occur? If so, would they have different temporal\ndynamics within that single segment? Or might the two processes occur simultaneously but\nbe supported by different neural circuits?\nWe thank the reviewer for this thoughtful comment. Indeed, our current design allowed us to\ntemporally separate memory integration and separation processes. Nevertheless, we speculate\nthat the simultaneous presentation of the BC pair (i.e., only a ‘Sim B and C in context’ segment)\ncould still trigger both integration and separation, albeit with temporal patterns that differ from\nthose observed in the present study.\nUpon encountering the shared B, integration would be triggered by the reactivation of the prior\nAB memory, which would facilitate the formation of an integrated ABC representation. This\nprocess appears to depend primarily on hippocampal-neocortical interactions that support\nbinding across episodes (Shohamy & Wagner, 2008; Zeithamova et al., 2012). Concurrently,\nseparation processes may be engaged to resolve interference between overlapping AB and BC\nrepresentations, recruiting cognitive control mechanisms involving prefrontal regions (Ritvo et\nal., 2019; Wimber et al., 2015). Thus, with simultaneous BC presentation, integration and\nseparation processes likely operate in parallel within the same time window, as they rely on\ndistinct neural substrates. As a result, dissociating these processes under this condition is\nchallenging, which may partly explain why previous studies have typically reported either\nintegration or separation (Brunec et al., 2020; Holmes et al., 2022; Ritvo et al., 2019).\nBy presenting event components separately, our design allowed us to disentangle these\nprocesses temporally and observe their engagement during the encoding of overlapping events.\nOur data showed that the engagement of integration and separation processes varies with the\nperceptual similarity between novel information and prior experience. To bridge our findings\nwith existing literature, we have now incorporated this discussion into the manuscript (see page\n22, paragraph 1).\n6. Given that both integration and separation processes are observed, I wonder if there is\nany evidence of a trade-off between them. For example, could stronger integration of a BC\nmemory into an AB memory lead to a weaker separation process in the Sim B segment, which\nmight, in turn, affect source memory performance? Conversely, could stronger integration\ncreate a greater need for separation, thereby increasing the observed neural dissimilarity?\nWhile this is an interesting question, I would understand if the authors find it to be outside\nthe scope of the current paper.\nThis is an interesting question. To address it, we conducted a partial correlation analysis at the\ntrial level, controlling for repetition effects (see Table R1). These results indicate that neural\npattern similarities tend to co-occur within the same trial. However, the relationship between\nsimilarity and dissimilarity remains unclear. While the idea of a potential trade-off between\nintegration and separation is compelling, a definitive conclusion would require further\ninvestigation. This analysis has been incorporated into Supplementary Note 6.\nTable R1 Partial correlation coefficients of the similarities and the dissimilarity\nSimilarityat 1.0s Similarity at 2.8s Similarityat 3.7s\nSimilarityat 2.8s 0.001**\nSimilarityat 3.7s 0.038** 0.046**\nDissimilarityat 7.4s 0.021** -0.016** 0.019**\n* - p < .05; ** - p < .01\n7. The RSA results showed that the neural similarity and dissimilarity measurements predict\nAC inference and source memory performance. Are these neural markers also associated\nwith memory for episodic details? For example, might a strongly integrated representation\ncome at the cost of memory for details like the character's clothing? Again, I understand if\nthe authors consider this question beyond the scope of the current paper.\nWe explored whether participants’ memory for clothing was related to the neural similarity\nbetween the AB and BC movies (see Supplementary Note 8). The results revealed that the\nsimilarity between AB movie and the ‘Sim A in Context’ was negatively associated with\nmemory for clothing. However, this effect was not predictive of AC inference performance.\nTherefore, we do not have evidence to conclude whether this similarity reflects the formation\nof an integrated representation or whether there is a trade-off between integration and the\nretention of episodic details. Addressing these questions will require further research.\nReviewer #3 (Remarks to the Author):\nSummary\nThis study makes a timely and valuable contribution to the field of memory research by\ninvestigating how the brain encodes episodic events to support multiple mnemonic functions.\nLeveraging an ABC associative inference paradigm—a framework well-suited to studying\nmemory generalization and inference—the authors employed naturalistic stimuli (simulated\nreal-life movies) and multivariate analysis techniques to explore the flexible encoding of\nrelated experiences. Participants first watched a series of short movies depicting interactions\nbetween two characters (AB pairs), followed by a second set of scenes involving one familiar\nand one novel character (BC pairs). This design enabled the testing of indirect (AC)\nassociations, as well as source memory—i.e., whether participants remembered if two\ncharacters had ever appeared together. This dual focus allowed the authors to probe two\ndistinct but complementary memory processes: integration, where overlapping events are\nlinked into unified representations, and separation, where distinct experiences are preserved\nindependently.\nThe use of time-resolved representational similarity analysis (RSA) on EEG data allowed\nthe authors to track the neural dynamics underlying memory formation with high temporal\nprecision. Importantly, the results revealed that neural pattern similarity between AB and\nBC episodes predicted successful inference of indirect (AC) associations—indicating\nintegration. In contrast, neural dissimilarity predicted accurate source memory, pointing to\nthe preservation of separate memory traces. This double dissociation is both conceptually\nand methodologically interesting. It suggests that the brain does not rigidly encode\nexperiences in one way but instead adapts flexibly, balancing integration and separation\ndepending on the demands of future retrieval. These findings align with and extend\ntheoretical frameworks suggesting that memory systems must support both generalization\n(e.g., for inferential reasoning) and specificity (e.g., for accurate source recollection).\nThe use of naturalistic stimuli adds ecological validity, enhancing the relevance of the\nfindings for real-world memory processes. Moreover, the combination of behavioral and\nneural data—analyzed with state-of-the-art multivariate methods—demonstrates a\nsophisticated experimental approach that provides insights not accessible with traditional\nunivariate or trial-averaged analyses. While the presented findings add to a growing body of\nevidence with potential implications for memory disorders, I have some major and a few\nminor issues that would need to be addressed and clarified before publishing. I detail these\nconcerns below.\nWe thank the reviewer for the positive and constructive remarks about our study and paper. We\nare glad that the reviewer recognizes the conceptual and methodological contributions of our\nwork, including the use of time-resolved RSA to track neural dynamics underlying memory\nformation, the double dissociation between neural similarity and dissimilarity in supporting\nintegration versus separation, and the ecological validity provided by naturalistic stimuli. We\nalso appreciate the acknowledgment of our combination of behavioral and neural data analyzed\nwith advanced multivariate methods, which provides insights not accessible with traditional\nunivariate approaches. We address the specific concerns raised by the reviewer in detail below.\nMajor concerns:\n1. My primary concern lies with the introduction, which needs to be substantially rewritten\nto better frame the study's rationale. As it stands, the introduction heavily emphasizes prior\nfMRI findings, particularly the role of hippocampal subregions in ABC inference paradigms.\nWhile this context is somewhat relevant, it reads more like a retrospective interpretation of\nthe current results than a focused theoretical or empirical motivation for the hypotheses\ntested in this EEG study. Critically, the introduction fails to reference existing M/EEG\nresearch that has examined inference and generalization using similar paradigms, including\nstudies employing representational similarity analysis (RSA). These omissions weaken the\njustification for using EEG and limit the reader’s understanding of how this work builds on\nor ex-tends prior findings especially concerning brain oscillations.\nWe appreciate the reviewer’s feedback on the introduction. In the revised manuscript, the\nintroduction was substantially rewritten to better align with the goals of the current study.\nRather than retrospectively interpreting our results through the lens of prior fMRI findings, the\nrevised framing emphasizes the theoretical motivation to study how memory integration and\nseparation unfold during encoding—processes that the EEG method is well-suited to\ninvestigate, especially given its sensitivity to temporal dynamics and shifts.\nTo strengthen the empirical context, we have incorporated relevant M/EEG studies that have\nexamined inference and generalization using paradigms related to ABC inference, including\nthose applying representational similarity analysis (Backus et al., 2016; Heinbockel et al., 2022;\nHerweg et al., 2020; Jafarpour et al., 2014; Nicolás et al., 2021). These changes clarify how\nour work builds on and extend prior EEG research on memory integration (see page 3-5).\nWe are open to including any additional literature that the reviewer or the editor considers\nrelevant to the study.\n2. Furthermore, although the authors perform both univariate and multivariate time-\nfrequency analyses, the introduction lacks any predictions of relevant frequency bands—\nsuch as theta—which are known to play a key role in memory integration and retrieval.\nWithout grounding the study in this literature, the introduction does not adequately set up\nthe relevance of frequency-specific dynamics to the research questions. A revised\nintroduction should clearly articulate the motivation for using EEG, specify what temporal\nor spectral in-sights it can offer beyond fMRI, and situate the work within the existing\nelectrophysiological literature on memory generalization and inference.\nWe thank the reviewer for this suggestion. We agree that the original introduction lacked\nsufficient grounding in the electrophysiological literature, particularly regarding frequency-\nspecific dynamics relevant to memory inference and integration. In the revised manuscript, we\nnow clearly articulate why EEG is well-suited to address our research questions—specifically,\nits ability to capture the temporal evolution and spectral content of neural signals during\nencoding.\nAdditionally, we now present specific hypotheses about the roles of theta (4-7 Hz) and alpha–\nbeta (8-30 Hz) activity in memory processes. Drawing on prior EEG studies, we discuss how\ntheta activity has been linked to mnemonic integration and associative binding (e.g., during\noverlapping episode encoding), while alpha-beta activity has been implicated in both memory\nreinstatement and inhibitory control mechanisms that may support memory separation. This\nhas been added to the text (see page 5).\nThese revisions strengthen the theoretical motivation for our time-frequency analyses and\nsituate our work within a growing body of M/EEG research on memory generalization,\ninference, and representational dynamics. We believe these changes provide a clearer rationale\nfor our analytic approach and the contribution of EEG beyond prior fMRI findings.\nGenerally, the results seem incomplete and demand deeper analyses:\n3. A major concern relates to the spatial characterization of the reported neural similarity\neffects. While I appreciate that the RSA was conducted at the whole-brain level, the results\nas presented lack any indication of where on the scalp these effects are most prominent.\nEspecially given the introduction’s emphasis on specific brain regions and memory-related\npro-cessing, I would have expected at least a basic breakdown of whether the observed effects\nare stronger over frontal, parietal, or other regions. To enhance interpretability, I\nrecommend the authors either (1) report the topographical distribution of the similarity\neffects or (2) re-run the RSA restricted to functionally meaningful electrode clusters (e.g.,\nfrontal, central, parietal). This would provide valuable insight into the potential cognitive\nand neural sources of the effects and better connect the findings to prior literature on\nmemory integration and segregation.\nWe thank the reviewer for inquiring about the spatial characterization of the reported neural\nsimilarity effects. To address this concern, we re-ran the RSA at the channel level. Specifically,\nwe estimated the RSA for each electrode along with its immediate neighbors (for a similar\nprocedure, see Bramão et al., 2025; Bramão & Johansson, 2018) to reconstruct the\ntopographical distribution of neural pattern similarity to ‘Sim A&B in Context’ during BC\nmovie viewing (see Figure R2).\nThe results showed that both similarity and dissimilarity effects were broadly distributed across\nthe scalp without a clear focal topography. Notably, this widespread topography aligns with the\nunivariate analysis results (see Figure 6), suggesting that while these processes may originate\nfrom distinct hippocampal subregions, they likely engage widespread cortical networks and\nreflect large-scale neural activity.\nThis topographical analysis has been added to the supplementary materials (see\nSupplementary Note 4) to provide additional insight into the spatial characteristics of the\nobserved effects.\nFigure R2 Topographical distribution of similarity and dissimilarity between ‘Sim A&B in Context'\nand BC movie.\n4. Closely related to the previous point, it is unclear why no source reconstruction analyses\nwere conducted, especially given that the experiment was designed around RSA with a clear\ntheoretical focus on specific brain regions, notably the hippocampus. While I understand\nthat the RSA was performed at the sensor level, this approach limits anatomical\ninterpretability—particularly in a study aiming to link EEG pattern similarity to memory\nmechanisms commonly associated with deep brain structures. Given the relevance of medial\ntemporal regions in memory integration and inference, I strongly recommend that the\nauthors complement their current analyses with source-level RSA or at least conduct source\nlocalization of the key effects. This would significantly strengthen the anatomical specificity\nof the findings and align the EEG results more directly with the fMRI literature that\nmotivated the study.\nWe appreciate the reviewer’s suggestion to include source reconstruction analyses to enhance\nthe anatomical interpretation of our findings. We fully agree that linking EEG-based RSA\nresults to underlying brain structures is an important goal, especially given the central role of\nmedial temporal regions in memory integration and inference.\nHowever, we ultimately decided not to pursue source-level RSA in this study for both\nmethodological and empirical reasons. First, as the reviewer notes, integration and separation\nare processes likely triggered by the hippocampus—a deep brain structure that is impossible to\ninvestigate with scalp EEG.\nSecond, our sensor-level analyses revealed broadly distributed effects across the scalp, without\nclear focal topographies that would guide targeted source reconstruction. In the absence of\nstrong, a priori constraints, or distinct topographical patterns, we were concerned that source\nlocalization would introduce uncertainty without yielding interpretable anatomical specificity.\nThat said, we agree this is an important direction for future research. In particular, combining\nEEG with complementary imaging modalities (e.g., fMRI-constrained source modelling or\nintracranial recordings) could help clarify the neural generators of the observed RSA effects.\nWe acknowledge these limitations and future directions in the Discussion section (see page 23).\n5. Given the well-known limitations of detecting hippocampal activity with EEG—even when\nemploying advanced source reconstruction methods such as LCMV beamformers—I believe\nit would be valuable for the authors to explore functional connectivity of relevant cortical\nregions, such as the angular gyrus, in relation to the reported effects. Incorporating\nconnectivity analyses would enhance the interpretive depth of the findings and provide a\nmore comprehensive picture of the neural mechanisms underlying the observed behavioral\noutcomes.\nWe thank the reviewer for this thoughtful suggestion. Following the recommendation, we\nexplored whether functional connectivity analyses could provide additional insight into the\nneural mechanisms underlying representational similarity and dissimilarity.\nWe conducted a dynamic functional connectivity analysis at the channel level. Specifically, we\nestimated connectivity between all channel pairs at each time point by correlating their decibel\ntransformed power values across the 3–35 Hz frequency range, using Pearson correlation\nfollowed by fisher-z transformation for statistical purposes. This yielded a time course of\ndynamic functional connectivity for each trial throughout the duration of BC movie. XY movie\nwas also investigated and included as a baseline.\nTo evaluate condition effects, we first computed the difference in connectivity between BC and\nXY encoding, averaged within each segment, using paired t-test with Bonferroni corrections to\np-values (see Figure R3); however, no significant difference emerged.\nNext, to assess whether connectivity dynamics were predictive of representational similarity,\nwe concatenated the trial-wise connectivity time courses during BC encoding for each\nparticipant and used them to predict the neural pattern similarity between AB and BC movies\n(concatenated in the same way). Using the Bayesian linear regression (with Normal-Inverse-\nGamma distribution prior, alpha = 15, beta = 15, mu = 0, lambda = I * 30, I is unit matrix),\nwe calculated the regression coefficient matrix reflecting the contribution of each connectivity pair\nin predicting similarity values (see Figure R4). However, no functional connectivity was\npredictive of neural pattern similarity.\nTaken together, the connectivity analyses did not provide conclusive evidence for either\ncondition-related differences or a systematic relationship to neural similarity/dissimilarity. For\nthis reason, we have opted not to include them in the paper. However, if the reviewer or editor\nbelieves that these analyses would still add value, we would be glad to incorporate them as\nsupplementary material.\nFigure R3 Channel-wise dynamic functional connectivity within each segment during BC/XY\nencoding.\nFigure R4. Standardized Bayesian Regression results showing how dynamic functional connectivity\npredicts neural pattern similarity.\nMinor concerns:\n1. It would be helpful if the authors addressed potential effects of the repeated presentation\n(5×) of the stimuli. Since neural signals were averaged across repetitions, the role of novelty\nvs. familiarity—a key factor in memory encoding—should be considered. The neural\ndynamics of the first versus the fifth presentation may differ substantially, and this could\nimpact both the behavioral and neural findings. A brief analysis or at least a discussion of\nhow repetition may have influenced RSA results are needed.\nWe thank the reviewer for raising this point. Repeated exposure to the movie clips likely\nstrengthened event representations and could have modulated the neural dynamics underlying\nmemory formation. To examine this, we analyzed the trajectory of neural pattern similarity and\ndissimilarity across the five repetitions (Supplementary Note 5).\nThe results indicated that changes were not linear across all repetitions. Instead, neural\nrepresentations showed incremental updating during the first few viewings, after which they\nstabilized. This pattern could be consistent with novelty-driven updating processes being most\nprominent early on, while later repetitions reflected more stable, familiarity-based retrieval.\nWe have now incorporated this consideration into the Discussion (see page 21, top paragraph\ncontinuing from the previous page), highlighting how novelty versus familiarity may have\ninfluenced the RSA results.\n2. The behavioral data in Figure 3 appear to exhibit substantial ceiling effects, particularly\nin accuracy scores. The authors should clarify whether the use of linear mixed models\n(LMMs) is appropriate given the limited variance. Please elaborate on how this was handled\nin the analysis and interpretation.\nWe acknowledge that ceiling effects were present in the behavioral data, but they were confined\nto the direct association trials, particularly the AB condition. Crucially, the primary focus of\nthe study lies in the indirect AC association trials and source memory, where no ceiling effects\nwere observed.\nRegarding the use of linear mixed models (LMMs), we conducted model-fitting tests to\nevaluate the contribution of each factor to model performance and to ensure that potential\nconfounds were controlled (see page 26, METHOD → Behavioral Data Analysis and Statistics,\nparagraph 1, and Supplementary Note 2). See also the minor comment 1 from Reviewer 1.\nThese steps mitigate concerns that the limited variance in the direct association trials could bias\nthe overall conclusions. Moreover, since our key findings are based on the indirect AC\nassociations, where performance showed sufficient variability, we believe the use of LMMs\nremains appropriate for the current data.\n3. Response times for BC retrieval were notably slower than for AB. This difference deserves\nfurther discussion. Could this be due to fatigue, re-encoding demands, or greater cognitive\nload during BC trials? Including a plausible interpretation would help contextualize this\nfinding.\nWe thank the reviewer for highlighting this effect. As noted, participants’ response times were\nsignificantly longer for BC trials compared to both AB and XY trials.\nBecause BC and XY movies were presented in an interleaved fashion during encoding, it is\nunlikely that the slower BC responses reflect fatigue or general task disengagement. A more\nplausible explanation is that BC trials elicited stronger interference due to the overlapping Sim\nB, which likely reactivated competing AB associations. This retrieval competition could have\nincreased retrieval demands, consistent with prior work showing that proactive interference\nand conflict resolution can prolong response times in associative memory tasks (Bramão et al.,\n2025; Liu et al., 2024). We have now incorporated this discussion into the main text (see page\n9, Results → Behavioral Results → Associative Memory Test, paragraph 2).\n4. In the caption of Figure 2, there is a minor typo:\n“(D) Systematic similarities, indicative of memory integration should predict of AC retrieval\nperformance.” This should be corrected to:“(D) Systematic similarities, indicative of\nmemory integration, should be predictive of AC retrieval performance.”\nThe text was revised accordingly.\n5. Did the authors assess the perceptual or narrative similarity between the SIM movies and\nstories (AB, BC pairs)? If some stimuli were more similar than others, this could confound\nRSA results. It would strengthen the study to report whether similarity ratings (e.g., by\nindependent raters) were obtained and whether these were controlled for or included in the\nanalyses.\nWe appreciate the reviewer’s thoughtful question regarding potential perceptual or narrative\nsimilarity between the Sim movies and how this might influence RSA results. While we did\nnot obtain independent similarity ratings for the AB, BC, or XY movies, we took care to address\nthis concern through our experimental design.\nSpecifically, the assignment of AB/BC and XY movies was fully counterbalanced across\nparticipants, such that each movie pair could appear in different conditions (AB/BC or XY)\nacross individuals. This design ensured that any inherent perceptual or narrative similarity\nbetween specific movies was not confounded with the condition (i.e., BC vs. XY). In other\nwords, across the full sample, each movie pair was equally likely to appear as AB/BC or XY\ncondition, minimizing the impact of stimulus-specific similarity on the RSA results.\nThis is now clearly explained in the text (see page 25, paragraph 1)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors did an excellent job and have fully addressed all my previous concerns. I am happy to recommend the manuscript for publication in its current form.", "reviewer_2": "The authors have satisfactorily addressed all my comments. I found the supplementary analyses and additional discussions very useful. I have no further concerns and am happy to recommend the manuscript for publication.", "reviewer_3": "The authors have substantially revised the manuscript and have provided thorough, detailed responses to all of my questions. The Introduction has been carefully revised as requested and is now significantly improved in both clarity and structure. All of the concerns I previously raised have been fully addressed, and I now consider the manuscript to be in excellent shape."}, "author_response": ""}]

Decision letter