Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Reviewer #1 (Public Review):

Summary:
Previous work in humans and non-human animals suggests that during offline periods following learning, the brain replays newly acquired information in a sequential manner. The present study uses a MEG-based decoding approach to investigate the nature of replay/reactivation during a cued recall task directly following a learning session, where human participants are trained on a new sequence of 10 visual images embedded in a graph structure. During retrieval, participants are then cued with two items from the learned sequence, and neural evidence is obtained for the simultaneous or sequential reactivation of future sequence items. The authors find evidence for both sequential and clustered (i.e., simultaneous) reactivation. Replicating previous work by Wimmer et al. (2020), low-performing participants tend to show sequential, temporally segregated reactivation of future items, whereas high-performing participants show more clustered reactivation. Adding to previous work, the authors show that an image's reactivation strength varies depending on its proximity to the retrieval cue within the graph structure.

Strengths:
As the authors point out, work on memory reactivation has largely been limited to the retrieval of single associations. Given the sequential nature of our real-life experiences, there is clearly value in extending this work to structured, sequential information. State-of-the-art decoding approaches for MEG are used to characterize the strength and timing of item reactivation. The manuscript is very well written with helpful and informative figures in the main sections. The task includes an extensive localizer with 50 repetitions per image, allowing for stable training of the decoders and the inclusion of several sanity checks demonstrating that on-screen items can be decoded with high accuracy.

Weaknesses:
Of major concern, the experiment is not optimally designed for analysis of the retrieval task phase, where only 4 min of recording time and a single presentation of each cue item are available for the analyses of sequential and non-sequential reactivation. The authors could consider including data from the (final) learning blocks in their analysis. These blocks follow the same trial structure as the retrieval task, and apart from adding more data points could also reveal important insights regarding a possible shift from sequential to clustered reactivation as learning of the graph structure progresses.

On a more conceptual note, the main narrative of the manuscript implies that sequential and clustered reactivation are mutually exclusive, such that a single participant would show either one or the other type. With the analytic methods used here, however, it seems possible to observe both types of reactivation. For example, the observation that mean reactivation strength (across the entire trial, or in a given time window of interest) varies with graph distance does not exclude the possibility that this reactivation is also sequential. In fact, the approach of defining one peak time window of reactivation may be biased towards simultaneous, graded reactivation. It would be helpful if the authors could clarify this conceptual point. A strong claim that the two types of reactivation are mutually exclusive would need to be supported by further evidence, for instance, a metric contrasting sequenceness vs clusteredness.

On the same point, the non-sequential reactivation analyses often use a time window of peak decodability that appears to be determined based on the average reactivation of all future items, irrespective of graph distance. In a sequential forward cascade of reactivations, it seems reasonable to assume that the reactivation of near items would peak earlier than the reactivation of far items. The manuscript would be strengthened by showing the "raw" timecourses of item decodability at different graph distances, clearly demonstrating their peak reactivation times.

Reviewer #2 (Public Review):

Summary:
The authors investigate replay (defined as sequential reactivation) and clustered reactivation during retrieval of an abstract cognitive map. Replay and clustered reactivation were analysed based on MEG recordings combined with a decoding approach. While the authors state to find evidence for both, replay and clustered reactivation during retrieval, replay was exclusively present in low performers. Further, the authors show that reactivation strength declined with an increasing graph distance.

Strengths:
The paper raises interesting research questions, i.e., replay vs. clustered reactivation and how that supports retrieval of cognitive maps. The paper is well-written, well-structured, and easy to follow. The methodological approach is convincing and definitely suited to address the proposed research questions.

The paper is a great combination between replicating previous findings (Wimmer et al. 2020) with a new experimental approach but at the same time presenting novel findings (reactivation strength declines as a function of graph distance).
What I also want to positively highlight is their transparency. They pre-registered this study but with a focus on a different part of the data and outlined this explicitly in the paper.

The paper has very interesting, individual findings but there are some shortcomings.

Weaknesses:
Even though the individual findings are interesting, it is not easy to grasp how they are related. For example, the authors show that replay is present in low but not in high performers with the assumption that high performers tend to simultaneously reactivate items. But then, the authors do not investigate clustered reactivation (= simultaneous reactivation) as a function of performance (due to ceiling effects for most participants).

Unfortunately, the evidence for clustered reactivation is not well supported by the analysis approach and the observed evidence. The analysis approach still holds the possibility of replay driving the observed clustered reactivation effect.

A third shortcoming is that at least some analyses are underpowered (very low number of trials, n = ~10, and for some analyses, very low number of participants, n = 14). In both cases (low trial number and low participant number) the n could be increased by including the learning part in the analyses as well. It is not clear to me why the authors restricted their analyses to the retrieval period only (especially given that participants also have to retrieve during learning).