Humans perceive and remember continuous experiences as discrete events (Brunec et al., 2018; Clewett et al., 2019; Shin and DuBrow, 2021; Zacks, 2020). Studies of event segmentation have shown that when participants attend to external information (e.g., watch a video), (1) boundaries between events are detected when mismatches arise between predicted and actual sensory input (Zacks et al., 2007; Zacks et al., 2011), and (2) boundary detection evokes transient neural responses in a consistent set of brain areas (Reagh et al., 2020; Speer et al., 2007; Zacks et al., 2001). Among these areas is the default mode network (DMN; Buckner and DiNicola, 2019) proposed to be involved in representing complex mental models of events (Ranganath and Ritchey, 2012; Ritchey and Cooper, 2020). However, a substantial portion of human cognition is internally driven (Hasselmo, 1995; Honey et al., 2018), and such spontaneous production of thoughts and actions is also punctuated by mental context transitions (Christoff et al., 2016; Mildner and Tamir, 2019; Smallwood and Schooler, 2015; Tseng and Poppenk, 2020). What manner of brain activity marks boundaries between mental contexts when they are internally generated? Are the brain responses at internal boundaries similar to those at external boundaries?

Here, we used naturalistic movie viewing and free spoken recall with fMRI to characterize neural activity at boundaries between internally generated mental contexts (Figure 1A). Subjects watched 10 short movies (encoding phase), then verbally recounted the movies in any order, in their own words (recall phase). The transitions between recalled movies were determined purely by subjects’ internal mentation; no external cues prompted the recall onset or offset of each movie. Moreover, the unguided spoken recall allowed us to identify the exact moments of context transitions and explicitly track shifts in the contents of thoughts (Chen et al., 2017; Sripada and Taxali, 2020), which was not possible in prior studies using silent rest (Karapanagiotidis et al., 2020; Tseng and Poppenk, 2020). At these internal boundaries between recalled movies, we observed transient, highly generalizable and fine-grained activation patterns throughout the DMN, consistent across diverse movie contents and similar to those at external between-movie boundaries during encoding. Moreover, these between-movie boundary patterns were not merely stronger versions of within-movie ‘event boundaries,’ but instead manifested as a distinct type of neural transition. We propose that these cortical patterns reflect a cognitive state related to the major flushing and reconfiguration of mental context (DuBrow et al., 2017; Manning et al., 2016).

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We first examined whether internally driven boundaries evoke changes in blood oxygen level-dependent (BOLD) signals during recall. We observed transient changes in activation at the boundaries between recalled movies in widespread cortical regions (Figure 1—video 1; see Figure 1—figure supplement 1 for activation time courses). A whole-brain analysis with multiple comparisons correction revealed that the mean activation of boundary periods (15 s following the offset of each movie) was generally lower than that of non-boundary periods (middle 15 s within each movie) in multiple areas, including the motor, auditory, and inferior parietal cortices, although a smaller number of regions showed higher activation during non-boundary periods (Figure 1B).

Next, we tested whether there were neural activation patterns specific to internally driven boundaries and consistent across different movies. We performed a whole-brain pattern similarity analysis on the recall data to identify regions where (1) boundary period activation patterns were positively correlated across different recalled movies (Figure 2A, blue arrow a > 0), and (2) this correlation was higher at boundaries than at non-boundaries (Figure 2A, blue arrows a > b). We observed a consistent boundary pattern, that is, whenever participants transitioned from talking about one movie to the next, in several cortical parcels (Schaefer et al., 2018), including the DMN and auditory/motor areas (Figure 2B). Thus, the boundary patterns within the recall phase were likely to be driven by both shared low-level sensory/motor factors (e.g., breaks in recall speech generation) as well as cognitive states (e.g., memory retrieval) at recall boundaries. No cortical parcel showed significantly negative correlations between boundary patterns or greater correlations in the non-boundary compared to boundary conditions.

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To what extent is the internally driven boundary pattern, measured during recall, similar to patterns observed at boundaries during encoding? To test this, we again computed between-movie pattern similarity for all cortical parcels in the brain, but now across the encoding and recall phases (Figure 2A, red arrows). We found that DMN areas showed a consistent boundary pattern across task phases (encoding and recall) and across movies (Figure 2C). Again, no cortical parcel showed negative correlations between boundary patterns or greater correlations in the non-boundary condition. Among the DMN areas, the posterior medial cortex (PMC) showed the most consistent boundary patterns; thus, we next examined the phenomenon in more detail specifically in PMC. Figure 3A and C visualize the high and consistently positive correlations of PMC boundary patterns across different movies both within the recall phase (recall offset vs. recall offset, t(14) = 11.82, p<0.001, Cohen’s d_z = 3.05, 95% confidence interval (CI) = [0.28,0.41]) and even between experimental phases (recall offset vs. encoding offset, t(14) = 14.54, p<0.001, Cohen’s d_z = 3.75, 95% CI = [0.28,0.38]). No such correlation was present between non-boundary patterns (t(14)s < 1, ps > 0.3). Individual subjects’ activation maps visualize the similarity between boundary patterns during encoding and recall (Figure 3B, Figure 3—figure supplement 1). We observed similar results in the lateral parietal DMN subregion (angular gyrus; Figure 3—figure supplement 2), as well as using shorter (4.5 s) time windows of boundary and non-boundary periods (Figure 2—figure supplement 1, Figure 3—figure supplement 3).

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Thus far, we tested boundary responses following offsets, based on prior findings that post-stimulus neural responses contribute to memory formation (Ben-Yakov et al., 2013; Ben-Yakov and Dudai, 2011; Medvedeva et al., 2021). However, other studies also reported neural responses specific to the onset of an episode (Bulkin et al., 2020; Fox et al., 2005; Wen et al., 2020). Is the generalized boundary pattern evoked by the onset of a movie, rather than the offset? We examined this question by comparing the temporal emergence of the generalized boundary pattern following movie offsets versus onsets (Figure 3D); note that the offset of a movie was temporally separated from the onset of the following movie during both encoding and recall (see Figure 1A). Specifically, we extracted the mean time series of PMC activation patterns around between-movie boundaries, time-locked to either the onset or offset of each watched or recalled movie. We then computed between-phase (encoding-recall) pattern similarity across the individual time points of the activation pattern time series. We found that significantly positive between-phase correlations emerged well before the encoding and recall onsets (Figure 3D, left panel), starting from 4.5 s following the offsets of the preceding watched or recalled movie (Figure 3D, right panel). Thus, boundary patterns were not exclusively triggered by movie onsets; it is likely that offset responses significantly contributed to the boundary patterns.

We focused our analyses up to this point on transitions between movies because they provided clear boundaries between mental contexts during recall. However, event boundaries in naturalistic movie stimuli are often defined as transitions between scenes within a movie (Baldassano et al., 2017; Chen et al., 2017; Zacks et al., 2010). In prior work, it has been shown that for within-movie event boundaries neural responses scale positively with human judgments of the ‘strength’ of scene transitions (Ben-Yakov and Henson, 2018). Thus, we hypothesized that boundaries between movies (i.e., between mental contexts) would manifest as stronger versions of within-movie boundaries with qualitatively similar patterns; in other words, boundary patterns would generalize across different scales of boundaries. To test this idea, we first confirmed that there were consistent within-movie event boundary patterns in PMC during encoding; within-movie boundary patterns were more similar to each other than to non-boundary patterns (Figure 4—figure supplement 1). We then tested whether this within-movie boundary pattern resembled the between-movie boundary pattern by measuring the correlation between (1) the mean between-movie boundary pattern during recall and (2) the mean within-movie event boundary pattern during encoding (Figure 4). Surprisingly, the two were negatively correlated (t(14) = 5.10, p<0.001, Cohen’s d_z = 1.32, 95% CI = [–0.34, –0.14]), in contrast to the strong positive correlation across encoding and recall between-movie boundary patterns (t(14) = 25.02, p<.001, Cohen’s d_z = 6.46, 95% CI = [0.67,0.79]). The within-movie event boundary pattern was also negatively correlated with the encoding phase between-movie boundary pattern (t(14) = 7.31, p<0.001, Cohen’s d_z = 1.89, 95% CI = [–0.44, –0.24]). Within-movie and between-movie boundary patterns did not resemble each other, regardless of the specific time windows used to define the boundary periods (Figure 4—figure supplement 2). These results suggest that the between-movie boundary pattern may reflect a cognitive state qualitatively different from the state elicited by within-movie event boundaries during movie watching.

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Is the generalized between-movie boundary pattern driven by shared low-level perceptual or motoric factors rather than cognitive states? First, shared visual features at between-movie boundaries (i.e., black screen) cannot explain the transient, boundary-specific similarity between encoding and recall phases because visual input was identical across boundary and non-boundary periods during recall (i.e., a fixation dot on black background). Indeed, encoding boundary patterns were more similar to recall boundary patterns than to recall non-boundary patterns in DMN areas, suggesting a limited contribution of shared visual input to the generalized boundary pattern (Figure 2—figure supplement 2). Likewise, the absence of verbal responses at boundaries cannot explain the boundary pattern generalized across encoding and recall phases as no speech was generated throughout the entire encoding phase. Moreover, PMC boundary patterns showed positive between-phase pattern correlations (t(14) = 3.94, p=0.003, Cohen’s d_z = 1.25, 95% CI = [0.1,0.36]) greater than those of non-boundary patterns (t(14) = 3.22, p=0.011, Cohen’s d_z = 1.02, 95% CI of the difference = [0.06,0.36]) even when restricted to boundaries without pauses between recalled movies. We also ruled out the possibility that silence during movie title scenes and pauses at recall boundaries drove the generalized boundary pattern in PMC; the recall boundary pattern was not correlated with the pattern associated with silent periods during encoding (t(14) = 1.93, p=0.074, Cohen’s d_z = 0.498, 95% CI = [–0.19,0.01]), whereas the auditory cortex showed a positive correlation between the two (t(14) = 10.31, p<0.001, Cohen’s d_z = 2.66, 95% CI = [0.3,0.45]) (Figure 5). Likewise, the movies’ audio amplitudes modulated the time course of similarity between the recall boundary pattern and the encoding data in the auditory cortex, but not in PMC (Figure 5—figure supplements 1 and 2).

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This study investigated brain responses to internally generated boundaries between mental contexts during continuous and unguided memory recall of naturalistic narratives. We found that internally driven mental context boundaries evoke generalized neural activation patterns in core posterior-medial areas of the DMN (Ritchey and Cooper, 2020). These cortical patterns were similar to those observed at major boundaries between externally driven contexts (different audiovisual movies), suggesting that they reflect a general cognitive state associated with mental context transitions. However, these between-context patterns were distinct from within-context event boundary detection signals.

The highly similar neural activation patterns for internally- and externally driven boundaries observed in this study demonstrate event segmentation without changes in external input. This finding diverges from the currently dominant empirical and theoretical perspectives on event segmentation; in most studies, event boundaries are defined or manipulated by changes in perceptual or spatiotemporal features (e.g., Chen et al., 2017; DuBrow and Davachi, 2013; Radvansky and Copeland, 2006), and boundary detection is posited to occur when those changes mismatch our expectations of the current situation (Zacks et al., 2007; Zacks et al., 2011). This prediction error framework successfully explains various phenomena related to event perception and memory organization (see Zacks, 2020 for a review); however, evidence has also shown that predicted changes in external features can create boundaries and have similar behavioral effects (Pettijohn and Radvansky, 2016; Schapiro et al., 2013). To resolve the discrepancy, an alternative theoretical framework has recently proposed that boundaries are perceived when the probability distribution of inferred current situations, rather than observed external features per se, changes from the previous time point (Shin and DuBrow, 2021). According to this account, event segmentation can occur when there is no perceptual change or when transitions are already predicted, which may explain the boundary-related neural responses at self-generated transitions between memories during recall in our study.

The boundary pattern that generalized across internally and externally driven boundaries was most strongly observed in the DMN, in line with earlier findings implicating the DMN in mental context transitions (Baldassano et al., 2017; Crittenden et al., 2015; Smith et al., 2018). Prior studies have shown that the DMN responds to external context transitions, including experimental task switching (Crittenden et al., 2015; Smith et al., 2018) as well as event boundaries in movie clips (Reagh et al., 2020; Speer et al., 2007). Considering these findings and the widely known involvement of the DMN in internally oriented cognition (e.g., Addis et al., 2007; Andrews-Hanna et al., 2010; Christoff et al., 2009) together, it has been suggested that the DMN integrates both internal and external information to represent and maintain an abstract mental model of the current situation or state (Stawarczyk et al., 2021; Yeshurun et al., 2021); located furthest away from sensorimotor areas (Smallwood et al., 2021), the DMN integrates information across different modalities (Bonnici et al., 2016; Ramanan et al., 2018) and over long timescales (Chang et al., 2021; Hasson et al., 2015). Supporting this idea, neural activation patterns in subregions of the DMN, especially PMC, tend to persist for extended periods of time during naturalistic movie watching, and transitions between these persistent neural states coincide with perceived event boundaries (Baldassano et al., 2017; Geerligs et al., 2021). Our study extends this finding by identifying a transient, boundary-induced phenomenon, which is a unique and independent state represented in the DMN. That is, at major event boundaries, a temporary boundary state may exist in between the neural patterns representing the two events, rather than one event pattern switching directly to the next.

Although the boundary-related PMC activation patterns were consistent across internally and externally driven boundaries, they did not generalize across within- and between-movie boundaries. Relatedly, a recent human neurophysiological study (Zheng et al., 2022) reported that medial temporal cortex neurons distinguished within- and between-movie boundaries while subjects were watching short video clips; some neurons responded only to between-movie boundaries, whereas a separate group of neurons responded to both types of boundaries. These findings may be in line with the view that event boundaries have a hierarchical structure, with different brain areas along the information pathway reflecting different levels of boundaries, from fine-grained sensory transitions to coarse-grained situational transitions (Baldassano et al., 2017; Chang et al., 2021; Geerligs et al., 2021). However, it is still puzzling that within- and between-movie boundaries in our study produced qualitatively distinct neural patterns within a highest-order area (PMC), even though both categories consisted of prominent boundaries between situations spanning tens of seconds to several minutes. What are the crucial differences between the two levels of boundaries? One important factor might be the presence or absence of inter-event connections. Even the most salient within-movie boundaries still demand some integration of information across events as the events are semantically or causally related, and ultimately constitute a single coherent narrative (Lee and Chen, 2021; Song et al., 2021b). In contrast, an entire cluster of related events, or the narrative as a whole, might be completely ‘flushed’ at between-movie boundaries; this difference could induce distinct cognitive states at the two levels of boundaries, giving rise to different PMC patterns.

What is the cognitive state that is generalized across internal- and external boundaries between completely different contexts, but distinct from the state evoked by boundaries within the same context? We speculate that the between-movie boundary state may be a temporary ‘relay’ state that occurs when no one mental model wins the competition to receive full attentional focus following the flushing of the prior mental context. Namely, when one major mental context switches to another, the brain may pass through a transient off-focus (Mittner et al., 2016) or mind-blanking (Mortaheb et al., 2021; Ward and Wegner, 2013) state that is distinct from both processing external stimuli (e.g., movie watching) and engaging in internal thoughts (e.g., memory recall). This account may also explain the difference between within- vs. between-movie boundary patterns: in terms of attentional fluctuation (Jayakumar et al., 2022; Song et al., 2021a), external attention is enhanced at within-movie event boundaries (Pradhan and Kumar, 2021; Zacks et al., 2007), whereas the relay state is associated with lapses in attention (deBettencourt et al., 2018; Esterman et al., 2014). An alternative, but not mutually exclusive, possibility is that the boundary state involves the recruitment of cognitive control to resolve the competition between mental contexts. This idea is based on the observation that the areas showing relatively higher activation at between-movie boundaries overlap with the frontoparietal control network (FPCN; Vincent et al., 2008) both during encoding and recall (Figure 1B, Figure 1—video 2). As the FPCN is interdigitated with the DMN and other nearby areas within individual subjects (Braga and Buckner, 2017), relative activation of the FPCN may create the stereotyped boundary pattern in higher associative cortices. It is also noteworthy that both of these candidate cognitive states are triggered not by the onset but by the offset of a mental context; the onset would rather signal the resolution of competition between mental contexts, hence the end of those states. This dovetails with our results showing that the generalized boundary pattern appears well before movie onsets, suggesting a major contribution of offset responses.

In conclusion, we found that internally driven boundaries between memories produce a stereotyped activation pattern in the DMN, potentially reflecting a unique cognitive state associated with the flushing and updating of mental contexts. By demonstrating stimulus-independent event segmentation during continuous and naturalistic recall, our study bridges the gap between the fields of event segmentation and spontaneous internal thoughts (also see Tseng and Poppenk, 2020). Without any task demands or external constraints, the mind constantly shifts between different internal contexts (Raffaelli et al., 2021; Sripada and Taxali, 2020). What are the characteristics of neural responses to different types of spontaneous mental context boundaries (e.g., between two different memories, between external attention and future thinking)? Is the boundary pattern observed in this study further generalizable to mental context transitions even more stark than between-movie transitions in our experiment? Are there specific neural signatures that predict subsequent thought transitions? Future work will explore answers to these questions by employing neuroimaging methods with behavioral paradigms that explicitly and continuously track the unconstrained flow of thoughts in naturalistic settings.

Here, we provide a selective overview of procedures and analysis methods. More detailed descriptions of participants, stimuli, experimental procedures, fMRI data acquisition, and preprocessing can be found in Lee and Chen, 2021.

The raw neuroimaging and behavioral data analyzed in the current study are publicly available via OpenNeuro (https://doi.org/10.18112/openneuro.ds004042.v1.0.0). Source data files have been provided for Figure 1-figure supplement 3, Figure 3, Figure 3-figure supplements 2, 3, & 4, Figure 4, Figure 4-figure supplements 1 & 2, Figure 5, and Figure 5-figure supplements 1 & 2.

1. 1. Lee H
   2. Chen J

   (2022) OpenNeuro

   FilmFestival.

   https://doi.org/10.18112/openneuro.ds004042.v1.0.0

1. 1. Addis DR
   2. Wong AT
   3. Schacter DL

   (2007) Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration

   Neuropsychologia 45:1363–1377.

   https://doi.org/10.1016/j.neuropsychologia.2006.10.016

   • PubMed
   • Google Scholar
2. Conference

   1. Ambekar A
   2. Ward C
   3. Mohammed J
   4. Male S
   5. Skiena S

   (2009) Name-ethnicity classification from open sources

   the 15th ACM SIGKDD international conference. pp. 49–58.

   https://doi.org/10.1145/1557019.1557032

   • Google Scholar
3. 1. Andrews-Hanna JR
   2. Reidler JS
   3. Sepulcre J
   4. Poulin R
   5. Buckner RL

   (2010) Functional-anatomic fractionation of the brain’s default network

   Neuron 65:550–562.

   https://doi.org/10.1016/j.neuron.2010.02.005

   • PubMed
   • Google Scholar
4. 1. Baldassano C
   2. Chen J
   3. Zadbood A
   4. Pillow JW
   5. Hasson U
   6. Norman KA

   (2017) Discovering Event Structure in Continuous Narrative Perception and Memory

   Neuron 95:709–721.

   https://doi.org/10.1016/j.neuron.2017.06.041

   • PubMed
   • Google Scholar
5. 1. Ben-Yakov A
   2. Dudai Y

   (2011) Constructing realistic engrams: poststimulus activity of hippocampus and dorsal striatum predicts subsequent episodic memory

   The Journal of Neuroscience 31:9032–9042.

   https://doi.org/10.1523/JNEUROSCI.0702-11.2011

   • PubMed
   • Google Scholar
6. 1. Ben-Yakov A
   2. Eshel N
   3. Dudai Y

   (2013) Hippocampal immediate poststimulus activity in the encoding of consecutive naturalistic episodes

   Journal of Experimental Psychology. General 142:1255–1263.

   https://doi.org/10.1037/a0033558

   • PubMed
   • Google Scholar
7. 1. Ben-Yakov A
   2. Henson RN

   (2018) The Hippocampal Film Editor: Sensitivity and Specificity to Event Boundaries in Continuous Experience

   The Journal of Neuroscience 38:10057–10068.

   https://doi.org/10.1523/JNEUROSCI.0524-18.2018

   • PubMed
   • Google Scholar
8. 1. Bonnici HM
   2. Richter FR
   3. Yazar Y
   4. Simons JS

   (2016) Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

   The Journal of Neuroscience 36:5462–5471.

   https://doi.org/10.1523/JNEUROSCI.4310-15.2016

   • PubMed
   • Google Scholar
9. 1. Braga RM
   2. Buckner RL

   (2017) Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity

   Neuron 95:457–471.

   https://doi.org/10.1016/j.neuron.2017.06.038

   • PubMed
   • Google Scholar
10. 1. Brunec IK
    2. Moscovitch M
    3. Barense MD

    (2018) Boundaries Shape Cognitive Representations of Spaces and Events

    Trends in Cognitive Sciences 22:637–650.

    https://doi.org/10.1016/j.tics.2018.03.013

    • PubMed
    • Google Scholar
11. 1. Buckner RL
    2. DiNicola LM

    (2019) The brain’s default network: updated anatomy, physiology and evolving insights

    Nature Reviews. Neuroscience 20:593–608.

    https://doi.org/10.1038/s41583-019-0212-7

    • PubMed
    • Google Scholar
12. 1. Bulkin DA
    2. Sinclair DG
    3. Law LM
    4. Smith DM

    (2020) Hippocampal state transitions at the boundaries between trial epochs

    Hippocampus 30:582–595.

    https://doi.org/10.1002/hipo.23180

    • PubMed
    • Google Scholar
13. 1. Caplar N
    2. Tacchella S
    3. Birrer S

    (2017) Quantitative evaluation of gender bias in astronomical publications from citation counts

    Nature Astronomy 1:1–5.

    https://doi.org/10.1038/s41550-017-0141

    • Google Scholar
14. Preprint

    1. Chang CHC
    2. Nastase SA
    3. Hasson U

    (2021) Information Flow across the Cortical Timescales Hierarchy during Narrative Construction

    bioRxiv.

    https://doi.org/10.1101/2021.12.01.470825

    • Google Scholar
15. 1. Chen J
    2. Leong YC
    3. Honey CJ
    4. Yong CH
    5. Norman KA
    6. Hasson U

    (2017) Shared memories reveal shared structure in neural activity across individuals

    Nature Neuroscience 20:115–125.

    https://doi.org/10.1038/nn.4450

    • PubMed
    • Google Scholar
16. 1. Christoff K
    2. Gordon AM
    3. Smallwood J
    4. Smith R
    5. Schooler JW

    (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering

    PNAS 106:8719–8724.

    https://doi.org/10.1073/pnas.0900234106

    • PubMed
    • Google Scholar
17. 1. Christoff K
    2. Irving ZC
    3. Fox KCR
    4. Spreng RN
    5. Andrews-Hanna JR

    (2016) Mind-wandering as spontaneous thought: A dynamic framework

    Nature Reviews. Neuroscience 17:718–731.

    https://doi.org/10.1038/nrn.2016.113

    • PubMed
    • Google Scholar
18. 1. Clewett D
    2. DuBrow S
    3. Davachi L

    (2019) Transcending time in the brain: How event memories are constructed from experience

    Hippocampus 29:162–183.

    https://doi.org/10.1002/hipo.23074

    • PubMed
    • Google Scholar
19. 1. Crittenden BM
    2. Mitchell DJ
    3. Duncan J

    (2015) Recruitment of the default mode network during a demanding act of executive control

    eLife 4:e06481.

    https://doi.org/10.7554/eLife.06481

    • PubMed
    • Google Scholar
20. 1. deBettencourt MT
    2. Norman KA
    3. Turk-Browne NB

    (2018) Forgetting from lapses of sustained attention

    Psychonomic Bulletin & Review 25:605–611.

    https://doi.org/10.3758/s13423-017-1309-5

    • PubMed
    • Google Scholar
21. 1. Dion ML
    2. Sumner JL
    3. Mitchell SM

    (2018) Gendered Citation Patterns across Political Science and Social Science Methodology Fields

    Political Analysis 26:312–327.

    https://doi.org/10.1017/pan.2018.12

    • Google Scholar
22. 1. DuBrow S
    2. Davachi L

    (2013) The influence of context boundaries on memory for the sequential order of events

    Journal of Experimental Psychology. General 142:1277–1286.

    https://doi.org/10.1037/a0034024

    • PubMed
    • Google Scholar
23. 1. DuBrow S
    2. Rouhani N
    3. Niv Y
    4. Norman KA

    (2017) Does mental context drift or shift?

    Current Opinion in Behavioral Sciences 17:141–146.

    https://doi.org/10.1016/j.cobeha.2017.08.003

    • PubMed
    • Google Scholar
24. 1. Dworkin JD
    2. Linn KA
    3. Teich EG
    4. Zurn P
    5. Shinohara RT
    6. Bassett DS

    (2020) The extent and drivers of gender imbalance in neuroscience reference lists

    Nature Neuroscience 23:918–926.

    https://doi.org/10.1038/s41593-020-0658-y

    • PubMed
    • Google Scholar
25. 1. Esterman M
    2. Rosenberg MD
    3. Noonan SK

    (2014) Intrinsic fluctuations in sustained attention and distractor processing

    The Journal of Neuroscience 34:1724–1730.

    https://doi.org/10.1523/JNEUROSCI.2658-13.2014

    • PubMed
    • Google Scholar
26. 1. Fox MD
    2. Snyder AZ
    3. Barch DM
    4. Gusnard DA
    5. Raichle ME

    (2005) Transient BOLD responses at block transitions

    NeuroImage 28:956–966.

    https://doi.org/10.1016/j.neuroimage.2005.06.025

    • PubMed
    • Google Scholar
27. Preprint

    1. Geerligs L
    2. Gözükara D
    3. Oetringer D
    4. Campbell K
    5. van Gerven M
    6. Güçlü U

    (2021) A Partially Nested Cortical Hierarchy of Neural States Underlies Event Segmentation in the Human Brain

    bioRxiv.

    https://doi.org/10.1101/2021.02.05.429165

    • Google Scholar
28. 1. Hasselmo ME

    (1995) Neuromodulation and cortical function: modeling the physiological basis of behavior

    Behavioural Brain Research 67:1–27.

    https://doi.org/10.1016/0166-4328(94)00113-t

    • PubMed
    • Google Scholar
29. 1. Hasson U
    2. Chen J
    3. Honey CJ

    (2015) Hierarchical process memory: memory as an integral component of information processing

    Trends in Cognitive Sciences 19:304–313.

    https://doi.org/10.1016/j.tics.2015.04.006

    • PubMed
    • Google Scholar
30. 1. Honey CJ
    2. Newman EL
    3. Schapiro AC

    (2018) Switching between internal and external modes: A multiscale learning principle

    Network Neuroscience (Cambridge, Mass.) 1:339–356.

    https://doi.org/10.1162/NETN_a_00024

    • PubMed
    • Google Scholar
31. Preprint

    1. Jayakumar M
    2. Balusu C
    3. Aly M

    (2022) Attentional Fluctuations and the Temporal Organization of Memory

    PsyArXiv.

    https://doi.org/10.31234/osf.io/j32bn

    • Google Scholar
32. 1. Karapanagiotidis T
    2. Vidaurre D
    3. Quinn AJ
    4. Vatansever D
    5. Poerio GL
    6. Turnbull A
    7. Ho NSP
    8. Leech R
    9. Bernhardt BC
    10. Jefferies E
    11. Margulies DS
    12. Nichols TE
    13. Woolrich MW
    14. Smallwood J

    (2020) The psychological correlates of distinct neural states occurring during wakeful rest

    Scientific Reports 10:21121.

    https://doi.org/10.1038/s41598-020-77336-z

    • PubMed
    • Google Scholar
33. Preprint

    1. Lee H
    2. Chen J

    (2021) Predicting Memory from the Network Structure of Naturalistic Events

    bioRxiv.

    https://doi.org/10.1101/2021.04.24.441287

    • Google Scholar
34. 1. Maliniak D
    2. Powers R
    3. Walter BF

    (2013) The Gender Citation Gap in International Relations

    International Organization 67:889–922.

    https://doi.org/10.1017/S0020818313000209

    • Google Scholar
35. 1. Manning JR
    2. Hulbert JC
    3. Williams J
    4. Piloto L
    5. Sahakyan L
    6. Norman KA

    (2016) A neural signature of contextually mediated intentional forgetting

    Psychonomic Bulletin & Review 23:1534–1542.

    https://doi.org/10.3758/s13423-016-1024-7

    • PubMed
    • Google Scholar
36. 1. Medvedeva A
    2. Saw R
    3. Silvestri C
    4. Sirota M
    5. Fuggetta G
    6. Galli G

    (2021) Offset-related brain activity in the left ventrolateral prefrontal cortex promotes long-term memory formation of verbal events

    Brain Stimulation 14:564–570.

    https://doi.org/10.1016/j.brs.2021.03.002

    • PubMed
    • Google Scholar
37. 1. Mildner JN
    2. Tamir DI

    (2019) Spontaneous Thought as an Unconstrained Memory Process

    Trends in Neurosciences 42:763–777.

    https://doi.org/10.1016/j.tins.2019.09.001

    • PubMed
    • Google Scholar
38. 1. Mitchell SM
    2. Lange S
    3. Brus H

    (2013) Gendered Citation Patterns in International Relations Journals

    International Studies Perspectives 14:485–492.

    https://doi.org/10.1111/insp.12026

    • Google Scholar
39. 1. Mittner M
    2. Hawkins GE
    3. Boekel W
    4. Forstmann BU

    (2016) A Neural Model of Mind Wandering

    Trends in Cognitive Sciences 20:570–578.

    https://doi.org/10.1016/j.tics.2016.06.004

    • PubMed
    • Google Scholar
40. Preprint

    1. Mortaheb S
    2. Van Calster L
    3. Raimondo F
    4. Klados MA
    5. Boulakis PA
    6. Georgoula K
    7. Majerus S
    8. Van De Ville D
    9. Demertzi A

    (2021) Mind Blanking Is a Distinct Mental State Linked to a Recurrent Brain Profile of Globally Positive Connectivity during Ongoing Mentation

    bioRxiv.

    https://doi.org/10.1101/2021.05.10.443428

    • Google Scholar
41. 1. Pettijohn KA
    2. Radvansky GA

    (2016) Narrative event boundaries, reading times, and expectation

    Memory & Cognition 44:1064–1075.

    https://doi.org/10.3758/s13421-016-0619-6

    • PubMed
    • Google Scholar
42. 1. Pradhan R
    2. Kumar D

    (2021) Event segmentation and event boundary advantage: Role of attention and postencoding processing

    Journal of Experimental Psychology 10:1155.

    https://doi.org/10.1037/xge0001155

    • Google Scholar
43. 1. Radvansky GA
    2. Copeland DE

    (2006) Walking through doorways causes forgetting: situation models and experienced space

    Memory & Cognition 34:1150–1156.

    https://doi.org/10.3758/bf03193261

    • PubMed
    • Google Scholar
44. 1. Raffaelli Q
    2. Mills C
    3. de Stefano N-A
    4. Mehl MR
    5. Chambers K
    6. Fitzgerald SA
    7. Wilcox R
    8. Christoff K
    9. Andrews ES
    10. Grilli MD
    11. O’Connor M-F
    12. Andrews-Hanna JR

    (2021) The think aloud paradigm reveals differences in the content, dynamics and conceptual scope of resting state thought in trait brooding

    Scientific Reports 11:19362.

    https://doi.org/10.1038/s41598-021-98138-x

    • PubMed
    • Google Scholar
45. 1. Ramanan S
    2. Piguet O
    3. Irish M

    (2018) Rethinking the Role of the Angular Gyrus in Remembering the Past and Imagining the Future: The Contextual Integration Model

    The Neuroscientist 24:342–352.

    https://doi.org/10.1177/1073858417735514

    • PubMed
    • Google Scholar
46. 1. Ranganath C
    2. Ritchey M

    (2012) Two cortical systems for memory-guided behaviour

    Nature Reviews. Neuroscience 13:713–726.

    https://doi.org/10.1038/nrn3338

    • PubMed
    • Google Scholar
47. 1. Reagh ZM
    2. Delarazan AI
    3. Garber A
    4. Ranganath C

    (2020) Aging alters neural activity at event boundaries in the hippocampus and Posterior Medial network

    Nature Communications 11:3980.

    https://doi.org/10.1038/s41467-020-17713-4

    • PubMed
    • Google Scholar
48. 1. Ritchey M
    2. Cooper RA

    (2020) Deconstructing the Posterior Medial Episodic Network

    Trends in Cognitive Sciences 24:451–465.

    https://doi.org/10.1016/j.tics.2020.03.006

    • PubMed
    • Google Scholar
49. 1. Schaefer A
    2. Kong R
    3. Gordon EM
    4. Laumann TO
    5. Zuo XN
    6. Holmes AJ
    7. Eickhoff SB
    8. Yeo BTT

    (2018) Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

    Cerebral Cortex (New York, N.Y 28:3095–3114.

    https://doi.org/10.1093/cercor/bhx179

    • PubMed
    • Google Scholar
50. 1. Schapiro AC
    2. Rogers TT
    3. Cordova NI
    4. Turk-Browne NB
    5. Botvinick MM

    (2013) Neural representations of events arise from temporal community structure

    Nature Neuroscience 16:486–492.

    https://doi.org/10.1038/nn.3331

    • PubMed
    • Google Scholar
51. 1. Shin YS
    2. DuBrow S

    (2021) Structuring Memory Through Inference-Based Event Segmentation

    Topics in Cognitive Science 13:106–127.

    https://doi.org/10.1111/tops.12505

    • PubMed
    • Google Scholar
52. 1. Smallwood J
    2. Schooler JW

    (2015) The science of mind wandering: empirically navigating the stream of consciousness

    Annual Review of Psychology 66:487–518.

    https://doi.org/10.1146/annurev-psych-010814-015331

    • PubMed
    • Google Scholar
53. 1. Smallwood J
    2. Bernhardt BC
    3. Leech R
    4. Bzdok D
    5. Jefferies E
    6. Margulies DS

    (2021) The default mode network in cognition: a topographical perspective

    Nature Reviews. Neuroscience 22:503–513.

    https://doi.org/10.1038/s41583-021-00474-4

    • PubMed
    • Google Scholar
54. 1. Smith V
    2. Mitchell DJ
    3. Duncan J

    (2018) Role of the Default Mode Network in Cognitive Transitions

    Cerebral Cortex (New York, N.Y 28:3685–3696.

    https://doi.org/10.1093/cercor/bhy167

    • PubMed
    • Google Scholar
55. 1. Song H
    2. Finn ES
    3. Rosenberg MD

    (2021a) Neural signatures of attentional engagement during narratives and its consequences for event memory

    PNAS 118:e2021905118.

    https://doi.org/10.1073/pnas.2021905118

    • PubMed
    • Google Scholar
56. 1. Song H
    2. Park BY
    3. Park H
    4. Shim WM

    (2021b) Cognitive and Neural State Dynamics of Narrative Comprehension

    The Journal of Neuroscience 41:8972–8990.

    https://doi.org/10.1523/JNEUROSCI.0037-21.2021

    • PubMed
    • Google Scholar
57. Preprint

    1. Sood G
    2. Laohaprapanon S

    (2018) Predicting Race and Ethnicity from the Sequence of Characters in a Name

    arXiv.

    https://arxiv.org/abs/1805.02109

    • Google Scholar
58. 1. Speer NK
    2. Zacks JM
    3. Reynolds JR

    (2007) Human brain activity time-locked to narrative event boundaries

    Psychological Science 18:449–455.

    https://doi.org/10.1111/j.1467-9280.2007.01920.x

    • PubMed
    • Google Scholar
59. 1. Sripada C
    2. Taxali A

    (2020) Structure in the stream of consciousness: Evidence from a verbalized thought protocol and automated text analytic methods

    Consciousness and Cognition 85:103007.

    https://doi.org/10.1016/j.concog.2020.103007

    • Google Scholar
60. 1. Stawarczyk D
    2. Bezdek MA
    3. Zacks JM

    (2021) Event Representations and Predictive Processing: The Role of the Midline Default Network Core

    Topics in Cognitive Science 13:164–186.

    https://doi.org/10.1111/tops.12450

    • PubMed
    • Google Scholar
61. 1. Tseng J
    2. Poppenk J

    (2020) Brain meta-state transitions demarcate thoughts across task contexts exposing the mental noise of trait neuroticism

    Nature Communications 11:3480.

    https://doi.org/10.1038/s41467-020-17255-9

    • PubMed
    • Google Scholar
62. 1. Vincent JL
    2. Kahn I
    3. Snyder AZ
    4. Raichle ME
    5. Buckner RL

    (2008) Evidence for a frontoparietal control system revealed by intrinsic functional connectivity

    Journal of Neurophysiology 100:3328–3342.

    https://doi.org/10.1152/jn.90355.2008

    • PubMed
    • Google Scholar
63. 1. Ward AF
    2. Wegner DM

    (2013) Mind-blanking: when the mind goes away

    Frontiers in Psychology 4:650.

    https://doi.org/10.3389/fpsyg.2013.00650

    • PubMed
    • Google Scholar
64. 1. Wen T
    2. Duncan J
    3. Mitchell DJ

    (2020) Hierarchical Representation of Multistep Tasks in Multiple-Demand and Default Mode Networks

    The Journal of Neuroscience 40:7724–7738.

    https://doi.org/10.1523/JNEUROSCI.0594-20.2020

    • PubMed
    • Google Scholar
65. 1. Yeshurun Y
    2. Nguyen M
    3. Hasson U

    (2021) The default mode network: where the idiosyncratic self meets the shared social world

    Nature Reviews. Neuroscience 22:181–192.

    https://doi.org/10.1038/s41583-020-00420-w

    • PubMed
    • Google Scholar
66. 1. Zacks JM
    2. Braver TS
    3. Sheridan MA
    4. Donaldson DI
    5. Snyder AZ
    6. Ollinger JM
    7. Buckner RL
    8. Raichle ME

    (2001) Human brain activity time-locked to perceptual event boundaries

    Nature Neuroscience 4:651–655.

    https://doi.org/10.1038/88486

    • PubMed
    • Google Scholar
67. 1. Zacks JM
    2. Speer NK
    3. Swallow KM
    4. Braver TS
    5. Reynolds JR

    (2007) Event perception: A mind-brain perspective

    Psychological Bulletin 133:273–293.

    https://doi.org/10.1037/0033-2909.133.2.273

    • PubMed
    • Google Scholar
68. 1. Zacks JM
    2. Speer NK
    3. Swallow KM
    4. Maley CJ

    (2010) The Brain’s Cutting-Room Floor: Segmentation of Narrative Cinema

    Frontiers in Human Neuroscience 4:168.

    https://doi.org/10.3389/fnhum.2010.00168

    • PubMed
    • Google Scholar
69. 1. Zacks JM
    2. Kurby CA
    3. Eisenberg ML
    4. Haroutunian N

    (2011) Prediction error associated with the perceptual segmentation of naturalistic events

    Journal of Cognitive Neuroscience 23:4057–4066.

    https://doi.org/10.1162/jocn_a_00078

    • PubMed
    • Google Scholar
70. 1. Zacks JM

    (2020) Event Perception and Memory

    Annual Review of Psychology 71:165–191.

    https://doi.org/10.1146/annurev-psych-010419-051101

    • PubMed
    • Google Scholar
71. 1. Zheng J
    2. Schjetnan AGP
    3. Yebra M
    4. Gomes BA
    5. Mosher CP
    6. Kalia SK
    7. Valiante TA
    8. Mamelak AN
    9. Kreiman G
    10. Rutishauser U

    (2022) Neurons detect cognitive boundaries to structure episodic memories in humans

    Nature Neuroscience 25:358–368.

    https://doi.org/10.1038/s41593-022-01020-w

    • PubMed
    • Google Scholar
72. Software

    1. Zhou D
    2. Cornblath EJ
    3. Stiso J
    4. Teich EG
    5. Dworkin JD
    6. Blevins AS
    7. Bassett DS

    (2020) Gender diversity statement and code notebook v1.0

    Zenodo.

    https://doi.org/10.5281/zenodo.3672110

Author details

1. Hongmi Lee

   Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   hongmi.lee@jhu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8023-0727

2. Janice Chen

   Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States

   Contribution

   Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

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