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DANDI:000207

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Data for: Neurons detect cognitive boundaries to structure episodic memories in humans (Zheng et al., 2022, Nat Neuro in press)

electrophysiological Human 50.0 MB 19 files
  • 5 reproduced
  • 1 partially reproduced
  • 14 blocked

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Claims

From the paper Neurons detect cognitive boundaries to structure episodic memories in humans

Reproducing Zheng et al. (2022) from dandiset 000207 9

Notebook reproduce_zheng_2022.ipynb

  • Partially reproducedFigure 5a-d; Results, Neural state shifts across boundaries reflect memory strength, paragraph 1

    The state of the population of all medial temporal lobe neurons changed abruptly in the first 300 ms after soft and hard boundaries, and only slowly in clips with no boundary, measured as the distance in principal-component space from the state at the boundary.

    During NB video clips, the neural state exhibited only slow changes as a function of time ... In contrast, the neural state changed abruptly following SBs and HBs ... Plotting MDD as a function of time revealed an abrupt change within ~300 ms after the boundary for SB and HB video clips

    Figure 5a-d; Results, Neural state shifts across boundaries reflect memory strength, paragraph 1

    After a hard boundary the distance from the state at the boundary is larger than in clips with no boundary (9.03 against 7.42, p = 0.0001) and larger than before the boundary (after minus before 0.84 against 0.04, p = 0.004); 12 of 18 subjects show it alone. After a soft boundary the distance is larger than without a boundary (8.15, p = 0.003) but equally large before the boundary (after minus before 0.00, p = 0.55). That the shift completes within 300 ms was not shown.

    How the test differed from the paper’s method

    • 549 units from 18 sessions against the paper's 580 from 19.
    • Pseudo-trials are matched across subjects by clip name; the paper does not say how.
    • Principal components are fitted on the three condition averages from 1 s before to 1.5 s after the boundary, giving 22 components for 99% of the variance against the paper's 55. The averages are of the same trials that are then compared.
    • A trial is z-scored from 1 s before to 8 s after clip onset; the paper says "the entire trial".
    • Clips with soft boundaries are aligned to their first soft boundary, one pseudo-trial per clip. The authors' published code does not cover this analysis.
    • The comparison with the second before the boundary is this attempt's; the paper shows curves.
    • Two SB clips have their first soft boundary less than 1 s after onset. Leaving them out gives the same result (SB after minus before -0.03, p = 0.59; HB 0.84, p = 0.003).
  • ReproducedFigure 2a; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    In the time discrimination task, subjects chose the frame that was shown first in 73% of trials when the two frames were separated by no boundary or by a soft boundary, and in 53% of trials, significantly fewer, when they were separated by a hard boundary.

    subjects correctly identified which frame was shown first in 73 ± 7% and 73 ± 8% of trials when the two frames were separated by no boundary (NB) or a soft boundary (SB) ... subjects performed significantly worse when discriminating the order of frames separated by a hard boundary (HB) (Fig. 2a, red, HB: 53 ± 5%

    Figure 2a; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    What the paper reports

    • F (2, 57) = 51.33, p = 2 × 10⁻¹³

    Time discrimination accuracy is 0.528 across a hard boundary, 0.728 with no boundary and 0.720 with a soft boundary (paper 0.53, 0.73, 0.73). It is lower across a hard boundary in 18 of 19 subjects (paired p < 1e-6 against both).

    How the test differed from the paper’s method

    • 19 of the paper's 20 subjects; the twentieth is not in the dandiset.
    • Trials without a response are kept as coded in the file; leaving them out moves no mean by more than 0.01.
    • Paired tests over subjects are added to the paper's one-way ANOVA.
  • ReproducedFigure 2b; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    Reaction times in the time discrimination task were longer when the two frames were separated by a hard boundary (2.10 s) than by no boundary (1.62 s) or a soft boundary (1.59 s).

    Subjects also showed longer reaction times (Fig. 2b; HB: 2.10 ± 0.37 seconds; NB: 1.62 ± 0.28 seconds; SB: 1.59 ± 0.34 seconds; F (2, 57) = 14.25

    Figure 2b; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    What the paper reports

    • F (2, 57) = 14.25, p = 10⁻⁵

    Mean reaction time is 2.05 s across a hard boundary, 1.62 s with no boundary and 1.54 s with a soft boundary (paper 2.10, 1.62, 1.59 s), longer across a hard boundary in 18 of 19 subjects. The file's RT column is used, which runs from the disappearance of the frames.

    How the test differed from the paper’s method

    • 19 of the paper's 20 subjects.
    • Trials without a response have an RT of 0 in the file and are kept; leaving them out gives 2.08, 1.64 and 1.56 s.
  • ReproducedFigure 2c; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    Confidence ratings in the time discrimination task were lower when the two frames were separated by a hard boundary (1.95 on a scale of 1 to 3) than by no boundary (2.52) or a soft boundary (2.59).

    lower confidence ratings when discriminating the order of frames separated by a hard boundary (Fig. 2c; HB: 1.95 ± 0.45; NB: 2.52 ± 0.29; SB: 2.59 ± 0.23; F (2, 57) = 20.41

    Figure 2c; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 3

    What the paper reports

    • F (2, 57) = 20.41, p = 2 × 10⁻⁷

    Mean confidence is 1.98 across a hard boundary, 2.45 with no boundary and 2.51 with a soft boundary (paper 1.95, 2.52, 2.59), lower across a hard boundary in 18 of 19 subjects.

    How the test differed from the paper’s method

    • 19 of the paper's 20 subjects.
    • Trials without a response have a confidence of 0 in the file and are kept; leaving them out gives 2.00, 2.48 and 2.55.
  • ReproducedExtended Data Figure 2a; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 4

    Scene recognition accuracy did not differ between frames from clips with no boundary (76%), a soft boundary (75%) and a hard boundary (75%).

    the ability to recognize a frame as ‘old’ did not differ significantly between the types of boundaries preceding the frame (Extended Data Fig. 2a; NB: 76% ± 10%; SB: 75% ± 9%; HB: 75% ± 8%, F (2, 57) = 0.07, p = 0.94).

    Extended Data Figure 2a; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 4

    What the paper reports

    • F (2, 57) = 0.07, p = 0.94

    Scene recognition accuracy over targets and foils is 0.738, 0.739 and 0.718 for clips with no, a soft and a hard boundary (paper 0.76, 0.75, 0.75), with no difference in a one-way ANOVA (F(2, 54) = 0.38, p = 0.69). Accuracy for hard-boundary clips is the lowest in every variant, by 0.01 to 0.05, without reaching significance.

    How the test differed from the paper’s method

    • 19 of the paper's 20 subjects.
    • The paper does not say whether foils are counted. Targets and foils are; targets alone give 0.737, 0.718 and 0.688 (p = 0.59).
  • ReproducedExtended Data Figure 5; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 3

    76 of 580 medial temporal lobe neurons (13.1%) changed their firing at clip onsets and 4 of 580 (0.7%) at clip offsets, and none of them was a boundary cell or an event cell.

    Seventy six of 580 (13.1%, p = 0.01) and four of 580 (0.7%, p = 0.43) MTL cells changed their firing rate in response to clip onsets and clip offsets, respectively, but neither of these cells qualified as boundary cells or event cells (Extended Data Fig. 5).

    Extended Data Figure 5; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 3

    What the paper reports

    • clip onsets: p = 0.01
    • clip offsets: p = 0.43

    98 of 549 units (17.9%) fire more after clip onset, against 13.1% in the paper; the gap of 4.8 points is just inside the tolerance of 5, and 98 is more than a proportion of 13.1% would plausibly give (p = 9e-4). 26 units (4.7%) fire more after clip offset, the chance level, against 0.7% in the paper. Seven sessions hold 82 of the 98 onset-responsive units; the other eleven have 16 of 359 (4.5%), about chance, so the overall proportion averages over sessions that differ widely. The spike clock is shown to be aligned for ten of those eleven; for subject 3 (1 of 31 units responsive) it could not be established. None of the 8 boundary and event cells found responds to onsets or offsets, which is too few to judge that part of the claim.

    How the test differed from the paper’s method

    • 549 units from 18 sessions against the paper's 580 from 19.
    • The paper's one-tailed test does not state its direction; an increase is tested. A decrease is found in 3.1% of units at onset.
    • The permutation test is a sign-flip test with 1000 permutations.
  • BlockedFigure 3a-c; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 1; Methods, Boundary cell· Methods underspecifiedverifier disagreed at first

    42 of 580 medial temporal lobe neurons (7.24%) were boundary cells, which changed their firing in the second after soft and hard boundaries but not in the middle of clips with no boundary; 2.11% were expected by chance.

    42/580 MTL neurons (7.24%; expected proportion by chance for all MTL neurons= 2.11%) were classified as boundary cells (Fig. 3c).

    Figure 3a-c; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 1; Methods, Boundary cell

    The analysis was run, but the procedure behind the paper's count could not be determined. 1 of 549 units (0.18%) meets the Methods' definition of a boundary cell, against 42 of 580 (7.24%) in the paper. The paper's chance level is 2.11%; the literal definition gives 0.03% here, alpha = 0.10 gives 0.10% and a looser pooled reading 0.55%, and the verifier found in simulation that the five-test definition stays at or below about 1% for every response profile it tried. No reading tried here produces the paper's chance level, so the paper's count came from a different procedure. Under the pooled reading there are 15 boundary cells (2.73%), more than in any of 300 shuffles. The authors' published code contains no cell selection. Recorded as not_reproduced before verification.

    How the test differed from the paper’s method

    • 549 units from 18 sessions against the paper's 580 from 19. Subject 19 is left out because its spike times are not on the clock of the trial tables, and the dandiset's units differ from the paper's in number and in their split by region.
    • The paper's "permutation t-test" is implemented as a sign-flip test for before against after and a one-sided label-permutation test between conditions, with 1000 permutations.
    • Soft-boundary trials are every soft boundary of a session, in SB clips and in HB clips (75 per session). The Methods do not say; this follows the authors' published plotting code (B01_raster_psth_encoding_BAligned_BSep_NWB.m, lines 29 to 37). The first soft boundary of each SB clip, used before that code was read, is kept in the notebook as an alternative.
    • Each condition is tested on its own, five tests per class, which is the literal reading of the Methods. The authors' published code has no cell selection to check this against.
    • 300 label shuffles for the chance level instead of 1000.
  • BlockedFigure 3e-g; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 3; Methods, Event cell· Methods underspecifiedverifier disagreed at first

    36 of 580 medial temporal lobe neurons (6.20%) were event cells, which changed their firing in the second after hard boundaries but not after soft boundaries or in the middle of clips with no boundary; 2.26% were expected by chance.

    36/580 MTL neurons (6.20%; proportion expected by chance for all MTL neurons = 2.26%) were classified as event cells (Fig. 3g).

    Figure 3e-g; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 3; Methods, Event cell

    As fig3c-boundary-cells. 7 of 549 units (1.28%) meet the Methods' definition of an event cell, more than in any of 300 label shuffles (mean 1.22, maximum 6), but about a fifth of the paper's 36 of 580 (6.20%). At alpha = 0.10 there are 12 (2.19%, above all 300 shuffles) and under the pooled reading 10 (1.82%, 4 of 300 shuffles as many). The chance levels of these readings are 0.22%, 0.72% and 0.65%, against the paper's 2.26%. Recorded as partially_reproduced before verification.

    How the test differed from the paper’s method

    • The same as for fig3c-boundary-cells.
  • BlockedResults, Medial temporal lobe neurons demarcate episodic transitions, paragraphs 1 and 3; Supplementary Table 4· Methods underspecifiedverifier disagreed at first

    Boundary cells were most common in the parahippocampal gyrus (18 of 68 neurons, 26%, against 7% in the amygdala and 3.5% in the hippocampus), and event cells in the hippocampus (27 of 343, 7.9%, against 4.3% in the amygdala and 2.9% in the parahippocampal gyrus).

    The regions with the largest proportion of boundary cells were the parahippocampal gyrus (n= 18/68, 26.47%) ... The regions with the largest proportion of event cells were the hippocampus (n= 27/343, 7.87%)

    Results, Medial temporal lobe neurons demarcate episodic transitions, paragraphs 1 and 3; Supplementary Table 4

    Depends on the cell selections of fig3c-boundary-cells and fig3g-event-cells, whose procedure could not be determined. With the literal definition there is no boundary cell among 53 parahippocampal units (paper 18 of 68), and event cells are 2 of 267 in the hippocampus, 3 of 229 in the amygdala and 2 of 53 in the parahippocampal gyrus. The dandiset's split of units by region also differs from the paper's. Recorded as not_reproduced before verification.

    How the test differed from the paper’s method

    • The same as for fig3c-boundary-cells.

Not tested in a notebook 11

8 metadata insufficient, 3 data missing from the dataset
  • BlockedFigure 2d-f; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 4· Metadata insufficient

    Recognition accuracy for a target frame fell with the time between the frame and the boundary before it, for soft boundaries (r = -0.61) and hard boundaries (r = -0.44), but not for clips with no boundary (r = 0.085).

    Target frames presented shortly after a SB and HB were remembered better than those farther away from the boundary (Fig. 2e and 2f; SB: r = −0.61 ... recognition accuracy did not differ significantly as a function of time relative to NBs (Fig. 2d, Pearson correlation; NB: r = 0.085, p = 0.65).

    Figure 2d-f; Results, Boundaries boost recognition but disrupt serial order memory, paragraph 4

    What the paper reports

    • soft boundaries: Pearson correlation, r = -0.61, p = 4 × 10⁻⁴
    • hard boundaries: Pearson correlation, r = -0.44, p = 0.015
    • no boundary: Pearson correlation, r = 0.085, p = 0.65

    The dandiset does not give the position of a tested frame within its clip. Its name says frameA or frameB, which by the paper's Methods (one frame from each half) is probably the first or the second half; no column states it. For clips with soft or hard boundaries the frame names also do not identify the clip.

  • BlockedFigure 3d,h; Extended Data Figures 3 and 4; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraphs 2 and 3· Metadata insufficient

    Boundary cells and event cells did not change their firing at clip onsets or offsets during encoding, nor at image onsets or offsets in the two memory tests.

    Boundary cells did not respond significantly to either clip or image onsets or offsets (p > 0.05; permutation t-test, Methods) ... event cells did not significantly change their firing rates following video clip onsets or offsets (Fig. 3h and Extended Data Fig. 3) during encoding, nor did they respond to image onsets or offsets

    Figure 3d,h; Extended Data Figures 3 and 4; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraphs 2 and 3

    What the paper reports

    • boundary cells: permutation t-test, p > 0.05
    • event cells: permutation t-test, p > 0.05

    The dandiset does not say which units the paper classified as boundary or event cells, and the Methods' definitions select 1 and 7 units here, fewer than the 10 of each that the claim's criterion requires. The authors' published code does not select or list the cells. The notebook reports that none of the 8 responds to clip onsets or offsets.

  • BlockedFigure 3i-j; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 6· Metadata insufficient

    Boundary cells responded to hard boundaries about 100 ms earlier than event cells; their firing peaked 197 ms after the boundary, against 301 ms for event cells.

    boundary cells responded to hard boundaries approximately 100 milliseconds earlier than event cells (Fig. 3i) ... boundary cells showed a peak at 197 ± 49 milliseconds, whereas event cells showed a peak at 301 ± 55 milliseconds (Fig. 3j, F (1, 76) = 274.78

    Figure 3i-j; Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 6

    What the paper reports

    • F (1, 76) = 274.78, p = 6 × 10⁻²⁷

    Needs the paper's boundary and event cells, which the dandiset does not label; 1 and 7 are found here, fewer than the 10 of each that the claim's criterion requires.

  • BlockedResults, Medial temporal lobe neurons demarcate episodic transitions, paragraph 7; Supplementary Tables 2 and 4· Data missing from the dataset

    Of 405 neurons recorded outside the medial temporal lobe, 8 were boundary cells and 9 were event cells, so boundary responses are largely confined to the medial temporal lobe.

    We found 8/405 (1.96%) boundary cells and 9/405 (2.22%) event cells among the non-MTL group ... Thus, boundary responsive neurons are largely within the MTL

    Results, Medial temporal lobe neurons demarcate episodic transitions, paragraph 7; Supplementary Tables 2 and 4

    All 565 units in the dandiset are in the hippocampus, amygdala or parahippocampal gyrus. The paper's 405 units from other areas are not included.

  • BlockedFigure 4a-c; Extended Data Figures 6 and 7; Results, Responses of boundary and event cells predict memory strength, paragraph 1· Metadata insufficient

    Boundary cells fired more after soft and hard boundaries during encoding when the frame that followed the boundary was later recognised than when it was forgotten; there was no such difference for clips with no boundary, for the time discrimination task, or for event cells.

    among boundary cells (n = 42) for frames preceded or followed by both SBs and HBs, but not by NBs ... the firing rate of boundary cells did not significantly predict performance in the time discrimination task ... the firing rate of event cells (n = 36) during encoding was not predictive of scene recognition memory

    Figure 4a-c; Extended Data Figures 6 and 7; Results, Responses of boundary and event cells predict memory strength, paragraph 1

    What the paper reports

    • boundary cells, scene recognition, soft boundaries (Fig. 4c): F (1, 82) = 82.93, p = 4 × 10⁻¹⁴
    • boundary cells, scene recognition, hard boundaries (Fig. 4c): F (1, 82) = 156.9, p = 10⁻²⁰
    • boundary cells, scene recognition, no boundary (Fig. 4c): F (1, 82) = 1.18, p = 0.28
    • boundary cells, time discrimination, no boundary: F (1, 82) = 1.25, p = 0.27
    • boundary cells, time discrimination, soft boundaries: F (1, 82) = 1.35, p = 0.25
    • boundary cells, time discrimination, hard boundaries: F (1, 82) = 1.14, p = 0.29
    • event cells, scene recognition, no boundary: F (1, 70) = 1.12, p = 0.29
    • event cells, scene recognition, soft boundaries: F (1, 70) = 1.63, p = 0.21
    • event cells, scene recognition, hard boundaries: F (1, 70) = 0.79, p = 0.38

    Scene recognition trials with soft or hard boundaries cannot be linked to the clip they test. Their frame names carry 10 distinct numbers, with up to 12 trials per number, where the clips are numbered 1 to 30. The authors' published code links nothing either; it groups test trials only by boundary type. The paper's boundary cells are not labelled.

  • BlockedFigure 4e-h; Results, Responses of boundary and event cells predict memory strength, paragraph 2· Data missing from the dataset

    Event cells' spikes were locked more strongly to the phase of the theta-band local field potential after soft and hard boundaries when the temporal order of the clip was later remembered than when it was not; boundary cells showed no such effect.

    The mean MRL across all event cells (n = 36) was significantly larger following both SB and HB but not NB if temporal order was later correctly remembered ... the strength of phase-locking of boundary cells neither predicted scene recognition memory success ... nor temporal order memory

    Figure 4e-h; Results, Responses of boundary and event cells predict memory strength, paragraph 2

    What the paper reports

    • event cells, soft boundaries: F (1, 70) = 81.55, p = 2 × 10⁻¹³
    • event cells, hard boundaries: F (1, 70) = 60.79, p = 4 × 10⁻¹¹
    • event cells, no boundary: F (1, 70) = 1.53, p = 0.22
    • boundary cells, temporal order, condition not labelled in the text: F (1, 82) = 1.33, p = 0.25
    • boundary cells, temporal order, soft boundaries: F (1, 82) = 0.14, p = 0.71
    • boundary cells, temporal order, hard boundaries: F (1, 82) = 1.98, p = 0.16

    The dandiset holds spike times only. There are no local field potentials from which to compute theta phase.

  • BlockedFigure 5i-j; Results, Neural state shifts across boundaries reflect memory strength, paragraph 3· Metadata insufficient

    The distance the population state travelled in the second after a boundary correlated positively with later recognition accuracy for soft (r = 0.65) and hard (r = 0.57) boundaries, and negatively with time discrimination accuracy for hard boundaries (r = -0.68).

    The AUC MDD was positively correlated with recognition accuracy for frames following SBs and HBs, but not NBs ... the AUC MDD was negatively correlated with accuracy in the time discrimination task for HBs, but not for NBs or SBs

    Figure 5i-j; Results, Neural state shifts across boundaries reflect memory strength, paragraph 3

    What the paper reports

    • recognition accuracy, soft boundaries: r = 0.653, p = 0.002
    • recognition accuracy, hard boundaries: r = 0.565, p = 0.009
    • time discrimination accuracy, hard boundaries: r = -0.677, p = 0.001

    Needs the memory outcome of each clip with a soft or hard boundary. Test trials of those conditions cannot be linked to clips, in either memory test, and the authors' published code contains no mapping.

  • BlockedFigure 6a-h; Results, Neural context after boundaries reinstates during recognition, paragraphs 1, 2 and 4· Metadata insufficient

    During scene recognition, population activity correlated with the activity during encoding shortly after the soft or hard boundary of the tested clip, for correctly recognised targets and for foils wrongly called old, but not for forgotten targets or correctly rejected foils.

    were accompanied by a significant positive correlation between neural activity during the scene recognition and the encoding period shortly after SB/HB transitions ... no significant correlation for forgotten targets ... no significant correlation for correctly identified foils was observed ... strong correlations ... also occurred when a new image was incorrectly classified as seen before

    Figure 6a-h; Results, Neural context after boundaries reinstates during recognition, paragraphs 1, 2 and 4

    What the paper reports

    • correct targets: permutation test, p < 0.01
    • incorrect foils: permutation test, p < 0.01

    Needs each scene recognition trial paired with the encoding of its clip. That is possible only for clips with no boundary, which are the control condition of the claim.

  • BlockedFigure 6i; Results, Neural context after boundaries reinstates during recognition, paragraph 2· Metadata insufficient

    The reinstated state was most similar to the state about 1.2 s after the boundary that preceded the tested frame, and 1.5 to 1.7 s before the tested frame itself was shown.

    The reinstated neural context during retrieval was most similar to the neural context present during encoding approximately ~1.2s after the boundary that preceded the tested frame ... the point of time of maximal similarity preceded the time at which the later tested frame was shown by 1–1.5s

    Figure 6i; Results, Neural context after boundaries reinstates during recognition, paragraph 2

    What the paper reports

    • time from the boundary, soft boundaries: t19 = 14.68, p = 8 × 10⁻¹²
    • time from the boundary, hard boundaries: t19 = 11.80, p = 3 × 10⁻¹⁰
    • time before the tested frame, soft boundaries: t19 = 11.18, p = 8 × 10⁻¹⁰
    • time before the tested frame, hard boundaries: t19 = 7.44, p = 5 × 10⁻⁷

    As fig6-reinstatement. The time of the tested frame within its clip is not given either.

  • BlockedFigure 6j-m; Results, Neural context after boundaries reinstates during recognition, paragraph 3· Metadata insufficient

    Reinstatement after soft and hard boundaries was stronger for old decisions made with high confidence than with low confidence.

    the correlations following boundary transitions were significantly stronger in high- compared to low-confidence trials ... This difference was observed during both correctly remembered targets and falsely recognized foils

    Figure 6j-m; Results, Neural context after boundaries reinstates during recognition, paragraph 3

    What the paper reports

    • soft boundaries, correct targets: t-test, p = 5 × 10⁻¹⁰
    • hard boundaries, correct targets: t-test, p = 4 × 10⁻⁶
    • soft boundaries, incorrect foils: t-test, p = 5 × 10⁻⁷
    • hard boundaries, incorrect foils: t-test, p = 5 × 10⁻⁵

    As fig6-reinstatement.

  • BlockedFigure 6n-o; Results, Neural context after boundaries reinstates during recognition, paragraph 4· Data missing from the dataset

    For foils wrongly called old, reinstatement was stronger when the foil was highly similar to its target; for correctly rejected foils it did not vary with similarity.

    Correlations between encoding and scene recognition were significantly stronger for highly similar foils falsely recognized as old compared to low and medium similarity foils ... the extent of correlation for correctly rejected foils (true negatives) did not vary as a function of similarity

    Figure 6n-o; Results, Neural context after boundaries reinstates during recognition, paragraph 4

    What the paper reports

    • foils wrongly called old: F (2, 54) = 10.67, p = 10⁻⁴
    • correctly rejected foils: F (2, 54) = 2.182, p = 0.144

    The similarity ratings of targets and foils, collected from a separate group of raters, are not in the dandiset. The trials could not be linked to clips either.

Findings about the data

What attempts found about the dataset itself, for its owner and the archive.

  • Metadata errorIn the most recent published version (0.230530.1822) the spike times of subjects 13, 17 and 18 are shifted by 191.5, -16.8 and 9.3 s against the encoding trials, which did not move, so spikes and clips no longer line up. The version the paper cites (0.220216.0323) does not have this shift.
  • Metadata errorThe spike times of subject 19 are near -7.02e12 s while its events run from 0 to 2892 s, in both versions read. Its 16 units cannot be aligned to the task.
  • Identifier missingScene recognition and time discrimination trials with soft or hard boundaries cannot be linked to the clips they test. Their frame names are numbered differently from the clips (10 distinct numbers in recognition, 1 to 60 in time discrimination, against clips 1 to 30). The position of a tested frame within its clip is not given.
  • Identifier missingThe dandiset does not say which units the paper classified as boundary cells or event cells, so the paper's selection cannot be compared with a new one cell by cell.
  • Data missingThe dandiset has no local field potentials, no units outside the medial temporal lobe (the paper has 405), no similarity ratings and no stimuli, and it lacks the behaviour of one of the paper's 20 subjects.
  • Selection not documentedThe dandiset has 565 medial temporal lobe units where the paper analyses 580, with a different split by region (hippocampus 279 against 343, amygdala 233 against 169, parahippocampal gyrus 53 against 68). Nothing says how the two sets relate.
  • Metadata errorSeveral column descriptions do not match the values. stimCategory holds 0, 1, 2 and is described as 1, 2, 3. A missing second or third boundary is stored as the clip's start time and described as 0. The accuracy code 0 for "no response" is never used; trials without a response are coded incorrect, or, in 20 time discrimination trials, correct. resp_value is 1 for those trials and described as -1. RT is measured from the disappearance of the frames, which its description does not say, and is negative in 151 time discrimination trials.
  • Metadata errorThe events series holds 43 entries with a code 0 that its description does not list, with timestamps as low as -7.0e12 s. The spike times have no stated unit.
  • Methods not availableThe paper's Methods do not specify the permutation test, which soft boundaries are used, or how trials are matched across subjects for the pseudopopulation. The definitions of boundary and event cells, read literally, give a chance level about seventy times lower than the paper reports.
  • Methods not availableThe code the dandiset links to (github.com/rutishauserlab/cogboundary-zheng, commit 60f7ece) is four scripts that plot rasters from the NWB files. It contains none of the paper's analyses, no cell selection, chance level, statistics, state-space or reinstatement analysis, and nothing that links a tested frame to its clip.
  • Metadata errorThe authors' plotting code drops missing boundaries by testing for a stored time above 0. In version 0.220216.0323 a missing boundary is stored as the clip's start time, so the code would keep 150 soft-boundary rows where 75 exist and its fixed row ranges for the three conditions would be wrong. In version 0.230530.1822 missing boundaries are NaN and the code works, but that version has the shifted spike times.
  • OtherMethods and published code disagree on the firing-rate traces. The Methods give a 200 ms window moved in 2 ms steps; the code uses 10 ms bins smoothed with a Gaussian window of 20 bins. The code shows 0.5 s before and 1 s after a boundary. It also treats every soft boundary, including those inside hard-boundary clips, as a soft-boundary trial (75 per session), which the Methods do not state.
  • OtherFour hard-boundary clips (HB_7, HB_11, HB_14, HB_15) drive strong population activity in the half second before the hard boundary; all contain a soft boundary between 2.75 and 3.40 s. This raises the baseline of a before-against-after test of the hard boundary.

Notebooks

Every registered entry that uses this dataset. An entry can hold several notebooks, and can use more than one dataset.

Reproduction Cognitive boundaries and human medial temporal lobe neurons (Zheng et al. 2022)

Tests the findings of Zheng et al. (2022) that can be tested on dandiset 000207, which holds spike times of medial temporal lobe neurons and the behaviour of 19 patients in a task with video clips that contain no boundary, a soft boundary or a hard boundary. It compares memory for temporal order and for scenes across the three kinds of clip, looks for boundary cells and event cells with the paper's definitions, counts neurons that respond to clip onsets, and measures how far the population state moves after a boundary. Eleven of the paper's twenty findings cannot be tested, because the dandiset has no local field potentials, no neurons outside the medial temporal lobe, no labels for the paper's cell classes, and no way to link test trials with soft or hard boundaries to the clips they came from. All files

Verification

  • Round 1
    2026-10-13    Agreed with 17 of 20 outcomes. Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook). Reviewed: Round 1, the notebook at commit 8284b56 executed, with the author's conclusions and summary withheld, the helper module and the claims file. Outcomes below are compared with those recorded before verification.
    • fig3c-boundary-cells: the verifier’s outcome was “Blocked”. Blocked by methods_underspecified. In simulation the five-test conjunction under label shuffling stayed at or below about 1% chance for every response profile tried, so the paper's 2.11% cannot come from the procedure as written, and failing to match 42 cells does not show the effect is absent.
    • fig3g-event-cells: the verifier’s outcome was “Blocked”. As fig3c-boundary-cells.
    • fig3-cell-regions: the verifier’s outcome was “Blocked”. Follows from fig3c-boundary-cells and fig3g-event-cells.
  • Round 2
    2026-10-13    Agreed with 20 of 20 outcomes. The same independent agent as round 1. Reviewed: Round 2, the notebook executed after the round-1 changes, with the conclusions and summary, the helper module, the entry README and this record. Its other findings were a header that still counted the outcomes from before verification, a summary sentence on chance levels that covered only four readings, and round-1 reasons recorded in words it had not used. All were corrected.

Method differences

How the tests differed from the paper’s method or data, and the claims each applies to.

  • 19 of the paper's 20 subjects.fig2b-order-reaction-time fig2c-order-confidence edfig2a-recognition-similar
  • The same as for fig3c-boundary-cells.fig3g-event-cells fig3-cell-regions
  • 549 units from 18 sessions against the paper's 580 from 19.edfig5-clip-onset-cells fig5d-state-shift
  • 19 of the paper's 20 subjects; the twentieth is not in the dandiset.fig2a-order-accuracy
  • Trials without a response are kept as coded in the file; leaving them out moves no mean by more than 0.01.fig2a-order-accuracy
  • Paired tests over subjects are added to the paper's one-way ANOVA.fig2a-order-accuracy
  • Trials without a response have an RT of 0 in the file and are kept; leaving them out gives 2.08, 1.64 and 1.56 s.fig2b-order-reaction-time
  • Trials without a response have a confidence of 0 in the file and are kept; leaving them out gives 2.00, 2.48 and 2.55.fig2c-order-confidence
  • The paper does not say whether foils are counted. Targets and foils are; targets alone give 0.737, 0.718 and 0.688 (p = 0.59).edfig2a-recognition-similar
  • 549 units from 18 sessions against the paper's 580 from 19. Subject 19 is left out because its spike times are not on the clock of the trial tables, and the dandiset's units differ from the paper's in number and in their split by region.fig3c-boundary-cells
  • The paper's "permutation t-test" is implemented as a sign-flip test for before against after and a one-sided label-permutation test between conditions, with 1000 permutations.fig3c-boundary-cells
  • Soft-boundary trials are every soft boundary of a session, in SB clips and in HB clips (75 per session). The Methods do not say; this follows the authors' published plotting code (B01_raster_psth_encoding_BAligned_BSep_NWB.m, lines 29 to 37). The first soft boundary of each SB clip, used before that code was read, is kept in the notebook as an alternative.fig3c-boundary-cells
  • Each condition is tested on its own, five tests per class, which is the literal reading of the Methods. The authors' published code has no cell selection to check this against.fig3c-boundary-cells
  • 300 label shuffles for the chance level instead of 1000.fig3c-boundary-cells
  • The paper's one-tailed test does not state its direction; an increase is tested. A decrease is found in 3.1% of units at onset.edfig5-clip-onset-cells
  • The permutation test is a sign-flip test with 1000 permutations.edfig5-clip-onset-cells
  • Pseudo-trials are matched across subjects by clip name; the paper does not say how.fig5d-state-shift
  • Principal components are fitted on the three condition averages from 1 s before to 1.5 s after the boundary, giving 22 components for 99% of the variance against the paper's 55. The averages are of the same trials that are then compared.fig5d-state-shift
  • A trial is z-scored from 1 s before to 8 s after clip onset; the paper says "the entire trial".fig5d-state-shift
  • Clips with soft boundaries are aligned to their first soft boundary, one pseudo-trial per clip. The authors' published code does not cover this analysis.fig5d-state-shift
  • The comparison with the second before the boundary is this attempt's; the paper shows curves.fig5d-state-shift
  • Two SB clips have their first soft boundary less than 1 s after onset. Leaving them out gives the same result (SB after minus before -0.03, p = 0.59; HB 0.84, p = 0.003).fig5d-state-shift

Provenance

Attempt record
000207-zheng-2022-01
Paper
Neurons detect cognitive boundaries to structure episodic memories in humans
Date
2026-10-13
Made by
An agent, run by CatalystNeuro
Dataset
DANDI:000207
Version
0.220216.0323 (doi:10.48324/dandi.000207/0.220216.0323)
Run on
local
Harness
Claude Code
Model
not recorded
Protocol
pilot-1

Transcripts of the agent runs

Each run’s prompt, messages and tool calls, with long output cut and the text of the paper left out, and its final report.

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