SWR-associated replay engages most of the dorsal
hippocampus (9, 27), we examined the relationship
of sMEC replay with SWRs. CA1 ripple power
showed only a marginal increase during sMEC
replay compared to CA1 replay, even for sMEC
events exhibiting coherent replay with CA1 (Fig.
3G). Indeed, out of 202 events, only 5.9% of sMEC
replay events were temporally aligned to the detected SWR (peak of HSE ± 25 ms from the peak
of the SWR), in contrast to 29.9% ( 184 out of 674)
for CA1 replay (Fig. 3H). HSEs in the two regions
were also independent; only 4.4% of sMEC replay events occurred within 25 ms of CA1 HSEs,
whether or not we could detect replay there (Fig.
3H). However, CA1 activity encoded places that,
with a probability higher than chance, overlapped with nonaligned parts of the sMEC trajectories, and vice versa (fig. S17, C and D; all Ps <
0.01, KS test). Therefore, even during rest, the two
regions could weakly interact during reactivation.
Our data show that sMEC cells, including grid
cells, fired in relation to the memory task on the
maze. Furthermore, sMEC was involved in the
mnemonic trajectory sequence coding, producing bursts of activity during both waking and
sleep or rest that contained sequences reflecting
task-related trajectories on the maze. Such events
tended to occur independently of hippocampal
trajectory replay and associated SWRs. Moreover,
trajectory replay that occurred in the sMEC was
not associated with temporally aligned coherent
activity in CA1. This suggests that the sMEC can
trigger its own replay events and initiate recall
and consolidation processes independent of hippocampal SWRs, whereas deep EC layers are directly influenced by CA1 replay (26). However,
some weak coordination may exist between CA1
and sMEC, and in some instances, replayed trajectories contain locations that were expressed in
the other region.
Overall, these findings indicate that the EC can
act independently in mnemonic processes rather
than having a subservient role to the hippocampus.
Therefore, the hippocampus and the EC may be
considered as interrelated but parallel systems in
initiating reactivation, and they may recruit different brain pathways and may have different roles.
REFERENCES AND NOTES
1. J. O’Keefe, L. Nadel, The Hippocampus as a Cognitive Map
(Oxford Univ. Press, 1978).
2. G. Riedel et al., Nat. Neurosci. 2, 898–905 (1999).
3. G. Buzsáki, Neuroscience 31, 551–570 (1989).
4. J. L. McClelland, B. L. McNaughton, R. C. O’Reilly, Psychol. Rev.
102, 419–457 (1995).
5. J. O’Neill, T. Senior, J. Csicsvari, Neuron 49, 143–155 (2006).
6. S. P. Jadhav, C. Kemere, P. W. German, L. M. Frank, Science
336, 1454–1458 (2012).
7. D. J. Foster, M. A. Wilson, Nature 440, 680–683 (2006).
8. D. K. Roumis, L. M. Frank, Curr. Opin. Neurobiol. 35, 6–12 (2015).
9. B. E. Pfeiffer, D. J. Foster, Nature 497, 74–79 (2013).
10. A. K. Lee, M. A. Wilson, Neuron 36, 1183–1194 (2002).
11. M. A. Wilson, B. L. McNaughton, Science 265, 676–679 (1994).
12. G. Girardeau, K. Benchenane, S. I. Wiener, G. Buzsáki,
M. B. Zugaro, Nat. Neurosci. 12, 1222–1223 (2009).
13. G. Buzsáki, Cereb. Cortex 6, 81–92 (1996).
14. N. K. Logothetis et al., Nature 491, 547–553 (2012).
15. T. Hafting, M. Fyhn, S. Molden, M. B. Moser, E. I. Moser, Nature
436, 801–806 (2005).
16. B. L. McNaughton, F. P. Battaglia, O. Jensen, E. I. Moser,
M. B. Moser, Nat. Rev. Neurosci. 7, 663–678 (2006).
17. G. R. Sutherland, B. McNaughton, Curr. Opin. Neurobiol. 10,
18. R. M. J. Deacon, J. N. P. Rawlins, Nat. Protoc. 1, 7–12 (2006).
19. E. I. Moser et al., Nat. Rev. Neurosci. 15, 466–481 (2014).
20. K. Gupta, L. A. Keller, M. E. Hasselmo, Learn. Mem. 19,
21. K. Zhang, I. Ginzburg, B. L. McNaughton, T. J. Sejnowski,
J. Neurophysiol. 79, 1017–1044 (1998).
22. L. M. Frank, E. N. Brown, M. Wilson, Neuron 27, 169–178 (2000).
23. P. A. Lipton, J. A. White, H. Eichenbaum, J. Neurosci. 27,
24. S. Leutgeb et al., Science 309, 619–623 (2005).
25. A. J. Horner, J. A. Bisby, E. Zotow, D. Bush, N. Burgess,
Curr. Biol. CB 26, 842–847 (2016).
26. H. F. Ólafsdóttir, F. Carpenter, C. Barry, Nat. Neurosci. 19,
27. A. Ylinen et al., J. Neurosci. 15, 30–46 (1995).
We thank P. Jonas for comments on an earlier version of the
manuscript. This work was supported by a European Research
Council Consolidator Grant (281511). The authors declare no
conflicts of interest. Original data and programs were stored in
the scientific repository of the Institute of Science and Technology
Austria and are available on request.
Materials and Methods
Figs. S1 to S17
2 June 2016; accepted 1 December 2016
Causal neural network of
metamemory for retrospection
Kentaro Miyamoto,1 Takahiro Osada,1,2 Rieko Setsuie,1 Masaki Takeda,1,2*
Keita Tamura,1 Yusuke Adachi,1 Yasushi Miyashita1,2†
We know how confidently we know: Metacognitive self-monitoring of memory states, so-called
“metamemory,” enables strategic and efficient information collection based on past experiences.
However, it is unknown how metamemory is implemented in the brain. We explored causal
neural mechanism of metamemory in macaque monkeys performing metacognitive confidence
judgments on memory. By whole-brain searches via functional magnetic resonance imaging,
we discovered a neural correlate of metamemory for temporally remote events in prefrontal
area 9 (or 9/46d), along with that for recent events within area 6. Reversible inactivation of
each of these identified loci induced doubly dissociated selective impairments in metacognitive
judgment performance on remote or recent memory, without impairing recognition performance
itself. The findings reveal that parallel metamemory streams supervise recognition networks
for remote and recent memory, without contributing to recognition itself.
Introspection on memory states (1), or self- monitoring (2, 3) and evaluation (3–5) of our own memory (6), makes us feel retrospective. This self-reflective mental process had been commonly believed to be unique to humans
because it requires a higher level of cognition
about our own cognition. This meta-level mem-
ory process is termed “metamemory” (1, 6–8),
and is conceptually considered to supervise the
process of memory execution itself (i.e., encod-
ing, maintenance, and retrieval). However, the
neural mechanism of metamemory, even the cor-
tical distribution of responsible neural activities,
is totally unknown, whereas the neural basis of
memory execution has been precisely revealed
as a multitiered brain-wide network in humans
and animals (1, 6, 9, 10). Therefore, it remains
elusive whether and, if so, how metamemory is
implemented in the brain as an independent
and integrative neural process that is distinct
from the memory execution process itself.
For exploration of unknown neural substrates,
it is efficient and fruitful to combine whole-brain
searches for neural correlates and subsequent ex-
aminations of causal behavioral impacts by finely
targeted neural intervention (11). The psycholog-
ical and behavioral framework for experimenta-
tion on metacognitive skills has been developed
only recently in nonlinguistic animals (12, 13).
Studies in rats (14) and macaques (15–17) re-
corded neuronal activity that was related to the
metacognitive judgment on perception rather
than on memory. These studies identified the neu-
ral correlates of the self-monitoring skills used to
make adaptive decisions based on real-time ex-
periences: Single-cell activity carried information
that correlated with both perceptual metacogni-
tion and perception itself (14–17). In contrast,
metamemory requires the reconstruction of past
experiences as present mental representations
and, thus, naturally requires more self-reflective
and introspective information processing than
188 13 JANUARY 2017 • VOL 355 ISSUE 6321 sciencemag.org SCIENCE
1Department of Physiology, The University of Tokyo School
of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan. 2Juntendo University Graduate School of Medicine,
2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
*These authors contributed equally to this work. †Corresponding
author. Email: email@example.com