Effective modulation from the ventral medial to the dorsal medial portion of the prefrontal cortex in memory confidence-based behavioral control.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 May 2024
02 May 2024
Historique:
received:
10
10
2022
accepted:
26
04
2024
medline:
3
5
2024
pubmed:
3
5
2024
entrez:
2
5
2024
Statut:
epublish
Résumé
Metacognition includes the ability to refer to one's own cognitive states, such as confidence, and adaptively control behavior based on this information. This ability is thought to allow us to predictably control our behavior without external feedback, for example, even before we take action. Many studies have suggested that metacognition requires a brain-wide network of multiple brain regions. However, the modulation of effective connectivity within this network during metacognitive tasks remains unclear. This study focused on medial prefrontal regions, which have recently been suggested to be particularly involved in metacognition. We examined whether modulation of effective connectivity specific to metacognitive behavioral control is observed using model-based network analysis and dynamic causal modeling (DCM). The results showed that negative modulation from the ventral medial prefrontal cortex to the dorsal medial prefrontal cortex was observed in situations that required metacognitive behavioral control but not in situations that did not require such metacognitive control. Furthermore, this modulation was particularly pronounced in the group of participants who could better use metacognition for behavioral control. These results imply hierarchical properties of metacognition-related brain networks.
Identifiants
pubmed: 38698131
doi: 10.1038/s41598-024-60755-7
pii: 10.1038/s41598-024-60755-7
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10141Subventions
Organisme : MEXT | Japan Society for the Promotion of Science (JSPS)
ID : 22H01100
Organisme : MEXT | Japan Society for the Promotion of Science (JSPS)
ID : 17H06380
Informations de copyright
© 2024. The Author(s).
Références
Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. Am. Psychol. 34, 906–911 (1979).
doi: 10.1037/0003-066X.34.10.906
Flavell, J. H. Stage-related properties of cognitive development. Cogn. Psychol. 2, 421–453 (1971).
doi: 10.1016/0010-0285(71)90025-9
Nelson, T. O. & Narens, L. Metamemory: A theoretical framework and new findings. Psychol. Learn. Motiv. 26, 125–173 (1990).
doi: 10.1016/S0079-7421(08)60053-5
Shimamura, A. P. Toward a cognitive neuroscience of metacognition. Conscious. Cogn. 9, 313–323 (2000).
doi: 10.1006/ccog.2000.0450
pubmed: 10924251
Shimamura, A. P. A neurocognitive approach to metacognitive monitoring and control In Handbook of Metamemory and Memory (ed. Dunlosky, J. & Bjork, R. A.). 373–390 (Psychology Press, 2008).
Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J. & Rees, G. Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010).
doi: 10.1126/science.1191883
pubmed: 20847276
pmcid: 3173849
Fleming, S. M., Huijgen, J. & Dolan, R. J. Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32, 6117–6125 (2012).
doi: 10.1523/JNEUROSCI.6489-11.2012
pubmed: 22553018
pmcid: 3359781
Baird, B., Smallwood, J., Gorgolewski, K. J. & Margulies, D. S. Medial and lateral networks in anterior prefrontal cortex support metacognitive ability for memory and perception. J. Neurosci. 33, 16657–16665 (2013).
doi: 10.1523/JNEUROSCI.0786-13.2013
pubmed: 24133268
pmcid: 6618531
Vaccaro, A. G. & Fleming, S. M. Thinking about thinking: A coordinate-based meta-analysis of neuroimaging studies of metacognitive judgements. Brain Neurosci. Adv. 2, 2398212818810591 (2018).
doi: 10.1177/2398212818810591
pubmed: 30542659
pmcid: 6238228
Bang, D. & Fleming, S. M. Distinct encoding of decision confidence in human medial prefrontal cortex. Proc. Natl. Acad. Sci. USA 115, 6082–6087 (2018).
doi: 10.1073/pnas.1800795115
pubmed: 29784814
pmcid: 6003322
Haber, S. N. & Behrens, T. E. The neural network underlying incentive-based learning: Implications for interpreting circuit disruptions in psychiatric disorders. Neuron 83, 1019–1039 (2014).
doi: 10.1016/j.neuron.2014.08.031
pubmed: 25189208
pmcid: 4255982
Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016).
doi: 10.1038/nn.4384
pubmed: 27669989
Botvinick, M. M., Cohen, J. D. & Carter, C. S. Conflict monitoring and anterior cingulate cortex: An update. Trends Cogn. Sci. 8, 539–546 (2004).
doi: 10.1016/j.tics.2004.10.003
pubmed: 15556023
D’Argembeau, A. On the role of the ventromedial prefrontal cortex in self-processing: The valuation hypothesis. Front. Hum. Neurosci. 7, 372 (2013).
doi: 10.3389/fnhum.2013.00372
pubmed: 23847521
pmcid: 3707083
Hiser, J. & Koenigs, M. The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biol. Psychiatry 83, 638–647 (2018).
doi: 10.1016/j.biopsych.2017.10.030
pubmed: 29275839
Su, J., Jia, W. & Wan, X. Task-specific neural representations of generalizable metacognitive control signals in the human dorsal anterior cingulate cortex. J. Neurosci. 42, 1275–1291 (2022).
doi: 10.1523/JNEUROSCI.1283-21.2021
pubmed: 34907025
pmcid: 8883847
Yuki, S., Nakatani, H., Nakai, T., Okanoya, K. & Tachibana, R. O. Regulation of action selection based on metacognition in humans via a ventral and dorsal medial prefrontal cortical network. Cortex 119, 336–349 (2019).
doi: 10.1016/j.cortex.2019.05.001
pubmed: 31181421
Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 1273–1302 (2003).
doi: 10.1016/S1053-8119(03)00202-7
pubmed: 12948688
Friston, K. & Penny, W. Post hoc Bayesian model selection. Neuroimage 56, 2089–2099 (2011).
doi: 10.1016/j.neuroimage.2011.03.062
pubmed: 21459150
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).
Di, X. & Biswal, B. B. Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging. Neuroimage 86, 53–59 (2014).
doi: 10.1016/j.neuroimage.2013.07.071
pubmed: 23927904
Jiao, Q. et al. Granger causal influence predicts BOLD activity levels in the default mode network. Hum. Brain Mapp. 32, 154–161 (2011).
doi: 10.1002/hbm.21065
pubmed: 21157880
Paneri, S. & Gregoriou, G. G. Top-down control of visual attention by the prefrontal cortex: Functional specialization and long-range interactions. Front. Neurosci. 11, 545 (2017).
doi: 10.3389/fnins.2017.00545
pubmed: 29033784
pmcid: 5626849
Siedlecka, M., Paulewicz, B. & Wierzchoń, M. But I was so sure! Metacognitive judgments are less accurate given prospectively than retrospectively. Front. Psychol. 7, 218 (2016).
doi: 10.3389/fpsyg.2016.00218
pubmed: 26925023
pmcid: 4759291
Shekhar, M. & Rahnev, D. Sources of metacognitive inefficiency. Trends Cogn. Sci. 25, 12–23 (2021).
doi: 10.1016/j.tics.2020.10.007
pubmed: 33214066
Yuki, S., Sakurai, Y. & Okanoya, K. The utility of internal cognitive states as discriminative cues affecting behavioral adaptation in humans and animals. Anim. Behav. Cogn. 6, 262–272 (2019).
doi: 10.26451/abc.06.04.06.2019