1. Reversible Silencing of the Frontopolar Cortex Selectively Impairs Metacognitive Judgment on Non-experience in Primates 
Kentaro Miyamoto, Rieko Setsuie, Takahiro Osada, Yasushi Miyashita 
Neuron 
Volume 97, Issue 4, Pages e6 (February 2018)
DOI: /j.neuron
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2. Figure 1
Metacognition Task and Metacognitive Performance in fMRI Experiments in Macaque Monkeys
(A) Sequence of the metacognition task in the post-decision wagering paradigm (left) and metacognitive performance indices (right). Based on self-evaluation of performance in the memory stage (yes/no visual memory recognition task), monkeys chose a high-bet (risky) or low-bet (safe) option in the bet stage. Metacognitive performance was evaluated by both phi coefficient, a contingency-table-based index (ΦExperienced and ΦNon-experienced), and meta-d’, a type II ROC function. CH, correct high-bet; CL, correct low-bet; CR, correct rejection; FA, false alarm; IH, incorrect high-bet; IL, incorrect low-bet.
(B and C) Metacognitive judgment performance in fMRI experiments evaluated by meta-d’ (B) and phi coefficient (C). Each open circle and dot represents a single session (n = 24). Bar graphs and error bars denote mean ± SEM. ∗p < 0.05; ∗∗p < 0.01; t test against zero.
(D) Recognition performance in high-bet (dark gray) and low-bet trials (light gray) evaluated by correct response rate (left) and d’ (right). Dot plots and error bars in the left panel denote mean ± SEM. Each dot in the right panel represents a single session. ∗p < 0.05; ∗∗∗p < 0.001; paired t test. Also see Figure S1.
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3. Figure 2
Whole-Brain Search for Metacognitive Sites for Experienced/Non-experienced Events
(A) Schematics of whole-brain search for metacognitive areas by session-by-session correlation between phi coefficient and fMRI activity in each voxel.
(B) Cortical areas predicting metacognitive performance for non-experienced items (left) and for experienced items (right; rΦ × fMRI; z > 3.1; p < uncorrected for display purpose). See Table 1 for coordinates of correlation peaks with p < 0.05, corrected by FWE across the whole-brain volume. Arrows, area 10 localized by whole-brain search.
(C) Cortical areas predicting metacognitive performance for non-experienced and experienced items in each monkey (rΦ × fMRI; z > 2.3; p < 0.01 uncorrected for display purpose). See Table S1A for coordinates of correlation peaks in each monkey. Also see Figures S2, S3, S5, and S8 and Table S1.
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4. Figure 3
Differential Contribution of the Frontopolar Cortex, Dorsal Prefrontal Cortex, and Hippocampus to Metacognition and Recognition
(A) ROI-based quantification of correlation coefficient between behavioral performance and fMRI activity in an anatomically predefined region of area 10 (Sallet et al., 2013; see text). rΦ × fMRI for non-experienced items (upper left), rΦ × fMRI for experienced items (upper right), rmeta-d’ × fMRI (lower left), and rd’ × fMRI (lower right) are shown. Circle, right area; square, left area.
(B) Correlation of behavioral performance and fMRI activity in the aPSPD and hippocampus, defined by previous monkey studies on metacognition and recognition (Miyamoto et al., 2013, 2017), as well as in the anatomically defined area 10 (as calculated in A). ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < See Figure S4 for rΦ × fMRI separately estimated for remotely and recently experienced items.
(C) Resting-state functional connectivity between the localized area 10 (see Figure 2B) and aPSPD (Miyamoto et al., 2017)/hippocampus (Miyamoto et al., 2013). Also see Figures S2, S3, S4, and S7.
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5. Figure 4
Causal Behavioral Impact by Inactivation of the Frontopolar Cortex (Area 10)
(A) Muscimol or saline was bilaterally injected at the spot in area 10 localized by whole-brain search in each monkey (see Table S1A for the coordinates of targeted injection sites). (Left) Gadolinium contrast agent visualized by MRI (white; the signals with the highest 1% of intensity) overlaid on the surface of template brain (copper color). (Right) Enlarged view of gadolinium injection sites on coronal and sagittal slices of fast spin-echo MRI scan images is shown. The calibration bars denote 5 mm. See also Figures S5C and S5D.
(B and C) Performance changes in confidence judgment after muscimol (10 sessions; green) or saline (7 sessions; light gray) injection targeted to the spot in area 10. Behavioral effects were evaluated using the phi coefficient (ΔΦ: Φ(POST-injection) − Φ(PRE-injection); B) and meta-d’ (Δmeta-d’: meta-d’(POST-injection) − meta-d’(PRE-injection); C). Dot plots and error bars denote mean ± SEM. ∗p < 0.05; ∗∗p < 0.01; post hoc simple main effects test after ANOVA (interactions; [muscimol and saline] × [experienced and non-experienced]; F1,13 = 4.77; p = 0.047). †p < 0.05; ‡p < 0.001; t test against zero.
(D) Performance changes in recognition memory evaluated by d’ after injection (Δd’: d’(POST-injection) − d’(PRE-injection)). Dot plots and error bars denote mean ± SEM.
(E) Recognition performance before (open circle) and after (filled circle) muscimol injection. Dot plots and error bars denote mean ± SEM. †p < 0.05; t test against zero. Also see Figures S5 and S6.
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6. Figure 5
Proposed Model of Neural Network for Metacognitive Judgment of Non-experienced and Experienced Events
The frontopolar cortex (area 10) contributes selectively to metacognitive judgment of non-experienced events or metacognition on one’s own ignorance (see also Figures 2, 3A, and 3B), by interacting with a center for recognition (hippocampus; see also Figures S8C and S8D). The frontopolar cortex and a dorsal prefrontal cortex (area 9; aPSPD), a center for metamnemonic judgment (see also Figure 3B), cooperatively work for metacognitive judgment of non-experienced and experienced events (see also Figure 3C). Solid double-headed arrow, task-evoked connectivity (hippocampus − area 10; for hippocampus − IPL, see Miyamoto et al., 2013; for area 9 − IPL, see Miyamoto et al., 2017); dotted double-headed arrow, resting-state functional connectivity (area 10 − area 9; for recognition memory network, see Miyamoto et al., 2013). Also see Figure S8.
Neuron  , e6DOI: ( /j.neuron )
Copyright © 2017 Elsevier Inc. Terms and Conditions