Task paradigm

We developed a novel decision–redecision paradigm for this study (Fig 2A). The participants made an initial decision (decision phase), immediately followed by another decision on the same situation (redecision phase). This allowed the participant the opportunity to revise the initial decision and update their confidence rating, even without feedback. The internal states of uncertainty regarding initial and final decisions were separately evaluated by confidence ratings. Confidence was rated on a scale of 1 to 4, immediately following the corresponding decisions. Decision uncertainty was then the inverse of the confidence rating (i.e., a confidence rating of 4 corresponded to an uncertainty rating of 1). Critically, our task differed from previous paradigms used to analyze ‘change of mind’ [21, 24]. Previous task paradigms were only able to analyze the small portion of trials in which a participant happened to change their mind, while our paradigm allowed analysis of each trial.

We used two different types of decision-making tasks in the present study: one was a rule-based decision-making task (Sudoku), the other a perceptual decision-making task (random-dot motion [RDM]), which has commonly been used to investigate the neural process of decision-making [5] and more recently, metacognition [21, 22, 24, 26]. The decisions in the Sudoku task rely on internal informational operations, but decisions in the RDM task should be more dependent on accumulation of external information. It is possible to continue accumulating evidence from external stimuli that may affect decision-making in the RDM task, but that is less likely in the Sudoku task because it is rule based. For this reason, the two tasks should result in differential processing in metacognitive control to adjust initial decisions. The sequences of both tasks were identical (Fig 2A, illustrated for the main fMRI experiment [fMRI1]). After a Sudoku problem or RDM stimulus was presented for 2 s, the participant made a choice from 4 possible solutions within 2 s and then reported their confidence rating on that decision within 2 s. A critical feature of our paradigm was that the same Sudoku problem or the same RDM stimulus was immediately repeated for 4 s, and the participant again made a choice within 2 s and again reported their confidence rating within 2 s. To better distinguish the metacognitive process from the decision-making process, we intentionally set a short initial decision phase (2 s), to minimize metacognition during the initial decision-making phase, but set a longer duration in the redecision phase (4 s) to allow enough time for metacognitive processing in redecision. There was no explicit feedback or cue to indicate whether the decision was correct after either the initial decision or the redecision. For both tasks, the task difficulty of each trial (Fig 2B) was adaptively adjusted by a staircase procedure [9, 27] so that the average accuracy for the initial decision was converged to approximately 50% (chance level was 25%). For the control condition, the participant was shown a digital number in the target grid in the Sudoku task, and for the RDM task, s/he was shown an RDM stimulus with 100% coherence. For the former, the participant only needed to press the button matching the number, and for the latter, the participant indicated the unambiguous RDM direction. Prior to the experimental testing, the participant was trained to attain high-level proficiency in Sudoku problem-solving.

The current study was composed of 4 fMRI experiments:

• fMRI1 was the main experiment to illustrate the neural system of metacognition accompanying decision-making.
• fMRI2 was designed to test whether the neural system of metacognition would also be activated during the initial decision-making when a new Sudoku problem or a new RDM stimulus was presented in the redecision phase.
• fMRI3 was designed to test whether redecision activated the neural system of metacognition more strongly than the condition with no redecision because redecision should involve additional neural processing for metacognitive control.
• fMRI4 was designed to confirm that the engagement of metacognition was independent of the duration of redecision (from 4 s in fMRI1 to 2 s in fMRI4 in the RDM task).

Behavioral results

Twenty-one participants took part in fMRI1 (see Materials and methods). In both the Sudoku and RDM tasks, decision uncertainty levels were largely consistent with the percentage of incorrect initial decisions (Fig 2C; Pearson’s r = 0.76 ± 0.12 [mean ± SD], one-tailed t test, t₂₁ = 7.3, P = 1.7 × 10⁻⁷ in the Sudoku task; r = 0.71 ± 0.14, t₂₁ = 6.8, P = 5.0 × 10⁻⁷ in the RDM task). To examine the trial-by-trial consistency between objective erroneous decisions and subjective decision uncertainty levels in individual participants, a nonparametric approach was employed to construct the receiver operating characteristic (ROC) curve by using the decision uncertainty levels as thresholds to characterize the likelihood of erroneous decisions. The area under curve (A_ROC) was then calculated to represent the individual uncertainty sensitivity, indicating how sensitive the participant was to the decision uncertainty [9]. As observed in the previous studies, the uncertainty sensitivity of individual participants markedly deviated from decision accuracy in both tasks, which were controlled around 50% (Fig 2D). The response times (RTs) of option choices in the initial decision were positively correlated with the decision uncertainty levels (Fig 2E; t₂₁ = 6.9, P = 4.0 × 10⁻⁷ in the Sudoku task; t₂₁ = 4.3, P = 1.6 × 10⁻⁴ in the RDM task). The correlation coefficient between RT of option choices and the decision uncertainty level (r_{RT-uncertainty}) in the initial decision was highly correlated with the uncertainty sensitivity (A_ROC1) across the participants (Pearson’s r = 0.61, z test, z = 3.4, P = 4.0 × 10⁻⁴ in the Sudoku task; r = 0.48, z = 2.4, P = 0.0085 in the RDM task). Thus, the RT–uncertainty correlation also reflected individual uncertainty sensitivity.

The level of decision uncertainty was reduced by redecision. The extent of decision uncertainty reduction via redecision was highly correlated with the decision uncertainty level in the initial decision phase (Fig 2F; Goodman and Kruskal’s γ = 0.82 ± 0.11, t₂₁ = 8.8, P = 2.1 × 10⁻⁸ in the Sudoku task; γ = 0.78 ± 0.14, t₂₁ = 7.7, P = 8.2 × 10⁻⁸ in the RDM task). Accordingly, accuracy also improved along with uncertainty reduction (Fig 2G; Pearson’s r = 0.54 ± 0.13, t₂₁ = 4.2, P = 2.3 × 10⁻⁴ in the Sudoku task; r = 0.39 ± 0.14, t₂₁ = 2.8, P = 5.6 × 10⁻³ in the RDM task). One could suspect that the improvement of uncertainty reduction and the change in accuracy in the redecision phase were caused by regression towards mean in the two separate decisions: higher uncertainty at the first measurement by chance would increase improvement at the second measurement. However, the decision accuracy and decision uncertainty levels for the final decision-making phase remained significantly differential (Pearson’s r = 0.35 ± 0.15, t₂₁ = 2.1, P = 0.032 in the Sudoku task; r = 0.36 ± 0.14, t₂₁ = 2.6, P = 8.9 × 10⁻³ in the RDM task in Fig 2C; Pearson’s r = 0.32 ± 0.14, t₂₁ = 2.0, P = 0.042 in the Sudoku task; r = 0.32 ± 0.15, t₂₁ = 2.2, P = 0.028 in the RDM task in Fig 2G), indicating that the participants’ performance in redecision reflected metacognitive processing ability rather than chance. Despite the fact that both decision accuracy and decision uncertainty levels were improved in the redecision phase, the divergence between uncertainty sensitivity and decision accuracy remained significant (Fig 2H). Indeed, neither the individual uncertainty sensitivities nor those of individual differences were altered by redecision (Fig 2I; t₂₁ = 0.82, P = 0.21 in the Sudoku task; t₂₁ = 1.0, P = 0.15 in the RDM task). Similarly, neither the individual RT–uncertainty correlation coefficients nor those of individual differences were altered by redecision (Fig 2E; t₂₁ = −0.77, P = 0.22 in the Sudoku task; t₂₁ = 0.35, P = 0.36 in the RDM task). These results show that individual uncertainty sensitivity was stable, was intrinsic to individual metacognitive ability, and was independent of the accumulated evidence and the type of decision-making required.

Neural correlates of metacognitive monitoring and control in redecision processing

Commonly across both tasks, brain activation during the initial decision phase was mainly restricted to brain areas posterior to the PFC, in particular the posterior portion of the PFC, the inferior frontal junction (IFJ) (S1A Fig; Fig 3A). In the redecision phase, a frontoparietal control network—consisting of the lFPC, dACC, anterior insular cortex (AIC), middle dorsolateral PFC (mDLPFC), and anterior inferior parietal lobule (aIPL)—was more extensively recruited (Fig 3B; S1B and S2 Figs; S1 Table). In contrast, the lFPC and mDLPFC regions of the frontoparietal control network were not activated when a new Sudoku problem or a new RDM stimulus was presented for the first time during the redecision phase, preceded by the control stimuli in the initial phase (fMRI2, n = 17; S1C and S3 Figs), while the dACC activity during the same phase became much weaker, and its response onset was much delayed from the onset of the stimulus presentation (delay offset >3 s; S3 Fig). Thus, the frontoparietal control network, in particular the regions of the lFPC, mDLPFC, and dACC in the anterior FPC, were more extensively involved in redecision than in the initial decision phase.

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Fig 3. The neural network involved in metacognitive monitoring and control.

(A) Activation in the task trials compared with the control trials during the initial decision phase (significant only in Sudoku task). (B) Activation in the task trials compared with the control trials during the redecision phase. (C) Activation in task trials during the redecision phase regressed with decision uncertainty levels. (D) Activation in task trials with redecision required the ‘redecision’ condition, compared with the “non-redecision” condition in fMRI3. Red/yellow patches indicate activations in the Sudoku task, blue/light-blue patches indicate activations in the RDM task; z = 3.1, P < 0.05, FDR-corrected. Green/light-green patches indicate conjunction activations across the two tasks; z = 3.1, P < 0.001, cluster-size corrected. “A” denotes the lFPC; “B” denotes the mDLPFC; “C” denotes the dACC; “D” denotes the AIC; “E” denotes the aIPL; and “F” denotes the IFJ. AIC, anterior insular cortex; aIPL, anterior inferior parietal lobule; dACC, dorsal anterior cingulate cortex; FDR, false discovery rate; fMRI, functional magnetic resonance; IFJ, inferior frontal junction; lFPC, lateral frontopolar cortex; mDLPFC, middle dorsolateral prefrontal cortex; RDM, random-dot motion.

https://doi.org/10.1371/journal.pbio.2004037.g003

Trial-by-trial activity in the regions of the frontoparietal control network in redecision was positively correlated with decision uncertainty level for the initial decision (Fig 3C and S2 Table). Critically, these correlations remained significant even for the correct trials (S1E Fig), indicating that these regions were encoding the decision uncertainty signal rather than the error signal. Furthermore, task difficulty or RT could not explain their association with the decision uncertainty in these regions. The residual fMRI signal changes after the components associated with the task difficulty and RT were regressed out remained highly correlated with decision uncertainty level, but residual fMRI signal changes after the components of the decision uncertainty level were regressed out were not further correlated with the task difficulty and RT. Although the dACC and AIC regions were also partially activated during the initial decision phase (S1A Fig and Fig 3A), this activity—as well as in other regions activated during the same phase—was neither positively nor negatively correlated with the decision uncertainty level (S1D Fig). Activity in the ventromedial PFC (VMPFC) and posterior cingulate cortex (PCC) regions of the default-mode network in redecision were negatively correlated with decision uncertainty level or positively correlated with its inverse—decision confidence (S1F Fig). Thus, the regional activity seen in the frontoparietal control network involved processes intrinsic to redecision but not the activity involved in decision-making for the initial phase.

In the third fMRI experiment (fMRI3, n = 25), we confirmed that the strength of activity in the frontoparietal control network depended critically on whether redecision was required after the initial decision phase. When decision uncertainty levels for initial decisions were matched in the two conditions (two-tailed paired t test, t₂₅ = 0.62, P = 0.27), activity in the frontoparietal control network was much stronger when redecision was required (‘redecision condition’), in comparison with those when redecision was not required (“non-redecision condition”) (Fig 3D), despite the fact that activation of the frontoparietal control network in the ‘non-redecision condition’ was also significant (S1G Fig) and was correlated with decision uncertainty level as well [25]. Thus, the frontoparietal control network, more strongly activated in redecision, should not only be involved in metacognitive monitoring of decision uncertainty of the initial decision but also in metacognitive control in redecision (Fig 1D). We then putatively defined this frontoparietal control network as the metacognition network.

Because the duration of the redecision phase in fMRI1 was longer (4 s) than that of the initial decision phase (2 s), it raised the question of whether the fMRI activity predominately observed during the redecision phase was induced by the longer exposure, specifically in the trials with more difficult decisions. To address this, we scanned an independent group of participants (fMRI4, n = 20) while they underwent the same RDM task as fMRI1 except that the duration of redecision was set to 2 s. The same behavioral and neural results were replicated as in fMRI1 (S4 Fig).

Just as the extent of uncertainty reduction by redecision was found to be highly correlated with the decision uncertainty level of the initial decision (Fig 2F), activity in the regions of the metacognition network were also found to be positively correlated with the extent of uncertainty reduction (S1H Fig). However, the strength of the correlations decreased somewhat after the components associated with the decision uncertainty level were regressed out (S1I Fig). Conversely, correlations with decision uncertainty level in the metacognition network remained significant after the components associated with the extent of uncertainty reduction were regressed out (S1J Fig). These partial correlations complementarily confirmed that the metacognition network in redecision was involved in both metacognitive monitoring and metacognitive control, indicating that the two processes interacted in redecision processing.

Dissociation of metacognitive monitoring and metacognitive control in redecision within individual participants

The two processes, although interactive, can be dissociated. In the region involved in uncertainty monitoring, activity strength should dynamically represent decision uncertainty level. As decision uncertainty levels were reduced by redecision, the strength of its activity should accordingly be reduced. Therefore, the neural activity change should be negatively correlated with the extent of decision uncertainty reduction. Alternatively, in the region that was critically involved in metacognitive control, its activity should become positively correlated with the extent of decision uncertainty reduction, representing the outcome or the extent of metacognitive control. We found that the activity in the dACC and AIC regions at the late phase of redecision did in fact negatively correlate with the extent of decision uncertainty reduction after the components associated with the decision uncertainty level of the initial decision were regressed out (Fig 4A, S1K and S1L Fig). Conversely, the lFPC activity in the Sudoku task was positively correlated with the extent of decision uncertainty reduction after components associated with the decision uncertainty level were regressed out (Fig 4B), but negatively in the RDM task (Fig 4B and S1I Fig). In addition, VMPFC activity was also positively correlated with the extent of decision uncertainty reduction in both tasks (S1I Fig). The regional activity in the default-mode network appeared intrinsically anticorrelated with the regional activity in the metacognition network (further detail regarding activity in the default-mode network associated with metacognition will be discussed in another study). Thus, the dACC and AIC regions were specifically involved in metacognitive monitoring. In contrast, the lFPC was specifically involved in metacognitive control in redecision, particularly in the Sudoku task. Therefore, their functional roles in metacognition appear to dissociate in redecision processing.

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Fig 4. Dissociated metacognitive monitoring in dACC and metacognitive control in the lFPC in redecision processing.

(A) dACC activity was negatively correlated with the extent of decision uncertainty reduction after orthogonalization with the decision uncertainty level in both tasks. (B) The lFPC activity was positively correlated with the extent of decision uncertainty reduction in the Sudoku task, but negatively in the RDM task. (C) The VS activity was positively correlated with the extent of decision uncertainty reduction in the Sudoku task, though the early VS activity was negatively correlated with the decision uncertainty level. (D) The lFPC activity was significantly modulated by the VS activity (physiological effect) and the decision uncertainty level (psychological effect) interaction (PPI) in the Sudoku task. (E) Individual accuracy change by redecision was positively correlated with the PPI coupling strength in the lFPC region in the Sudoku task. Time courses are relative to the onset of the initial decision. The broken lines indicate regressions with the decision uncertainty level of the initial decision, and the solid lines indicate regressions with the uncertainty change by redecision (red: the Sudoku task; blue: the RDM task). The data can be found in S1 Data. dACC, dorsal anterior cingulate cortex; lFPC, lateral frontopolar cortex; PPI, psycho–physiological interaction; RDM, random-dot motion; VS, ventral striatum.

https://doi.org/10.1371/journal.pbio.2004037.g004

In the Sudoku task, whether the problem would be better solved should be conditioned to individual intrinsic motivation to engage metacognitive control because metacognitive control was effortful. The ventral striatum (VS) activity during the redecision phase was positively correlated with the extent of decision uncertainty reduction in the Sudoku task, but not in the RDM task (Fig 4C). VS might encode the intrinsic motivation or the internal reward on reduction in uncertainty during the redecision phase in the Sudoku task. Critically, the lFPC activity was significantly coupled with the interaction between the VS activity and the decision uncertainty level of the initial decision (Fig 4D; see psycho–physiological interaction [PPI] analysis in Materials and methods). Furthermore, the accuracy change of each participant by redecision was positively correlated with the coupling strength in the Sudoku task (Fig 4E). These results implied that the efficiency of lFPC involvement in metacognitive control in rule-based decision-making tasks (i.e., Sudoku) might be facilitated by the VS activity.

Dissociation of metacognitive ability in monitoring and control in individual participants

The abilities of metacognitive monitoring and control are behaviorally embodied in two components: uncertainty sensitivity and accuracy change, respectively. Throughout all sessions, including fMRI1 and the other repeated behavioral experiments, the individual uncertainty sensitivity was highly consistent across different sessions of the Sudoku task (Cronbach’s α = 0.91; Fig 5A, left column, upper panel) and the RDM task (α = 0.89, Fig 5A, left column, middle panel), as well as across the two tasks (α = 0.85; Fig 5A, left column, lower panel). In contrast, the individual accuracy change in redecision was not consistent across the two tasks (α = 0.03; Fig 5A, right column, lower panel), although it was consistent between different sessions of the Sudoku task (α = 0.80; Fig 5A, right column, upper panel) and the RDM task (α = 0.76; Fig 5A, right column, middle panel). Thus, individual metacognitive abilities of uncertainty monitoring were reliably consistent, but individual metacognitive control was dissociable in the two tasks.

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Fig 5. Individual metacognitive uncertainty sensitivity and accuracy change in redecision associated, respectively, with dACC and lFPC activity.

(A) The histograms of correlation coefficients of individual uncertainty sensitivity (A_ROC, left column) and individual accuracy change (right column) between different sessions in the Sudoku task and the RDM task as well as across the two tasks. The arrows indicate the medians of the histograms. (B) The individual uncertainty sensitivity (A_ROC) was positively correlated with the uncertainty-level regression β values of the fMRI activity mainly in the dACC and AIC regions. (C) The scatter plots of the dACC and lFPC activity regressed with the decision uncertainty level against the individual uncertainty sensitivity. (D) The individual accuracy change was positively correlated with the mean activity predominately in the lFPC region, in the Sudoku task. (E) The scatter plots of the dACC and lFPC mean activity against the individual accuracy change. In panel C and E, the solid lines indicate fitting data in the Sudoku task, and the broken lines indicate fitting data in the RDM task. The conventions in panel B and C are the same as in Fig 3. The data can be found in S1 Data. AIC, anterior insular cortex; dACC, dorsal anterior cingulate cortex; fMRI, functional magnetic resonance imaging; lFPC, lateral frontopolar cortex; RDM, random-dot motion.

https://doi.org/10.1371/journal.pbio.2004037.g005

Accordingly, the individual uncertainty sensitivity (A_ROC) was positively correlated with the uncertainty-level regression β value of the fMRI signal changes (i.e., neural uncertainty sensitivity), primarily in the dACC and AIC regions (Fig 5B, P < 0.001, cluster size = 20; and Fig 5C upper, one-tailed t test, Pearson’s r = 0.79, t₁₉ = 5.6, P = 6.0 × 10⁻⁶ in the Sudoku task; r = 0.55, t₁₉ = 2.9, P = 0.0049 in the RDM task; S3 Table), but not in the lFPC region (Fig 5B and 5C bottom; Pearson’s r = 0.17, t₁₉ = 0.8, P = 0.22 in the Sudoku task; r = 0.21, t₁₉ = 1.0, P = 0.17 in the RDM task), commonly in both tasks. The differences of correlations were significant between the two regions (t₁₉ = 3.8, P = 5.6 × 10⁻⁴ in the Sudoku task; t₁₉ = 2.3, P = 0.016 in the RDM task). In contrast, the individual accuracy change was significantly correlated with the mean activity in the lFPC region (Fig 5D, P < 0.001, cluster size = 20; and Fig 5E bottom; Pearson’s r = 0.69, t₁₉ = 4.2, P = 2.2 × 10⁻⁴ in the Sudoku task; r = −0.39, t₁₉ = 1.9, P = 0.041 in the RDM task), but not in the dACC region (Fig 5D and 5E upper; Pearson’s r = 0.18, t₁₉ = 0.8, P = 0.21 in the Sudoku task; r = −0.02, t₁₉ = 0.09, P = 0.47 in the RDM task). When the lFPC activity was stronger, the accuracy change was more in the Sudoku task but became less in the RDM task (Fig 5E). The differences of correlations were significant between the two regions (t₁₉ = 2.7, P = 0.007 in the Sudoku task; t₁₉ = 1.8, P = 0.045 in the RDM task). Thus, the dACC activity (AIC as well) commonly represented individual metacognitive abilities in monitoring of decision uncertainty, whereas the lFPC differentially modulated individual metacognitive abilities in control of decision adjustment—in both the Sudoku and RDM tasks—consistent with their dissociated functional roles in metacognitive monitoring and metacognitive control, respectively.

Task baseline activity in the metacognitive network predicts individual metacognitive monitoring and control

The regions of the metacognition network were also activated in the trials of both tasks with confidence level 4 in comparison with their respective control conditions (Fig 6B and S2 Fig). These activity differences might be partially caused by differentially subjective uncertain states of the two conditions that were not reflected by the four-scale confidence ratings (i.e., the ceiling effect). The averaged accuracy was about 80% in the certain trials of the tasks (Fig 2C), but it was about 95% in the control conditions. Nevertheless, the task baseline activity in the certain trials of the tasks could also predict the individual uncertainty monitoring bias and potential abilities of efficient metacognitive control of decision adjustment. Individual uncertainty monitoring bias—as estimated by averaging the decision uncertainty levels of all trials in each session of the tasks, representing the individual’s overconfident or underconfident tendency—was consistent between different sessions in the Sudoku task (α = 0.95; Fig 6A, left panel) and in the RDM task (α = 0.94, Fig 6A, middle panel), as well as across the two tasks (α = 0.91; Fig 6A, right panel). Accordingly, individual uncertainty monitoring bias was positively correlated with the mean task baseline activity in the dACC region (Fig 6C P < 0.001, cluster size = 20; and Fig 6F left, Pearson’s r = 0.50, t₁₉ = 2.5, P = 0.0096 in the Sudoku task; r = 0.44, t₁₉ = 2.1, P = 0.022 in the RDM task) but not in the lFPC region (Fig 6C and 6F right; Pearson’s r = 0.18, t₁₉ = 0.80, P = 0.22 in the Sudoku task; r = −0.04, t₁₉ = 0.17, P = 0.43 in the RDM task), commonly in both tasks. The differences of correlations were significant between the two regions (t₁₉ = 2.1, P = 0.026 in the Sudoku task; t₁₉ = 1.8, P = 0.042 in the RDM task). Meanwhile, the individual accuracy change in the Sudoku task was positively correlated with the mean task baseline activity in the lFPC region (Fig 6D and 6G right; Pearson’s r = 0.45, t₁₉ = 2.2, P = 0.020) but not with that in the dACC region (Fig 6G left; one tailed t test, r = 0.14, t₁₉ = 0.62, P = 0.27). In contrast, the individual accuracy change in the RDM task was negatively correlated with the mean task baseline activity in the lFPC region (Fig 6E and 6G right; Pearson’s r = −0.40, t₁₉ = 1.9, P = 0.035) but not with that in the dACC region (Fig 6G left; r = −0.13, t₁₉ = 0.57, P = 0.29). The differences of correlations were significant between the two regions (t₁₉ = 1.9, P = 0.039 in the Sudoku task; t₁₉ = 1.8, P = 0.046 in the RDM task). Furthermore, the differences of correlations with the individual uncertainty monitoring bias and the individual accuracy change in the dACC (t₁₉ = 2.2, P = 0.020 in the Sudoku task; t₁₉ = 2.8, P = 0.0055 in the RDM task), as well as in the lFPC (t₁₉ = 1.8, P = 0.046 in the Sudoku task; t₁₉ = 2.0, P = 0.030 in the RDM task), were significant. Thus, the task baseline activity in the dACC region commonly reflected the individual uncertainty monitoring bias in both tasks, whereas that in the lFPC region could predict the individually differential potential abilities of metacognitive control for decision adjustment in both tasks.

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Fig 6. Individual metacognitive abilities predicted by the task baseline activity of the dACC and lFPC regions in the metacognition network in redecision.

(A) The histograms of the correlation coefficients of individual uncertainty bias between different sessions of the Sudoku or RDM task as well as across the two tasks. The arrows indicate the medians of the histograms. (B) The task baseline activity (confidence level = 4) in comparison to those of the control trials in the Sudoku task and the RDM task. (C) Positive correlation of task baseline activity during the redecision phase of the task trials with the individual uncertainty bias across the participants. The conventions in panel B and C are the same as in Fig 3. (D) The lFPC task baseline activity was positively correlated with the individual accuracy change across the participants in the Sudoku task. (E) The lFPC task baseline activity was negatively correlated with the individual accuracy change across the participants in the RDM task. (F) The scatter plots of the dACC and lFPC task baseline activity against the individual uncertainty bias. (G) The scatter plots of the dACC and lFPC task baseline activity against the individual accuracy change. In panel F and G, the solid lines indicate fitting data in the Sudoku task, and the broken lines indicate fitting data in the RDM task. The data can be found in S1 Data. dACC, dorsal anterior cingulate cortex; lFPC, lateral frontopolar cortex; RDM, random-dot motion.

https://doi.org/10.1371/journal.pbio.2004037.g006

Subsystems in the metacognition network

Thus far, we have shown that the neural system of metacognition could be dissociated into at least two subsystems: the dACC and AIC regions involved in metacognitive monitoring of decision uncertainty, and the lFPC region involved in metacognitive control of decision adjustment. To further elaborate the subsystems of the metacognition network, we performed analyses of interregional functional connectivity in the metacognition network. By regressing out the mean activity and the modulations by the decision uncertainty level, the RT and the extent of uncertainty reduction, as well as their interactions, we calculated trial-by-trial correlations between each pair of regions in the metacognition network (see Materials and methods). The interregional functional connectivity patterns in both the task condition (Fig 7A) and the control condition (Fig 7B) were almost identical across the two types of tasks and were also similar to those at the resting state (Fig 7C). The interregional functional connectivity patterns consistently showed that the metacognition network might be divided into three subsystems: the lFPC region; the dACC and AIC regions; and the DLPFC and aIPL regions. The interregional functional connectivity within each of the subsystems was systematically stronger than that across the subsystems (paired t test, P < 0.05 in all comparisons). So far, the functional roles of the subsystem consisting of the DLPFC and aIPL regions in metacognition remain unclear. It is worth noting that the functional connectivity between the dACC and the regions of the other two subsystems in the task conditions was numerically stronger than the corresponding one at the resting state but was not statistically significant.

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Fig 7. Regional functional connectivity of the metacognition network during (A) task and (B) control conditions in Sudoku and RDM tasks, as well as during (C) resting state.

dACC, dorsal anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; lAIC, left anterior insular cortex; laIPL, left anterior inferior parietal lobule; lFPC, lateral frontopolar cortex; rAIC, right anterior insular cortex; raIPL, right anterior inferior parietal lobule; RDM, random-dot motion; rFPC, right frontopolar cortex.

https://doi.org/10.1371/journal.pbio.2004037.g007

Ethics statement

All participants were university students, who were recruited through a campus bulletin board system (BBS). Informed consent was obtained from each individual participant in accordance with a protocol approved by Beijing Normal University Research Ethics Committee.

Participants

Twenty-one participants (19–33 y old, 12 female) took part in the main fMRI experiment (fMRI1) and the resting fMRI experiment. Out of them, 16 participants (19–33 y old, 9 female) took part in all sessions of the repeated behavioral experiments. In addition, 17 participants (19–25 y old, 10 female) took part in the second fMRI experiment (fMRI2), 25 participants (19–27 y old, 14 female) took part in the third fMRI experiment (fMRI3), and 20 participants (19–26 y old, 11 female) took part in the fourth fMRI experiment (fMRI4).

RDM task

In an aperture with a radius of 3 degrees (visual angle), about 300 white dots (radius: 0.08 degrees; density: 2.0%) on a black background that were moving in different directions at a speed of 8.0 degrees/s were displayed. The time span of the movement of each dot lasted for 3 frames. A portion of dots was moving in the same direction (one of the four directions: left, right, up, and down), while the others were moving in different, random directions. The participant was required to discriminate the net motion direction. According to the proportion of coherently moving dots, discrimination difficulty was classified into 10 levels (Fig 2B), for which the movement coherence varied from 1.6% to 51.2%; coherence of moving dots in the control condition was 100%.

Sudoku task

To fill a 4 × 4 grid matrix, each digital number from 1 to 4 should be filled in once and only once in each column, each row, and each corner with 4 grids. The task used in the present study required participants to fill in a target grid with a digital number from 1 to 4 in a partially completed Sudoku problem. Each problem had a unique solution. A Sudoku generator (custom codes) was used to create thousands of different Sudoku problems. Problem difficulty was classified into 10 levels according to the minimum number of logic operation steps required to arrive at the solution; this classification scheme largely matched with participants’ reported subjective difficulty level (Fig 2B). In the control condition, the presented problem was made up of symbols (“#”) replacing the digital numbers except for the space in the target grid where the digital number was illustrated. Thus, the participant only needed to press the corresponding button.

Learning procedure

Participants were trained in the cognitive skills used to solve the 4 × 4 Sudoku problems under experimenters’ guidance for at least two h per d over a continuous span of 4 d. The participants practiced solving problems with no time constraints first in two to four runs of 40 problems at a set difficulty level. Once the average accuracy of a session crossed 90%, s/he then practiced solving problems at the same level within 2 s. Once the average accuracy of the run in a 2-s time frame reached 70%, the participant repeated these steps at the next level of difficulty. After the 4-d intensive training program, each participant had attained high-level proficiency in solving 4 × 4 Sudoku problems in 2 s, as the mean task difficulty finally approached the fifth level.

Task sequences

The sequences of both the Sudoku and RDM tasks were identical. In fMRI1, each trial started with a green-cross cue to indicate that the task stimulus would be presented 1 s later. The stimulus was presented for 2 s, then four options were presented, and the participant made a choice within 2 s. After a choice was made, four confidence level ratings from 1 (lowest) to 4 (highest) were presented, and the participant reported their confidence rating within 2 s. The same stimulus was immediately presented again for 4 s, and the participant again selected a choice and again reported their confidence rating. Each trial lasted for 15 s. The control trials were intermingled with task trials. The sequence of the control trials was identical to that of the task trials. In each task, there were 4 runs, and each run consisted of 30 task trials and 10 control trials. The task difficulty of each trial was adjusted by a staircase procedure through which one level was upgraded after two consecutive correct trials, was downgraded by one level after two consecutive erroneous trials, and was kept at the same level otherwise, so that the mean accuracy converged at about 50%. Prior to each experiment, two runs were carried out to allow each participant to practice and stabilize performance. The Sudoku problems used in the learning and practice sessions were different from those used in the fMRI and behavioral experiments. In addition, a 10-min resting fMRI experiment was conducted when the participant was in a resting state with eyes opened.

The second fMRI experiment (fMRI2, Fig 4 and S1C Fig) was carried out to examine whether the metacognition network would also be essentially involved in the decision-making process in the initial decision if a new Sudoku problem or new RDM stimulus was presented during the redecision phase, following a control stimulus presentation in the initial decision phase, as used in fMRI1. In fMRI2, a randomized selection of a control stimulus, a new Sudoku problem, or a new RDM stimulus was presented in the redecision phase. The appearance of a new stimulus in the redecision phase occurred in half of the trials. The Sudoku problems or RDM stimuli used in this experiment were selected from those at the middle level of task difficulty. This design was used to reduce the participant’s decision uncertainty in this experiment by controlling the difficulty of the task. In all other ways, the task sequence was the same as that used in fMRI1. In total, there were 120 trials across two runs in each task.

The third fMRI experiment (fMRI3, Fig 3D and S1E Fig) was carried out to compare brain activity in a redecision-task condition (like the task in fMRI1) to activity in a task that did not require redecision. In non-redecision trials, a control stimulus was presented in the redecision phase, therefore no redecision was required. The task sequence was the same as the design used in fMRI1 with the exception that the presentation time of the stimulus was 3 s during the redecision phase. In each task, the ‘redecision’ condition and the ‘non-redecision’ condition were randomized, and each consisted of 60 trials across 3 runs.

The fourth fMRI experiment (fMRI4, S4 Fig) was carried out to confirm that the engagement of metacognition was independent of the duration of redecision. The task sequence was exactly the same as was used in fMRI1, with the exception that the redecision phase was 2 s instead of 4 s. Only the RDM task was used in this experiment. In total, there were 120 task trials and 40 control trials across 4 runs.

Stimuli presentation

In the fMRI experiments, the participants viewed images of the stimuli on a rear-projection screen through a mirror (resolution, 1,024 × 768 pixels; refresh rate, 60 Hz). Normal or corrected-to-normal vision was achieved for each participant. All images were restricted to 3 degrees surrounding the fixation cross.

fMRI experiments

All fMRI experiments were conducted using a 3 T Siemens Trio MRI system with a 12-channel head coil (Siemens, Germany) after the 4-d Sudoku training. Functional images were acquired with a single-shot gradient echo T₂* echo-planar imaging (EPI) sequence with volume repetition time (TR) of 2 s, echo time (TE) of 30 ms, slice thickness of 3.0 mm, and in-plane resolution of 3.0 × 3.0 mm² (field of view [FOV]: 19.2 × 19.2 cm²; flip angle [FA]: 90 degrees). Thirty-eight axial slices were taken, with interleaved acquisition, parallel to the anterior commissure–posterior commissure (AC–PC) line.

Behavioral experiments

To test the reliability of the participants’ metacognitive abilities, behavioral experiments were carried out using the same paradigms as the Sudoku and RDM tasks. Each of the participants completed 6 sessions of behavioral experiments on consecutive days. Each session was composed of 4 runs of the Sudoku task and 4 runs of the RDM task, the same as was used in fMRI1.

Behavioral data analyses

A nonparametric approach was employed to assess each participant’s uncertainty sensitivity. The ROC curve was constructed by characterizing the incorrect probabilities with different uncertainty levels for initial decisions as thresholds. The area under curve (AUC) was calculated to represent how well the participant was at detecting and rating their decision uncertainty [9]. The individual uncertainty bias was estimated by the mean uncertainty level of each session, regressed out the factor of A_roc. The accuracy change was the change in mean accuracy from the first decision to the second decision. The individual uncertainty sensitivity and uncertainty bias, as well as accuracy change, were calculated for each session of the fMRI and behavioral experiments.

fMRI analyses

The analysis was conducted with FMRIB’s Software Library (FSL) [51]. To correct for the rigid head motion, all EPI images were realigned to the first volume of the first scan. Data sets in which the translation motions were larger than 2.0 mm or the rotation motions were larger than 1.0 degree were discarded. It turned out that no data had to be discarded in the fMRI experiments. The EPI images were first aligned to individual high-resolution structural images and were then transformed to the Montreal Neurological Institute space by using affine registration with 6 degrees of freedom and resampling the data with a resolution of 2 × 2 × 2 mm³. A spatial smoothing with a 4-mm Gaussian kernel (full-width at half-maximum) and a high-pass temporal filtering with a cutoff of 0.005 Hz were applied to all fMRI data.

Each trial in fMRI1 was modeled with three regressors. The first regressor represented the decision-making process in the initial decision, which was time-locked to the onset of the first stimuli presentation, with summation of the presentation time (2 s) and the differential RT from the mean RT of control trials as the event duration. The second regressor represented the neural process following the initial decision, including the metacognition process and the decision-making process in the redecision, and was time-locked to the onset of the first confidence judgment—with summation of the confidence report, the second presentation time (4 s) of the stimuli, and the differential RT from the mean RT of control trials as the event duration. The third regressor represented the baseline during the intertrial intervals (ITIs), time-locked to the onset of ITI, with the ITI duration as the event duration. The uncertainty level of the initial decision, the RT, and the level of uncertainty reduction (differences in the uncertainty level between the final decision and the initial decision) were implemented as modulators of the second regressor by demeaning the variances of the uncertainty level (Fig 3C) and consequently orthogonalizing the RT and the level of uncertainty reduction with each other (Fig 3A–3C and S1I Fig), or reversing the orthogonalization order (S1J Fig). It should be noted that the orthogonalization processes were equal to stepwise regression analyses on these covariates. The same analyses were applied to the fMRI data of fMRI2 and fMRI3.

For group-level analysis, we used FMRIB’s local analysis of mixed effects (FLAME), which model both “fixed effects” of within-participant variance and ‘random effects’ of between-participant variance using Gaussian random-field theory. Statistical parametric maps were generated by a threshold with P < 0.05 with false discovery rate (FDR) correction, unless noted otherwise. The regressions of the individual uncertainty sensitivity (A_ROC), the individual RT–uncertainty correlation coefficient, the individual mean uncertainty level, and the individual accuracy change with the β weights of uncertainty levels (Figs 5B, 5D and 6C)—or with the task baseline activity (Fig 6C–6E)—were calculated at the third level of group analyses. For these analyses, statistical parametric maps were generated by a threshold of P < 0.001 with the cluster-size threshold as 20 (family-wise error correction).

ROI analyses

The ROIs of the metacognition network were defined by the voxels that were significantly activated during the redecision phase in the task trials compared to those during the same phase in the control trials across both tasks using conjunction analysis (P < 0.001, cluster-wise correction; green areas in statistical parametric maps). ROI analyses were obtained from both hemispheres of the same region. The VS ROI was anatomically defined by the striatum atlas of FSL templates [52]. The time courses were derived from the ROIs, calculating a mean time course within an ROI in each participant individually. We then averaged the time courses of the same condition across the participants (S2 and S3 Figs); or, we oversampled the time course by 10 and created epochs from the beginning of an event onward, then applied the corresponding GLM to every pseudo-sampled time point separately. By averaging the β weights across participants, we created the time courses shown in Fig 4. SEMs were calculated between participants.

PPI analysis

The PPI analysis (Fig 4D) was conducted with the demeaned VS time courses after removing the mean activity and the component correlated with the uncertainty level as the physiological factor, and the uncertainty level convolved with the canonical hemodynamic response function (HRF) during the redecision phase as the psychological factor. The two factors per se, and the interaction between the two factors as confound regressors, were put together into a new GLM analysis across the whole brain.

Functional connectivity analyses

Functional connectivity analyses were independently conducted for the task and resting fMRI data. For the task fMRI data in each ROI, the mean activity and the components associated with the uncertainty level, RT, level of uncertainty reduction, and their interactions were regressed out; the residual time courses were then averaged across the voxels of the region and segmented into the individual trials of the task and control conditions in the Sudoku and RDM task, respectively. The segmented data of each trial were then modeled using a single regressor during the redecision phase convolved with the canonical HRF, and then a regression value was obtained for each trial. The correlation coefficient of the regression values between each pair of the ROIs in the metacognition network was calculated across the trials of the task or control condition in each participant. Finally, the averaged correlation coefficients were shown (Fig 7A and 7B). For the resting fMRI data, the standard processing was carried out [53], and the averaged correlation coefficients were shown (Fig 7C).