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Metacognition and mentalizing are associated with distinct neural representations of decision uncertainty

['Shaohan Jiang', 'State Key Laboratory Of Cognitive Neuroscience', 'Learning', 'Idg Mcgovern Institute For Brain Research', 'Beijing Normal University', 'Beijing', 'Sidong Wang', 'Xiaohong Wan']

Date: 2022-05

Metacognition and mentalizing are both associated with meta-level mental state representations. Conventionally, metacognition refers to monitoring one’s own cognitive processes, while mentalizing refers to monitoring others’ cognitive processes. However, this self-other dichotomy is insufficient to delineate the 2 high-level mental processes. We here used functional magnetic resonance imaging (fMRI) to systematically investigate the neural representations of different levels of decision uncertainty in monitoring different targets (the current self, the past self [PS], and others) performing a perceptual decision-making task. Our results reveal diverse formats of internal mental state representations of decision uncertainty in mentalizing, separate from the associations with external cue information. External cue information was commonly represented in the right inferior parietal lobe (IPL) across the mentalizing tasks. However, the internal mental states of decision uncertainty attributed to others were uniquely represented in the dorsomedial prefrontal cortex (dmPFC), rather than the temporoparietal junction (TPJ) that also represented the object-level mental states of decision inaccuracy attributed to others. Further, the object-level and meta-level mental states of decision uncertainty, when attributed to the PS, were represented in the precuneus and the lateral frontopolar cortex (lFPC), respectively. In contrast, the dorsal anterior cingulate cortex (dACC) represented currently experienced decision uncertainty in metacognition, and also uncertainty about the estimated decision uncertainty (estimate uncertainty), but not the estimated decision uncertainty per se in mentalizing. Hence, our findings identify neural signatures to clearly delineate metacognition and mentalizing and further imply distinct neural computations on internal mental states of decision uncertainty during metacognition and mentalizing.

Funding: This research was funded by the Key Program for International S&T Cooperation Projects of China (MOST, 2016YFE0129100, X.W.), the National Natural Science Foundation of China (No. 31471068, X.W.), the Fundamental Research Funds for the Central Universities (2017EYT33, X.W.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In the current study, we aimed to delineate the neural representations of decision uncertainty attributed to different target participants: the current self, the PS, and others. To do so, we adapted a task paradigm often used in metacognition to apply to mentalizing. By means of such task alignments, we could compare the 2 mental processes in a similar task context. Further, we could compare the different mentalizing processes in the same task context, to specify whether the mental state representations are shared or segregated in attributing to different targets. That is, whether it is the target that matters (different neural signatures despite of similar computations) or the computation that matters (same neural signatures despite of different targets). We segregated decision uncertainty into 2 dissociated components—associations with external cue information and internal mental states unassociated with external cue information. We took the residuals after regressing out external cue information from decision uncertainty reported by the participants as a proxy of internal mental states in each task. We used functional magnetic resonance imaging (fMRI) to separately characterize the neural correlates of external cue information and residuals in attributing decision uncertainty to others and the PS in mentalizing and to the current self in metacognition. Our results reveal diverse representations of internal mental states in the mentalizing tasks, but a general format of internal mental state representations in metacognition.

Inferences of others’ mental states in mentalizing are often made under social contexts with external cue information. The mental states attributed to others might be inferred through object-level associations between external cue information and covert mental states. For example, inferring decision uncertainty from others’ hesitations in responses (i.e., reaction times), rather than on the basis of others’ metacognitive abilities. Associating external cue information with covert mental states may also lead to predict others’ performance. Thereby, it is difficult to discern the underlying cognitive processes merely from the observed behaviors [ 23 – 26 ]. Because of this ambiguity, to date, it remains unclear whether or not nonhuman primates can mentalize, namely creating a mental model simulating others’ mental world and generating internal mental state representations (i.e., theory of mind, ToM) [ 23 – 26 ]. To demonstrate that this mentalizing capability exists in humans or animals, one approach is to identify internal mental state representations that is unassociated with external cue information. Although the neural correlates of external cue information might not merely comprise the cue associations, the existence of neural signatures of internal mental state representations should undoubtedly endorse mentalizing.

We then look to neural signatures to delineate metacognition and mentalizing. Surprisingly, although a number of disparate studies on the neural mechanisms of metacognition and mentalizing have been conducted in cognitive neuroscience [ 15 – 17 ] and social neuroscience [ 18 , 19 ], respectively, a direct comparison of the 2 neural processes is so far lacking. This unusual situation might be primarily due to the lack of an appropriate experimental paradigm applicable for both processes. The mental state that is mainly concerned in studies of metacognition is decision uncertainty or decision confidence. Decision uncertainty is the opposite of decision confidence where decision confidence is a belief about that one’s own decision is correct. Decision uncertainty serves as a control signal to improve one’s decision even with no external feedback [ 15 , 20 ]. If a higher level of decision uncertainty is retrospectively monitored, then more cognitive control is consequently evoked. On the other hand, decision uncertainty also serves as a critical social control signal for efficacious decision improvement in joint decision-making [ 21 , 22 ]. Hence, it is of great importance to understand mental state representations of decision uncertainty in metacognition and mentalizing.

The observer often needs to estimate others’ covert cognitive states from external cue information during social interactions, e.g., to infer others’ decision uncertainty by observing their task performance. According to the framework of metacognition, others’ cognitive states can be hierarchically divided into meta-level and object-level states: The metal-level states reflect internal monitor on their object-level states. Accordingly, the mentalizing processes in monitoring others’ object-level and meta-level performance might be differential (right side). Occasionally, we may estimate the PS momentary cognitive states (left side). The mental state representations might be different in attributing different cognitive states to different target participants. PS, past self.

However, the self-other dichotomy on the target agents is insufficient to discern the 2 processes. First, similar to attributing mental states to others, the momentary mental states of the past self (PS) cannot be concurrently experienced by inspection as in metacognition, but are inferred from the available external cue information (e.g., facial expressions) or from the episodic memory cued by the external information. It thus becomes ambiguous whether such mental state representations in attributing to the PS should be classified as metacognition or mentalizing ( Fig 1 ). Second, one’s own mental states are hierarchically categorized into type 1 (object level) and type 2 (meta level) mental states in metacognition [ 10 ]. The meta-level mental states about the mental world are the signals generated during monitoring the object-level mental states in response to the physical world. For example, the belief (meta level) about whether one’s decision is correct (object level). Accordingly, the mental states attributed to others in mentalizing could also be hierarchically categorized into 2 levels concerned with object-level and meta-level performance, respectively ( Fig 1 ). Thereby, the representations of the 2-level mental states even attributed to the same target in mentalizing might be different. Third, in attributing the object-level mental states to others or the PS, the meta-level mental states are actually generated by the observer, rather than the target participants. In this sense, the meta-level mental state representations appear more similar to those in metacognition than mentalizing. Hence, the distinctions between metacognition and mentalizing along the self-other dichotomy remain considerably ambiguous.

A principal criterion conventionally used to distinguish nonsocial activities from social activities is whether the activities are conducted toward the self or others [ 6 – 9 ]. A corresponding distinction is also drawn on mental state attributing processes: Monitoring one’s own cognitive processes is referred to as metacognition [ 10 ], but when the target participant is an intentional agent other than the self, it is referred to as mentalizing [ 11 – 13 ]. Although both metacognition and mentalizing involve meta-representations of the mental worlds [ 1 – 3 ], the representational formats and the sources of the mental states differ. Critically, mentalizing necessitates others’ perspective taking to infer their mental states [ 14 ], while one’s own mental states are directly accessible by inspection [ 1 , 2 ].

Humans are social beings. We interact with others not only in the physical world but also in the mental world. Differing from objects in the physical world, humans are free and intentional agents who hold mental states that are not necessarily reflections of reality in the physical world. If there are a thousand readers, there must be a thousand Hamlets. Even though the physical world is same, the readers’ mental worlds are different from one another. The human brain thus needs to concurrently represent different mental states in the mental worlds of both the self and others during social interactions [ 1 – 3 ]. Failures of normal development of such an ability may cause deficits in human cognition and behaviors, e.g., in autism spectrum disorder (ASD) and schizophrenia [ 4 , 5 ]. Thus, it is a central question in psychology and neuroscience to understand the mechanisms of human mental state representations.

Results

Task paradigm We carried out 3 fMRI experiments to investigate the mental state representations of decision uncertainty in metacognition and mentalizing (Fig 2A; S1 Fig). A total of 28 healthy participants took part in all of the 3 experiments (see Methods). In experiment 1, each participant judged the gross motion direction of random moving dots and rated his/her uncertainty about the preceding decision (Fig 2B). There were 4 different task difficulty levels randomly mixed in the task (S2 Fig). Hereafter, this perceptual decision-making task was referred to as the random dots motion (RDM) task (Fig 2A). The participant reported the current-self decision uncertainty (CS-DU), immediately accompanying perceptual decision-making in each trial. PPT PowerPoint slide

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TIFF original image Download: Fig 2. Task paradigms and behavioral results. (a) The fMRI experimental setup. During the metacognition task, the participant inside the MRI scanner did the metacognition task alone. During the mentalizing tasks, the participant inside the MRI scanner observed the task performance on the metacognition task done by the target participant who was outside the scanner. (b) The metacognition task: The participant completed the RDM task and reported his/her CS-DU. (c) The type 1 mentalizing tasks: The participant observed the RDM task performance by a target participant and reported the target participant’s decision inaccuracy. The target participant was either an AO-DI or the PS-DI. (d) The type 2 mentalizing task: Instead of judging the target participants’ decision inaccuracy, the participants estimated decision uncertainty that would be concurrently reported by the AO/PS in the current trial (AO-DU/PS-DU). (e) The decision inaccuracy changed with task difficulty and RT in the AO-DU task, averaged across participants (n = 28). (f) Theoretically, decision inaccuracy is a sigmoid function of task difficulty and RT on each trial. Each target participant has unique internal noise (σ 1 ) in perceptual decision-making that causes different variances in decision inaccuracy. (g) Theoretically, in estimating the target participant’s decision uncertainty, the unique internal noise (σ 2 ) needs to be further considered in mapping decision inaccuracy to decision uncertainty. (h) Theoretically, different levels of internal noise (σ 2 ) cause different metacognitive abilities (AUROC). (i) The regression beta values of the normalized (task) difficulty and RT with the estimates in each task. The weights did not differ across mentalizing tasks (ANOVA, task difficulty: F [3,112] = 0.11, P = 0.95; RT: F [3,112] = 0.16, P = 0.92), but significantly differ from the metacognition task (2-tailed paired t test, task difficulty: t 27 = 6.2; P = 2.5 × 10−8; RT: t 27 = 4.1; P = 5.9 × 10−5). (j) The correlation between the estimated decision inaccuracy/uncertainty in the mentalizing tasks with the target participant’s actual decision uncertainty reported in the metacognition task, before (original: ANOVA, F [3,112] = 0.28, P = 0.84) and after (residual: Ps > 0.30) the associations with external cue information were regressed out. (k) The consistency between the estimated decision inaccuracy/uncertainty and the actual decision outcome (true or false) measured by the AUROC, before (original: ANOVA, F [4,139] = 1.22, P = 0.31) and after (residual: Ps > 0.20) the associations with external cue information were regressed out. (l) The ratio of estimate residuals to the total estimate variances (2-tailed t test, AO: t 27 = 2.5; P = 0.0096 in the contrast between the type 2 and type 1 mentalizing tasks; PS: t 27 = 2.1; P = 0.023 in the contrast between the type 2 and type 1 mentalizing tasks; 2-tailed paired t test, t 27 = 3.5; P = 3.3 × 10−4 in the contrast between the metacognition task and the mentalizing tasks). The error bars represent SEM across participants. *P < 0.05; **P < 0.01; ***P < 0.001, after Bonferroni correction. The raw data for Fig 2 can be found in the Supporting information as S1 Data. ANOVA, analysis of variance; AO-DI, anonymous other decision inaccuracy; AUROC, area under the ROC curve; CS-DU, current-self decision uncertainty; fMRI, functional magnetic resonance imaging; PS-DI, past-self decision inaccuracy; RDM, random dots motion; RT, reaction time; SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3001301.g002 In experiment 2, the participant inside the scanner observed an anonymous other (AO) concurrently performing the RDM task outside the scanner and judged the AO’s decision inaccuracy (AO-DI). Decision inaccuracy is the opposite of decision accuracy where decision accuracy is the objective probability that the AO’s decision is correct (Fig 2C). Differing from the metacognition task (CS-DU), the partner’s cognitive states were inaccessible. It thus necessitated the participant to infer the probability that the partner’s decision was correct. To avoid evoking the participant’s own decision uncertainty, the stimuli presented to the participant were noiseless: Only coherently moving dots were moving, whereas randomly moving dots remained stationary. By virtue of this altered stimulus presentation, the participant could perceive the task difficulty without evoking his/her own decision uncertainty (Fig 2C). This is a necessary condition to dissociate the neural representations of decision uncertainty in mentalizing from those in metacognition. Otherwise, the participant might use his/her own decision uncertainty to estimate the partner’s decision uncertainty. The partner’s response time (RT) was reported to the participant by a progress color bar, whereas neither the choice nor the reported decision uncertainty by the partner was presented to the participant. Hence, the participant could only use the external cue information of task difficulty and RT to estimate the partner’s decision inaccuracy. In a parallel task, the participant instead observed task performance on the metacognition task done past by himself/herself and judged the past-self decision inaccuracy (PS-DI). Otherwise, the experimental procedure was identical to the AO-DI task. Notably, as the past decision-making processes by oneself were also inaccessible and the past mental states associated with similar stimuli were impossible to explicitly memorize, the underlying cognitive process might also be mentalizing. We therefore refer to the 2 tasks as the type 1 mentalizing tasks. In experiment 3 (Fig 2D), the experimental procedure was identical to experiment 2, but the participant estimated the target participants’ mental states of decision uncertainty in each trial, namely the participant estimated the target participants’ believes about whether their own decisions were correct. The 2 tasks thus also entailed mentalizing to attribute type 2 mental states of decision uncertainty to the AO/PS (AO-DU and PS-DU). We therefore refer to the 2 tasks as the type 2 mentalizing tasks. Experiment 1 and experiment 3 were conducted in the same session, but experiment 2 was conducted in another session. To reduce confusion between type 1 and type 2 mentalizing tasks, the 2 sessions were separated at least over 2 weeks. The tasks in each session were randomly interleaved and were counterbalanced across the participants. The task sequences of the 4 mentalizing tasks were identical, only the instructions differed. Thereby, any behavioral and neural differences between them should be caused by different mentalizing processes. The task sequences of the metacognition task and the mentalizing tasks were also quite similar. However, the differences between the 2 types of tasks existed in both the perception phase and the judgment phase. Here, however, we are not so much concerned with the former but with the latter phase. In particular, the participant currently experienced decision uncertainty accompanying perceptual decision-making in the metacognition task, but inferred decision uncertainty that was not concurrently experienced from the cue information in the mentalizing tasks. However, because the perception phase and the judgment phase were temporally close to each other, it might be argued that any difference in neural signatures is due to the difference in the stimulus presentation as opposed to the difference between the metacognitive and metalizing processes. To confirm whether the neural correlates between the 2 phases are separable, we made analyses on the simulated fMRI signals generated by the same task sequence. Our simulation analyses demonstrated that the neural correlates of decision uncertainty in the perception phase or the judgment phase could be dissociated by conventional general linear models (GLMs) (S3A Fig).

Hierarchical mental state representations of decision inaccuracy and decision uncertainty in mentalizing To assess behavioral metrics used for data analyses, we made theoretical analyses on mental state representations of decision inaccuracy and decision uncertainty in mentalizing. According to the decision-making theory [27], decision inaccuracy is crucially dependent on both task difficulty and RT. The higher the task difficulty and the longer the RT, the higher the decision inaccuracy (Fig 2E). For the sake of simplicity, decision inaccuracy is assumed to be a sigmoid function of task difficulty and RT (Eq 1 in Methods; Fig 2F). Hence, it is plausible to estimate decision inaccuracy and decision uncertainty in the mentalizing tasks merely from external information provided by task difficulty and RT. However, one indispensable process to distinguish social inferences in mentalizing from nonsocial inferences or associations is taking the target participant’s perspective. For example, in the type 1 mentalizing tasks, the participants should consider that the target participant has unique internal noise (σ 1 ) during the perceptual decision-making process as described by the drift-diffusion model [27], which affects the target participant’s object-level performance (i.e., decision inaccuracy, Fig 2F). In the type 2 mentalizing tasks, the participant should further consider that the target participant has unique internal noise (σ 2 ) in mapping decision inaccuracy to decision uncertainty (Fig 2G), which renders the target participant’s unique metacognitive ability even with the same object-level performance [28] (i.e., a low variance results in a high metacognitive ability). We constructed the receiver operating characteristic (ROC) curve by using the level of decision uncertainty as the criterion to judge the incorrectness of the choice in each trial and measured the metacognitive ability as the area under the ROC curve (AUROC), indicating the extent to which the subjective uncertainty ratings matched the actual decision inaccuracy [28] (Fig 2H). Taken together, the internal mental state representations of decision inaccuracy and decision uncertainty in mentalizing should be hierarchically organized. However, due to the lack of feedback in the mentalizing tasks, each participant did not learn about the target participants’ (even for the PS) object-level and meta-level performance. Therefore, the internal information generated by mentalizing may not reflect the target participant’s actual internal mental states.

Behavioral results We analyzed the behavioral data in the experiments to assess how the participants used the associations with external cue information of task difficulty and RT to estimate the targets’ decision inaccuracy and decision uncertainty. The analyses showed that the weights of normalized task difficulty and RT (Eq 1 in Methods) on the estimates were equivalent [analysis of variance (ANOVA), task difficulty: F [3,112] = 0.11, P = 0.95; RT: F [3,112] = 0.16, P = 0.92; Fig 2I] and were highly correlated across the mentalizing tasks (S4 Fig). Hence, the participants used such external cue information to estimate the corresponding mental states equally across the mentalizing tasks. Notably, the estimates in the mentalizing tasks relied more on task difficulty than RT (2-tailed paired t test, t 27 = 6.2; P = 2.5 × 10−8). On the contrary, the estimates of decision uncertainty in the metacognition task relied more on RT than task difficulty (2-tailed paired t test, t 27 = 4.1; P = 5.9 × 10−5; Fig 2I). This is likely due to the fact that stimulus coherence (by virtue of the nature of the experimental design) was clearly discerned in the mentalizing tasks, but it was hard to inversely infer the stimulus coherences in the metacognition task. Because of the stable associations with external cue information, the decision inaccuracy/uncertainty estimated by the participants in the mentalizing tasks was correlated with what the target participants had actually reported themselves at each level of difficulty and RT in the metacognition task (ANOVA, F [3,112] = 0.28, P = 0.84, Fig 2J). However, all these correlations in the mentalizing tasks disappeared after regressing out the associations with external cue information of task difficulty and RT from the estimates of decision inaccuracy/uncertainty reported by the participants (2-tailed t test, Ps > 0.30; Fig 2J). That is, estimate residuals in each mentalizing task did not further predict the actual decision uncertainty reported by the target participants in the metacognition task. Further, as the estimates of decision uncertainty in each task could largely predict the actual decision inaccuracy, we used the AUROC to characterize this consistency. On average, the AUROCs (mean: 0.67 to 0.71) were larger than the chance level that was calculated by shuffling the orders between the estimates and actual decision inaccuracy and showed no significant differences across all the tasks (ANOVA, F [4,139] = 1.22, P = 0.31, Fig 2K). After the associations with external cue information were regressed out, the residual AUROCs (measured by estimate residuals) were no longer significantly different from the chance level in each mentalizing task (2-tailed t test, Ps > 0.20), but it remained significant in the metacognition task (2-tailed t test, t 27 = 11.8; P = 3.6 × 10−12; Fig 2K). Estimate residuals in the metacognition task should reflect the subjective difficulty due to the trial-by-trial noises in internal neural processing that task difficulty (coherence) and RT could not explain [29]. Thereby, the residuals also considerably contributed to decision uncertainty in the metacognition task. In striking contrast, reliable estimates of decision inaccuracy/uncertainty in the mentalizing tasks were crucially dependent on external information provided by task difficulty and RT. Nonetheless, estimate residuals accounted for about half of the total variance of the estimates of decision inaccuracy/uncertainty in each mentalizing task (Fig 2L), although each ratio was much lower than that in the metacognition task (2-tailed paired t test, t 27 = 3.5; P = 3.3 × 10−4; Fig 2L). These estimate residuals might serve as a proxy of internal mental states that were generated through target participant’s perspective taking and were independent of external cue information. We thus used estimate residuals as the main behavioral metric to identify the neural representations of internal mental states. As indirect evidence, the variances of estimate residuals were significantly larger in the type 2 mentalizing tasks than the type 1 mentalizing tasks (2-tailed t test, AO: t 27 = 2.5; P = 0.0096; PS: t 27 = 2.1; P = 0.023). These extra variances might be generated by the additional process of the target participant’s meta-level perspective taking in the type 2 mentalizing tasks, as suggested by the hierarchical mental state representation model described above (Fig 2G and 2H).

Common neural representations of external cue information in mentalizing We first examined the neural representations of external cue information in mentalizing by analyzing the fMRI data acquired during the experiments. As external cue information of task difficulty and RT contributed to the estimates equally across the mentalizing tasks, we hypothesized that the neural representations of each type of external cue information might be shared across the mentalizing tasks. To test the hypothesis, we regressed the trial-by-trial fMRI activities during the judgment phase with external cue information of task difficulty and RT across the whole brain in each task (see Methods). Across the mentalizing tasks, the fMRI activities in the primary visual cortex (V1) were negatively correlated with the levels of task difficulty (the conjunction analysis, z > 2.6, P < 0.05 after cluster-level family-wise error (FWE) correction; Fig 3A), decreasing as the number of moving dots was reduced (task difficulty increased). On the other hand, the fMRI activities in the right inferior parietal lobe (IPL) were positively correlated with the levels of task difficulty (the conjunction analysis, z > 2.6, P < 0.05 after cluster-level FWE correction; Fig 3A). In contrast, the fMRI activities in a wide range of brain regions were positively correlated with the levels of RTs (the conjunction analysis, z > 2.6, P < 0.05 after cluster-level FWE correction; Fig 3B). Among these brain regions, the right IPL region overlapped with the regions associated with task difficulty: The same right IPL region responded to both task difficulty and RT across the mentalizing tasks (Fig 3C and 3D). Thus, integration of the 2 pieces of external information together in the right IPL partially contributed to the estimates of decision inaccuracy/uncertainty in mentalizing. PPT PowerPoint slide

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TIFF original image Download: Fig 3. Common neural representations of external cue information across the mentalizing tasks. (a) The activation maps for the activities significantly correlated with task difficulty in a conjunction analysis across the mentalizing tasks (z > 2.6, P < 0.05 after cluster-level FWE correction). (b) The activation maps for the activities significantly correlated with RT in a conjunction analysis across the mentalizing tasks (z > 2.6, P < 0.05 after cluster-level FWE correction). The activation maps in (a) and (b) were displayed in radiological convention (the left/right side of the image corresponds to the right/left side of the brain). (c) The parametric regression beta values of task difficulty in each mentalizing task in the right IPL ROI defined by the conjunction of (a) and (b). (d) The beta values of RT in each mentalizing task in the right IPL ROI defined by the conjunction analysis of (a) and (b). The error bars represent SEM across participants. ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001, uncorrected. The raw data for Fig 3 can be found in the Supporting information as S1 Data. AO-DI, anonymous other decision inaccuracy; AO-DU, anonymous other decision uncertainty; FWE, family-wise error; IPL, inferior parietal lobe; PS-DI, past-self decision inaccuracy; PS-DU, past-self decision uncertainty; ROI, region of interest; RT, response time; SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3001301.g003

Neural representations of estimate residuals in metacognition In the metacognition task, estimate residuals were significantly correlated with the fMRI activities in the dACC and the lFPC (z > 3.1, P < 0.05 after cluster-level FWE correction, blue in Fig 4A; see also S1 Table), as repeatedly observed in previous studies [15–17,31,32]. Although the lFPC region was also associated with type 2 mentalizing (S6F Fig), the dACC region selectively represented estimate residuals in the metacognition task, but not in the mentalizing tasks (S6B Fig; the post hoc comparisons between the 2 types of tasks, P < 0.0031 after Bonferroni correction). Notably, the components of decision uncertainty associated with task difficulty and RT were also represented in the dACC (S6B Fig). Thus, the dACC uniformly represented all components of decision uncertainty in metacognition.

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