Most experimental studies of decision-making have examined situations in which only one less-predictable correct answer exists. With low-predictability, a low probability of reward or punishment can be associated with a stimulus, action, and/or outcome. In such cases, decision-making can be characterized by “uncertainty.” Platt and Huettel (2008) define the concept of uncertainty as the psychological state in which a decision maker lacks knowledge about what outcome will follow from either choice in decision-making. Experimentally, uncertainty has been operationalized as low-predictability using a probabilistic outcome (Volz et al., 2003, 2004, 2005; Delgado et al., 2005b; Knutson et al., 2005; Huettel, 2006; Tobler et al., 2007; Chandrasekhar et al., 2008; Preuschoff et al., 2008; Abler et al., 2009) or by a perceptual difficulty to judge (Heekeren et al., 2004; Grinband et al., 2006; Callan et al., 2009). Despite the low-predictability, these experimental situations subsume that one of the possible answers is correct. In these situations, participants must adjust their decision to comply with the externally defined sole correct answer.

For example, Volz et al. (2003) manipulated low-predictability by the probabilistic outcome. They examined brain activity during participants’ prediction of which of the two concurrently presented visual stimuli would win. Each of the pairings of figures was associated systematically with a particular probability of winning from 60 to 100% (e.g., B wins against C with a mean probability of 60%). In their experiment, participants were never given explicit information about these probabilities.

As the manipulation of low-predictability, Hsu et al. (2005) manipulated the predictability of the probabilities of different outcomes. They compared neural substrates of decision-making under risk (low-predictability outcomes with predictable probabilities) and ambiguity (low-predictability outcomes with unpredictable probabilities) which are two conditions in which the consequences of possible outcomes have low-predictability.

Not only the probabilistic outcome, perceptual difficulty to judge is also used to manipulate uncertainty (Heekeren et al., 2004; Grinband et al., 2006; Callan et al., 2009; Banko et al., 2011). For example, Heekeren et al. (2004) used face and house stimuli to which were added several levels of noise to manipulate the amount of sensory evidence in the stimuli. Participants were asked to decide whether a presented image was a face or a house. Although an objectively correct answer was presented, it was difficult to predict which judgment (house or face) was correct for the stimulus during simultaneous presentation of noise.

Results of these neuroimaging reports using probabilistic outcome and perceptual difficulty have typically shown increased activity within the dorsal part of the medial prefrontal cortex (DMPFC; Volz et al., 2003, 2004; Hsu et al., 2005; Knutson et al., 2005; Grinband et al., 2006; Krain et al., 2006; Callan et al., 2009; Mohr et al., 2010a), lateral prefrontal cortex (LPFC; Volz et al., 2003, 2004; Heekeren et al., 2004; Hsu et al., 2005; Krain et al., 2006; Abler et al., 2009; Callan et al., 2009), orbitofrontal cortex (Hsu et al., 2005; Tobler et al., 2007; Abler et al., 2009), insula (Volz et al., 2003, 2004; Heekeren et al., 2004; Knutson et al., 2005; Grinband et al., 2006; Krain et al., 2006; Callan et al., 2009; Mohr et al., 2010a), and thalamus (Volz et al., 2003; Heekeren et al., 2004; Grinband et al., 2006; Krain et al., 2006; Callan et al., 2009; Mohr et al., 2010a).

The process of externally guided decision-making has generally been interpreted in the context of a reinforcement learning (RL) model. In that model, the expected value (i.e., the magnitude of outcome times the probability of outcome) biases the decision; the expected value is modified based on the prediction error (i.e., discrepancies between expected and actual rewards; e.g., O’Doherty et al., 2004; Tanaka et al., 2004; Kim et al., 2006; Yoshida and Ishii, 2006; Behrens et al., 2007; Cohen, 2007; Boorman et al., 2009; Glascher et al., 2009; Wunderlich et al., 2009).

Corresponding neural substrates to this model and related concepts have well been identified. The expected value is typically processed within the orbitofrontal cortex, amygdala, ventral striatum, and insula. Prediction error is related to the ventral striatum and the dorsal anterior cingulate cortex (dACC; Tanaka et al., 2004; Daw et al., 2006; Kim et al., 2006; Cohen, 2007; O’Doherty, 2007; Tom et al., 2007; Rolls et al., 2008; Glascher et al., 2009; Wunderlich et al., 2009, 2011).

Hampton et al. (2006) reported results suggesting an important limitation of the RL model. They sought to ascertain whether the use of stored knowledge of the task structure guides choice or whether learned values guide choice without assuming a higher-order structure, as in the standard RL model. A computational model of standard RL model and another model that exploits knowledge of a task structure for a probabilistic reversal learning task (i.e., when one action is “good” the other is “bad” and vice versa, as well as the rule that after a time the contingencies will reverse; structure-based model) were then constructed and fitted to both the behavioral and fMRI data.

The results revealed that neural activity in the ventral region of MPFC (VMPFC), the orbitofrontal cortex and the posterior dorsal amygdala were more consistent with the expected reward signal from a structure-based model than with that from an RL model.

Their results imply that the standard RL model is not always appropriate for the analysis of decision-making in the human brain. The limitation of the standard RL model was also pointed out by other studies (Daw et al., 2006; Hampton et al., 2008; Pearson et al., 2011).

Taken together, externally guided decision-making under uncertainty has been investigated mainly using a task with a probabilistic outcome or stimuli with perceptually difficult judging. Although the RL model has generally been used to interpret externally guided decision-making, it is also pointed out that the model cannot fully explain the brain functions for externally guided decision-making under uncertainty.

In addition to the probabilistic outcome and perceptual difficulty, an outcome that is varied (or believed to be varied) by other people’s decisions has been used in externally guided decision-making (e.g., trust game and prisoner’s dilemma game; Rilling et al., 2002, 2004, 2008a; Delgado et al., 2005b; Elliott et al., 2006; Sanfey, 2007; Frith and Singer, 2008; McCabe and Castel, 2008; Assaf et al., 2009; Wischniewski et al., 2009; Yoshida et al., 2010). Despite low-predictability on a social basis, experimental situations include the presumption that one of the possible answers is correct, and participants are required to adjust their choices to comply with an externally defined single correct answer. For that, one might consequently want to categorize tasks of these kinds, called neuroeconomic tasks, as involving externally guided decision-making.

The study by Gallagher et al. (2002) is a good example of an externally guided decision-making in a social situation. They studied brain activation in humans who played the game rock–scissors–paper against a human or a computer. The play of the “human” or the “computer” did not actually differ: they were random sequences.

In their experiment, greater activity was visible in the pregenual ACC (pACC) and MPFC when participants believed they were playing against a human as opposed to a computer. Similar observations have been obtained using neuroeconomic tasks of other kinds (prisoner’s dilemma game, Rilling et al., 2004; guessing task, Elliott et al., 2006; domino game, Assaf et al., 2009; and a beauty contest game, Coricelli and Nagel, 2009).

The control conditions of these experiments were non-social low-predictability decision-making (e.g., random sequences of outcomes), meaning that the differences between conditions were not uncertainty itself but were differences in the stance of the participants (i.e., playing against a person, or against a computer). For that reason, the observed brain activities when participants believe they are playing against another person compared to the control task have been inferred as reflecting the process of thinking about the mental state of that person (mentalizing; Frith and Frith, 1999; Frith and Singer, 2008).

Hampton et al. (2008) presented evidence that mentalizing has the function of guiding decision-making during game performance. They scanned human participants using fMRI while they played a repetitive inspection game in which employees decide whether to work or shirk at each trial and an employer decides whether or not to inspect the work area. In addition to a simple RL model, the following two computational models were used to analyze the behavioral and fMRI data: a fictitious model, which exploits prediction of the opponent’s next actions considering the history of prior actions by the opponent; and an influence model, which exploits not only tracking of the opponent’s actions but which also incorporates knowledge of how one’s own actions influence the opponent’s strategy.

As a result, the influence model provided a better fit to participants’ behavior than did either the fictitious model or the RL model. Regarding brain activity, results show that the expected reward signal from the influence model provides a better account of the neural data in MPFC than does that from a simple RL model. These results suggest that mentalizing engaged in MPFC affects reward prediction, and that it might be used to guide choice during game performance.

Collectively, these neuroeconomic researchers have examined the effects of social interaction in externally guided decision-making. Even if the outcome varied by other people’s decisions, a correct answer is determined externally, and participants are required to predict which option produces a better outcome in each trial. Different from the externally guided decision-making under uncertainty, however, the results from these neuroeconomic studies do not reflect uncertainty itself, but instead reflect the effects of social interaction. These reports described that signals in MPFC related to mentalizing have a function of biasing decision-making in a social situation to choose an externally determined correct option.

Uncertainty and social situations still presuppose some externally determined single correct answer, although that answer is chosen with low-predictability. How about the complete absence of one correct answer based on external circumstances, even when given no low-predictability choices? In such cases, we cannot rely on an externally determined objectively correct answer to choose and to regulate one’s own behavior, and the answer and its correctness depends on one’s own, i.e., internal, preferences rather than on circumstantial, i.e., external, criteria (Goldberg and Podell, 1999, 2000; Lieberman and Eisenberger, 2005; Volz et al., 2006; Nakao et al., 2009b).

Such situations are apparent in the context of moral decision-making (Moll et al., 2001, 2002, 2006; Zysset et al., 2002, 2003; Heekeren et al., 2003, 2005; Greene et al., 2004; Paulus and Frank, 2006; Schaich Borg et al., 2006; Greene and Paxton, 2009; Cikara et al., 2010; Hare et al., 2010; Sommer et al., 2010; Caspers et al., 2011; Kahane et al., 2011; Schleim et al., 2011). For instance, when requiring participants to decide about giving money to either themselves or to a charitable organization, the study by Moll et al. (2006) does not presume that either of the two options is correct. Here, the outcome indicating that the participant receives money (the good outcome in the case of the externally guided decision-making) is not necessarily a correct answer because, taking a more moral stance, the donation to the charitable organization might be regarded as the correct answer. While adopting the subject’s viewpoint and that person’s own financial interests, receiving the money (rather than giving it to charity) would be regarded as the correct answer. This choice entails that the decision (whether participant choose their behavior based on self-interest or moral) depends on criteria employed by the participant. Results demonstrate that costly decisions (choosing costly donation or costly opposition) were associated more closely with activation of the MPFC than pure reward decisions were.

A similar finding was also reported by Greene and Paxton (2009). They examined neural activity involved in participants’ decisions of whether to tell the truth or lie when reporting their success at predicting the outcome of coin flips. In this task, if participants report their success at the prediction, then they win the amount of money shown. In contrast, if they report their failure at the prediction, they lose the amount of money shown. In this task, lying to get the money is not a good choice from a moral viewpoint. Nevertheless, reporting the successful prediction is a good choice for obtaining money even if it is based on lying. Consequently, neither of the choices was the correct answer. The authors found DMPFC, LPFC, and right parietal lobe activity when dishonest people chose to tell the truth instead of lying for profit.

In addition to such moral decision-making, preference judgments are included in internally guided decision-making. In the preference judgment task, participants are required to make a decision based on personal criteria; the judgment is not based on external criteria. Preference judgments of many kinds have been used in previous studies: preference judgment for food (Arana et al., 2003; Paulus and Frank, 2003; Hare et al., 2009; Piech et al., 2009; Linder et al., 2010), products (Knutson et al., 2007, 2008), brands (Santos et al., 2011), faces (Kim et al., 2007; Chen et al., 2010), holiday options (Chaudhry et al., 2009), paintings (Jarcho et al., 2011), political beliefs (Zamboni et al., 2009), occupations (Nakao et al., 2009a, 2010c), task types (Forstmann et al., 2006), agencies of choice (Forstmann et al., 2008), shapes (Jacobsen et al., 2006), and colors (Goldberg and Podell, 1999, 2000; Johnson et al., 2005).

For instance, Paulus and Frank (2003) investigated brain activity during preference judgment for soft drinks. They presented two pictures of a soft drink in each trial. In preference judgment tasks, participants were asked to judge which drink they would like better. In the control task (visual discrimination task), stimuli were the same picture set with the preference judgment task. Then they were asked to identify which soft drink was in a bottle, a can, or a carton: the control task has an objective correct answer with no uncertain situation.

Analogously, Nakao et al. (2009a) used an occupational choice task (e.g., Which occupation do you think you could do better? – dancer or chemist) without an objectively correct answer and a word-length task (e.g., Which word is longer? – dentist or comedian) that has one certain correct answer. In the occupational choice task, participants were clearly instructed that there is neither an objectively correct answer nor a contingent outcome with each decision.

These preference judgment tasks typically show increased activity within the pACC, VMPFC, and posterior cingulate cortex (PCC) compared with the control task, which is the externally guided decision-making with a certain correct answer.

In sum, internally guided decision-making has been investigated in moral judgment and preference judgment studies. When compared with the judgment task with a clear objective correct answer, several neural substrates’ increased activity has been observed during internally guided decision-making. Although MPFC seems to be observed consistently in internally guided decision-making (Nakao et al., 2009b, 2010b), no previous report has described examination of which brain regions were activated consistently among internally guided decision-making studies using a quantitative approach. Furthermore, no report has described a study that has investigated the differences and similarities of neural substrates between the two kinds of decision-making representing real-life decision-making (i.e., internally guided decision-making and externally guided decision-making under uncertainty). For that purpose, we conducted the exploratory meta-analysis described hereinafter.

As externally guided decision-making studies, we included reports of studies using a task in which one choice was associated with a better outcome (e.g., reward) than others, indicating that the choice is correct. We also included studies using a task in which no feedback was presented, but for which the task has one objective correct answer and participants had to try to respond correctly (e.g., Heekeren et al., 2004; Hsu et al., 2005; Callan et al., 2009; Banko et al., 2011). For comparison with internally guided decision-making, we excluded reports of studies using a computational model that is not applicable to internally guided decision-making to analyze the fMRI data (e.g., RL model with incorporating the effect of the situation of low-predictability (task structure; Hampton et al., 2006, see review part for the details). We excluded neural activations that are specific to the feedback epoch and prediction error (e.g., Wittmann et al., 2008), which cannot be compared with internally guided decision-making.

For studies of internally guided decision-making, we included studies using tasks in which no stimulus or option was regarded as correct. Studies investigating differences of decision-making for problems with no correct answer from decision-making for problems with one correct answer were included. We excluded the contrasts which compared different kinds of internally guided decision-making (e.g., Schaich Borg et al., 2006; Hare et al., 2009; Sommer et al., 2010). We also did not include contrasts which compared different decisions in the internally guided decision-making (e.g., Sanfey et al., 2003; Greene et al., 2004). We excluded a study using a task that clearly requires participants to make judgments based on social criteria instead of the participants’ own criteria (Prehn et al., 2008).

Meta-analysis results indicated different neural representation patterns for externally guided decision-making (uncertainty; Figure 1A) and internally guided decision-making (Figure 1C; see also Table 2). Figure 2A presents results of statistical overlap as based on inclusive masking. Regions with significant proportions of activation for the externally guided decision-making were in DMPFC, dorsal LPFC (DLPFC), insula, thalamus, and IPL. For internally guided decision-making, the clusters in MPFC, pACC, PCC, and superior temporal gyrus (STG) were revealed. Only the DMPFC (BA 8) overlapped between decision-making of the two kinds. Although we refer to the overlapped region as DMPFC hereinafter, it is noteworthy that the same region (BA8) has been mentioned also as a part of the supplemental motor area (SMA; Caria et al., 2011) and pre-SMA (Rubia et al., 2001; Chen et al., 2010) in several previous studies.

Figure 3 presents results from the two difference maps as based on their respective contrasts [i.e., externally guided decision-making (uncertainty) < / > internally guided decision-making]. Although the extensions of the several clusters were restricted, the direct comparison showed (more or less) similar regions to those portrayed in Figure 1. Internally guided decision-making showed larger clusters in mainly medial cortical regions while externally guided decision-making showed stronger clusters in lateral regions (see also Table 3).

Figure 1B presents results of externally guided decision-making in a social situation. To observe the effect from social component included in internally guided decision-making, we mounted the MKDA results of externally guided decision-making (social) and internally guided decision-making on the same stereotaxic standard brain, and indicated the statistical overlaps (Figure 2B. DMPFC (BA8, 9) overlapped between social and internally guided decision-making. In contrast, no overlap was observed in the other regions observed in internally guided decision-making.

Before further discussion related to meta-analysis results, the difference of stimulus type (verbal or non-verbal) used in externally guided decision-making under uncertainty and in internally guided decision-making should be explained. Verbal stimuli tended to be used more in internally guided decision-making; non-verbal stimuli were used more in externally guided decision-making under uncertainty (see Table A1 in Appendix).

Based on the following four reasons, however, we conclude that the regions observed in our meta-analysis results were not attributable to the difference of stimulus type. First, in every study included in the present meta-analysis, stimuli of the same type with experimental conditions were used in control conditions. For that reason, the coordinates from these studies were not specific to the stimulus type itself, but were specific to uncertainty or absence of an objective correct answer. Second, previous meta-analytical studies of neural substrates for working memory (Owen et al., 2005) and associative learning (Chein and Schneider, 2005) demonstrated broadly similar activation patterns for verbal and non-verbal stimuli including the regions observed in externally guided decision-making under uncertainty. Third, regarding internally guided decision-making, studies included in our meta-analysis and which used non-verbal stimuli (Paulus and Frank, 2003; Johnson et al., 2005; Jacobsen et al., 2006; Chen et al., 2010; Hare et al., 2010) yielded results indicating similar neural substrates with our meta-analysis results. Fourth, although Kobayashi et al. (2007) observed similar brain regions with internally guided decision-making by their mentalizing task, no increased activities within these regions were observed using verbal stimuli compared to non-verbal stimuli.

We found no other significant difference between externally and internally guided decision-making with respect to the stimulus modality (visual or auditory), the sample size, the quality of the statistical analysis (corrected, uncorrected), and differences of reaction times between the experimental condition (uncertain, social, or internally guided) and control condition. Moreover, the reaction times in the experimental condition were not significantly longer than those in the control condition in either the externally guided decision-making under uncertainty or the internally guided decision-making. Based on these results, we conclude that the brain region observation results were not attributable to these factors.

In our meta-analysis results, the DMPFC–DLPFC–insula–thalamus–IPL network was activated consistently in externally guided decision-making under uncertainty. In contrast, VMPFC–pACC–PCC–STG network was activated in internally guided decision-making. This difference is similar to the distinction into two complementary networks, task-positive networks and task-negative networks, called default-mode networks (DMN; Fox et al., 2005; Broyd et al., 2009; Hampson et al., 2010; Kim et al., 2010; Northoff et al., 2010; Wu et al., 2011a). The task-positive network is known to be activated consistently during goal-directed/externally oriented cognitive tasks, and it is known to include DLPFC, insula, IPL, thalamus, (pre-)SMA, dACC, and the cerebellum (Cabeza and Nyberg, 2000; Fox et al., 2005; Owen et al., 2005; Kim et al., 2010; for detailed hypothetical explanations of the functions of observed regions in externally guided decision-making, see Mohr et al., 2010a).

In contrast, the DMN consists mainly of cortical midline structures (Gusnard and Raichle, 2001; Raichle and Gusnard, 2005) and comprises MPFC, pACC, PCC, and superior temporal/inferior parietal cortex (Fox et al., 2005; Kim et al., 2010; Qin and Northoff, 2011). The DMN is more active at rest than during externally oriented cognitive tasks (Raichle et al., 2001; Buckner et al., 2008b). The regions within DMN are known to show a high degree of functional connectivity during rest (Raichle et al., 2001; Beckmann et al., 2005; Raichle and Snyder, 2007; Buckner et al., 2008a). Interestingly, the DMN and task-positive network are temporally anticorrelated such that task-induced activation within the task-positive network is associated with attenuation of the DMN (Fox et al., 2005, 2009). These physiological phenomena are thought to reflect stimulus-independent thought (e.g., mind-wandering; Mason et al., 2007; Christoff et al., 2009), which has been studied since the 1960s from a naturalistic viewpoint (Singer and Antrobus, 1962, 1963; Antrobus et al., 1966, 1970; Wollman and Antrobus, 1986).

The DMN is also activated by a task that requires processing internally generated information, including self-reference (Kelley et al., 2002; Northoff et al., 2006), episodic memory retrieval (Buckner et al., 2008b), envisioning the future (Szpunar et al., 2007), mental imaginary (Hassabis et al., 2007; Daselaar et al., 2010), and mentalizing (Gusnard et al., 2001; Amodio and Frith, 2006). Because of the long lists of psychological contents related to the DMN, it is difficult to attribute any specific psychological function to task-negative regions. The DMN is often summarized more physiologically as the reflection of intrinsic brain activity in the context of neuroscience (for detailed reviews about task-positive and DMN, see Broyd et al., 2009; Northoff et al., 2010).

Intrinsic brain activity during a resting state is known to affect a stimulus-induced activity (Northoff et al., 2010). For instance, Northoff et al. (2007) measured the level of g-aminobutyric acid (GABA) in pACC, which is part of the DMN during a resting state using magnetic resonance spectroscopy (MRS), in addition to the blood oxygen level dependent (BOLD) response during an emotion judgment task using fMRI. The resting-state level of GABA in the pACC correlated with the degree of decreased BOLD response in the same region induced by an emotional judgment task. This study demonstrated that the resting-state concentration of GABA in the pACC can indeed impact upon stimulus-induced activity changes in the same region pACC.

Based on the rest–stimulus interaction and the overlap between the network for internally guided decision-making with DMN, internally guided decision-making seems to be based largely on intrinsic brain activity.

Taken together, by linking with the notions about the DMN, our meta-analysis results suggest that the decision in internally guided decision-making is based largely on intrinsic brain activity within the DMN (see Figure 4 for schematic summary). This implication from physiological evidence has high affinity with the psychological nature of internally guided decision-making: decision in internally guided decision-making depends on the participant’s own criteria rather than on circumstantial criteria. Internally guided decision-making might be modulated directly by intrinsic brain activity, which can be assessed according to the resting-state brain activity.

Is internally guided decision-making modulated solely by intrinsic brain activity within the DMN? As described earlier in the review part of this report, it is known that the outcomes and feedback are used to regulate externally guided decision-making process (e.g., RL model) to avoid error decision. The outcome-based regulation process is not applicable to internally guided decision-making that does not presuppose the presence of outcomes and feedback (i.e., an objectively correct answer). Is there any regulatory process in internally guided decision-making, as there is in externally guided decision-making?

A possible regulatory process for internally guided decision-making is conflict-based regulation instead of outcome-based regulation in the case of externally guided decision-making (see Figure 4). Conflict is defined psychologically and computationally as the simultaneous activation of incompatible representations (Botvinick et al., 2001). The abilities of monitoring and regulation of conflict have been investigated extensively in cognitive psychology and neuroscience. Their emphases have been made predominantly on the conflict between error and correct response tendencies using tasks which strongly activate the error response (e.g., Flanker task, Ullsperger and von Cramon, 2001; Takezawa and Miyatani, 2005; Stroop task, Stroop, 1935; MacDonald et al., 2000a; and Simon task, Masaki et al., 2007). Several neuroimaging studies have documented that greater dACC activation is observed when participants are confronted with situations that demand detection of conflict (MacDonald et al., 2000b; Milham et al., 2003; Kerns et al., 2004; Egner and Hirsch, 2005), whereas the cognitive regulation of conflict (e.g., attentional modulation) is apparently related to the LPFC to reduce conflict (Botvinick et al., 2001, 2004; Kerns et al., 2004).

In addition to the conflict between error and correct response, the dACC evaluates conflict that occurs during internally guided decision-making (Greene et al., 2004; Forstmann et al., 2008; Knutson et al., 2008; Nakao et al., 2009a, 2010a,c; Sommer et al., 2010; Caspers et al., 2011; Kahane et al., 2011). In these studies, the conflict was manipulated based on the number of choices (Forstmann et al., 2008), scenarios of types (Kahane et al., 2011), ratings for each stimulus (Nakao et al., 2009a, 2010c), the chosen frequency of each stimulus (Nakao et al., 2010a), or reaction times (Greene et al., 2004; Knutson et al., 2008; Sommer et al., 2010; Caspers et al., 2011). Irrespective of the mode of conflict manipulation, higher dACC activities were observed in a large-conflict condition than in a small conflict condition during internally guided decision-making in these studies. This evidence suggests that dACC evaluates the conflict between possible decision branches in internally guided decision-making.

The regulation process used to reduce conflict in internally guided decision-making is probably different from that of externally guided decision-making (Lieberman and Eisenberger, 2005; Nakao et al., 2009b, 2010a,c; Chen et al., 2010). Instead of LPFC in the case of externally guided decision-making, MPFC and PCC as the part of the DMN associate with reduction of the conflict. Using psychophysiological interaction (PPI) analyses of fMRI data, Chen et al. (2010) showed that the dACC co-varied significantly more highly with the DMPFC and PCC during a face preference judgment task with no objective correct answer when compared to the control task: a gender judgment task with one correct answer. Similarly, Nakao et al. (2010c) reported that dACC has functional connectivity with VMPFC only during an occupational choice task, as internally guided decision-making, and not during a word-length judgment task. These results suggest that the MPFC and PCC as the parts of the DMN are modulated in response to the amount of conflict evaluated within dACC to reduce conflict during internally guided decision-making (Nakao et al., 2009b, 2010a,c).

One might argue that the dACC is not observed in our meta-analysis results for internally guided decision-making, which means that dACC does not function in internally guided decision-making. As described above, the evaluation of conflict within dACC works in situations with and without an objective correct answer. Additionally, the function of dACC is not limited to evaluation of conflict. It includes detection of error (Garavan et al., 2003; de Bruijn et al., 2009) and evaluation of the action value (Rushworth et al., 2007; Walton et al., 2007): dACC can be activated during externally guided decision-making for these functions. For these reasons, dACC activation was not shown in the meta-analysis results for internally guided decision-making based on the previous studies’ contrasts of internal decision-making vs. a control task with one objective correct answer without uncertainty (see review part and Table A1 in “Appendix” for details of the contrasts). We did not include the contrast of large-conflict vs. small conflict in internally guided decision-making as well as results from PPI analyses in our meta-analysis because these did not fit our main aim. However, regarding results from previous studies about conflict evaluation during internally guided decision-making, conflict is evaluated within dACC during internally guided decision-making. The evaluated conflict affects the regulation process, which differs from externally guided decision-making.

Taken together, instead of outcome-based regulation in externally guided decision-making, conflict-based regulation might have an important role in internally guided decision-making. The internally guided decision-making is probably based not only on intrinsic brain activity within DMN but also on the dACC as the part of task-positive network.

Internally guided decision-making, which is supported mainly by the DMN, might also be modulated in anticorrelated way by the network for attentional control. Corbetta et al. (2008) and Corbetta and Shulman (2002) proposed that networks of two types are involved in attending to environmental stimuli: a dorsal frontoparietal network and a ventral frontoparietal network. The dorsal frontoparietal network includes the dorsal parietal cortex (particularly the intraparietal sulcus and superior parietal lobule) and the dorsal frontal cortex (precentral sulcus and frontal eye field; see Figure 2 of Corbetta et al., 2008). The ventral frontoparietal network includes the temporoparietal junction and ventral frontal cortex (i.e., middle frontal gyrus, inferior frontal gyrus, frontal operculum, and anterior insula). When focusing attention on an object, the dorsal frontoparietal network is activated, but the ventral frontoparietal network is deactivated. When an unexpected but important event is evoked, both attentional networks are activated to reorient the attention.

Both of these networks consist mainly of lateral cortical regions (i.e., task-positive network), and do not include the cortical midline structure within the DMN, which is mainly observed in internally guided decision-making. However, the activity within the dorsal frontoparietal network is negatively correlated with the DMN activity (Fox et al., 2005; Golland et al., 2007; Corbetta et al., 2008). When the dorsal frontoparietal network is activated, the DMN is deactivated, and vice versa. Such functional connectivity was not observed between the ventral frontoparietal network and the DMN (Corbetta et al., 2008). Although no study has investigated the role of the top-down attentional control in internally guided decision-making, it is possible that the attentional network affect to internally guided decision-making in an anticorrelated way. For instance, when the dorsal frontoparietal network is activated and the ventral frontoparietal network is deactivated (i.e., when attention is focused on external stimuli), the processes for internally guided decision-making are expected to be attenuated.

Our meta-analysis results showed that the DMPFC is activated in externally guided decision-making under uncertainty, that in a social situation, and internally guided decision-making. Psychologically, this result suggests that the DMPFC is not modulated solely by the uncertainty of outcome, social situation, or non-availability of outcome, and that it has common functions in decision-making of these kinds. Physiologically, our results suggest that the DMPFC is co-activated both with DLPFC–insula–thalamus–IPL and/or VMPFC–pACC–PCC–STG networks, and that it has functional relations with these networks.

One might want to argue that the overlap within DMPFC does not reflect that the area was activated both in externally and internally guided decision-making, but the DMPFC was observed because of the extended area from SMA (BA6) in externally guided decision-making and the extended area from VMPFC in internally guided decision-making. That is, the DMPFC observed in externally guided decision-making was caused by the activation within SMA and using a spherical kernel of 10 mm radius in MKDA, it was expanded to the DMPFC (BA8). In contrast, the DMPFC observed in internally guided decision-making was caused by the activity in VMPFC and by a spherical kernel, it was expanded to the area DMPFC. However, as Figure 2 shows, the area observed in internally guided decision-making was expanded to the posterior part of the overlap. Furthermore, the overlapped area includes the central part of DMPFC observed in externally guided decision-making (see Figures 1A and 2A). Based on these observations, it is implausible that the result of DMPFC was the overlap between the edges of the spherical kernels. It would be reasonable to infer that the overlapped area was activated consistently both in externally and internally guided decision-making.

Another possible confounding factor reflected in the overlap is the task difficulty. It is possible that the experimental tasks in both externally guided (i.e., uncertain condition) and internally guided circumstances were more difficult than the control tasks, and that the difference of difficulty was reflected in the DMPFC activation both in externally and internally guided decision-making. However, to assess the effect of the difference of task difficulty between experimental and control conditions, we examined the reaction time difference between these conditions. Results show no significant difference either in externally guided or in internally guided decision-making. The overlap within DMPFC is not expected to reflect the difference of task difficulty between experimental and control tasks.

Although the specific function of the DMPFC remains unclear, one possible role suggested by our result is that it integrates signals from task-positive regions and/or task-negative regions to bias either choice of behavior (see Figure 4), which was also proposed in previous articles (Volz et al., 2006; Nakao et al., 2009b). Depending on whether an objective correct answer is available or not, the DLPFC–insula–thalamus–IPL network or VMPFC–pACC–PCC–STG network is strongly activated. However, irrespective of which network is strongly activated, the DMPFC would receive the signals from the activated network(s), then integrate and mediate these signals to the motor control regions to output. In fact, the DMPFC has a strong connection with motor areas (Averbeck and Seo, 2008).

Ochsner et al. (2004) and Ochsner and Gross (2005) reported that the DMPFC was associated with different forms of cognitive control over emotional response. This fact suggests that the DMPFC is the node point between cognition and emotion. The DMPFC might be suited to integrate relevant cognitive and emotional processes in externally and internally guided decision-making. For that reason, it is involved in decision-making of both types.

One might be surprised that only the DMPFC was overlapped between these two types of decision-making tasks. One possible reason for the small fraction of overlap is that the data used in meta-analysis were already contrasted in previous studies. Both in the externally guided decision-making under uncertainty and internally guided decision-making, previous studies used a control task which required participants to make judgment in the situation with an objective correct answer without uncertainty. The brain regions which have functions in the control task were not reflected in the results for externally guided decision-making under uncertainty and internally guided decision-making. Therefore, our results might show the small fraction of overlapping. For example, the visual or auditory cortex for stimulus input, motor area for response, and dACC for regulation process can be activated during the control task. The striatum, amygdala, and orbitofrontal cortex for reward expectation can also be activated in the control task with reward feedback (e.g., pure monetary rewards task in Moll et al., 2006, and a gambling task using learned rules in Bhanji et al., 2010). We should note that we cannot conclude that the regions which were not observed in the meta-analysis have no function in these decision-making processes.

Another possible reason for the limited overlap area is the nature of MKDA. The MKDA (and other methods of meta-analyses) shows only the consistently activated regions in each category, although this is the aim of the meta-analysis. Consequently, for example, even when one of the studies of internally guided decision-making reported insula activity, such as that of Johnson et al. (2005), it was not reflected in the result from MKDA for internally guided decision-making. Therefore, although the insula was observed in the results of MKDA for externally guided decision-making, that region was not observed as a common region between externally and internally guided decision-making. Again, we should note carefully that the regions that were not observed using MKDA are not equal to the regions which have no function in decision-making. What we can know from the meta-analysis is that the observed regions were observed consistently in previous studies. This point is explained further in the following section.

In this report, to examine internally guided decision-making specifically as distinguished from externally guided decision-making, we categorized decision-making into externally and internally guided decision-making conceptually and methodologically. Consequently, we showed a difference of neural networks between these two. These two neural networks are, however, thought to be not completely independent of each other. They are merely the two extremes of a single continuum (see Figure 4). Each decision-making task can be located on the continuum, and the extent to which the DLPFC–insula–thalamus–IPL or the VMPFC–pACC–PCC–STG networks become involved is expected to differ depending on the decision-making situation.

In fact, several studies included in externally guided decision-making have shown activation within the network for internally guided decision-making (e.g., VMPFC, Elliott et al., 1999; Callan et al., 2009; PCC, Coricelli and Nagel, 2009; STG, Elliott et al., 1999; Elliott et al., 2006; Coricelli and Nagel, 2009; and vice versa DLPFC, Johnson et al., 2005; Greene and Paxton, 2009; Schleim et al., 2011; insula, Johnson et al., 2005; IPL, Chen et al., 2010). In addition, Pearson et al. (2011) reviewed mainly monkey single-neuron recording studies and implicated PCC as the part of DMN which has a role in externally guided decision-making. The clear distinctive neural substrates were observed in our meta-analysis because the results of meta-analysis show only the consistently activated regions in each category. This feature functioned well to reveal regions associated with the two extreme categories. However, non-activated regions from MKDA analysis are not equal to non-participating regions in each category of decision-making.

When participants refer to criteria that are probably used predominantly in internally guided decision-making, the VMPFC–pACC–PCC–STG network was activated even in externally guided decision-making. For instance, Hampton et al. (2008) reported increased VMPFC and STG activities during externally guided decision-making in a social situation when they used a computational model incorporating referencing process of one’s own actions to analyze fMRI data (see the review section for additional details). Furthermore, Goel and Dolan (2003) used a deductive reasoning task (e.g., “No harmful substances are natural; All poisons are natural; ∴ No poisons are harmful”… true, false, or unsure) with one objective correct answer. They observed increased VMPFC activity when participants reached a decision based on their internal beliefs about the world (e.g., false response for “No poisons are harmful” based on the belief that “Poisons are harmful”) instead of logical reasoning (e.g., true response for “No poisons are harmful”). Even in the case of externally guided decision-making, the network that functions predominantly for internally guided decision-making is activated to some degree depending on the task type and the participant’s strategy.

Taken together, although one might wish to distinguish decision-making as two completely different phenomena – externally guided or internally guided – such a distinction between networks of the two types becomes relevant based on those earlier studies. How these two networks interact and how they are integrated during real-life decision-making remains to be resolved. However, our meta-analysis results at least suggest that two complementary networks are involved in decision-making and that the DMPFC serves some role in the integrative process.

The network for internally guided decision-making overlapped with the DMN. This fact implies that internally guided decision-making is strongly affected by resting-state brain activities. Investigating how the resting state affects the decision-making process (i.e., rest–stimulus interaction in decision-making) is a key directive leading to understanding of internally guided decision-making. The number of studies investigating the rest–stimulus interactions is growing (Greicius and Menon, 2004; Boly et al., 2007; Northoff et al., 2007, 2010; Wiebking et al., 2010, 2011; Duncan et al., 2011). Using the methods in those earlier studies, further detailed neuronal characteristics of internally guided decision-making would be revealed.

For example, the resting-state EEG for several minutes before conducting experimental tasks can be used to investigate the effect from intrinsic brain activity to internally guided decision-making. As decision-making tasks, color-similarity judgment and color preference judgment tasks which were used in Johnson et al. (2005) are expected to be useful for this purpose (similar tasks were also used by Goldberg and Podell, 1999, 2000). In both tasks, three colored squares are presented in each trial. The colored square presented in the upper center is the target color, and the squares presented in the lower left and right are choices. In the color-similarity judgment task, participants are asked to judge which choice is more similar to the target color (“Which is more similar?”). In the color preference judgment task, participants were asked to judge which color pair (target–choice pair) they prefer (“Which do you prefer?”).

If intrinsic brain activity modulates internally guided decision-making, then the following is expected. Especially in participants who showed more increased resting-state activity (i.e., higher power spectral density during resting state), the color preference judgments are less biased from properties of external stimulus (e.g., color similarity; similar and dissimilar pairs are almost equally selected as the preferred pairs in those participants). In other words, participants who showed higher resting-state activity are expected to rely less on the properties of external stimulus for their preference judgment but might rely greatly on their internal criteria. In the color-similarity judgment, such a relation would not be observed even in cases where the judgment is difficult because of the similar color choices: the color-similarity judgment is the task of making a judgment based on the external stimulus properties. It is expected to be less affected by the intrinsic brain activity.

Regarding internally guided decision-making, outcomes and feedback are not available to adjust decision-making processes as externally guided decision-making. For that reason, outcome-based learning and regulation are not applicable to internally guided decision-making. Instead, previous results of studies have suggested that the amount of conflict is evaluated within dACC during internally guided decision-making (Greene et al., 2004; Forstmann et al., 2008; Knutson et al., 2008; Nakao et al., 2009a, 2010a,c; Sommer et al., 2010; Caspers et al., 2011; Kahane et al., 2011), and the signal from dACC is expected to regulate activation within DMN during internally guided decision-making (Chen et al., 2010; Nakao et al., 2010c). Details of conflict-based regulation processes in internally guided decision-making, however, might be less readily apparent. For instance, learning and regulation processes of what kinds are achieved to reduce conflict during internally guided decision-making remains unclear.

Several options are related to manipulation of conflict during internally guided decision-making: stimulus-based manipulation by the number of choices (Forstmann et al., 2008) or type of scenario (Kahane et al., 2011), and individualized manipulation based on reaction time (Greene et al., 2004; Knutson et al., 2008; Sommer et al., 2010; Caspers et al., 2011), ratings (Nakao et al., 2009a, 2010c; Jarcho et al., 2011), or chosen frequency of each stimulus (Nakao et al., 2010a). Although each manipulation has strong and weak points, all are applicable to internally and externally guided decision-making. These methods are useful to investigate the differences of conflict-based regulation process between decision-making of the two kinds.

To measure brain activities relating to conflict-based regulation process, not only fMRI but also event-related brain potentials (ERPs) are useful. The amplitudes of correct and conflict-related negativity (CRN; Simon-Thomas and Knight, 2005; Masaki et al., 2007; Nakao et al., 2010a) and N2 components (Yeung et al., 2004; Bartholow et al., 2005) are known to reflect the amount of conflict. Nakao et al. (2010a) reported that the amount of conflict during internally guided decision-making (occupational choice in their case) is also reflected in the amplitude of the CRN.