During decision making, individuals are prone to rely on external cues such as expert advice when the outcome is not known. However, the electrophysiological correlates associated with outcome uncertainty and the use of expert advice are not completely understood. The feedback-related negativity (FRN), P3a, and P3b are event-related brain potentials (ERPs) linked to dissociable stages of feedback and attentional processing during decision making. Even though these ERPs are influenced by both reward-and punishment-related feedback, it remains unclear how extrinsic information during uncertainty modulates these brain potentials. In this study, the effects of advice cues on decision making were investigated in two separate experiments. In the first experiment, electroencephalography (EEG) was recorded in healthy volunteers during a decision-making task in which the participants received reward or punishment feedback preceded by novice, amateur, or expert advice. The results showed that the P3a component was significantly influenced by the subjective predictive value of an advice cue, whereas the FRN and P3b were unaffected by the advice cues. In the second, sham-controlled experiment, cathodal transcranial direct current stimulation (ctDCS) was administered in conjunction with EEG in order to explore the direct contributions of the frontal cortex to these brain potentials. Results showed no significant change in either advice-following behavior or decision times. However, ctDCS did decrease FRN amplitudes as compared to sham, with no effect on the P3a or P3b. Together, these findings suggest that advice information may act primarily on attention allocation during feedback processing, whereas the electrophysiological correlates of the detection and updating of internal prediction models are not affected.

Behavioral flexibility is the hallmark of goal-directed behavior. Whereas a great deal is known about the neural substrates of behavioral adjustment when it is explicitly cued by features of the external environment, little is known about how we adapt our behavior when such changes are made on the basis of uncertain evidence. Using a Bayesian reinforcement-learning model and fMRI, we show that frontopolar cortex (FPC) tracks the relative advantage in favor of switching to a foregone alternative when choices are made voluntarily. Changes in FPC functional connectivity occur when subjects finally decide to switch to the alternative behavior. Moreover, interindividual variation in the FPC signal predicts interindividual differences in effectively adapting behavior. By contrast, ventromedial prefrontal cortex (vmPFC) encodes the relative value of the current decision. Collectively, these findings reveal complementary prefrontal computations essential for promoting short- and long-term behavioral flexibility.

Dorsal anterior cingulate cortex (dACC) carries a wealth of value-related information necessary for regulating behavioral flexibility and persistence. It signals error and reward events informing decisions about switching or staying with current behavior. During decision-making, it encodes the average value of exploring alternative choices (search value), even after controlling for response selection difficulty, and during learning, it encodes the degree to which internal models of the environment and current task must be updated. dACC value signals are derived in part from the history of recent reward integrated simultaneously over multiple time scales, thereby enabling comparison of experience over the recent and extended past. Such ACC signals may instigate attentionally demanding and difficult processes such as behavioral change via interactions with prefrontal cortex. However, the signal in dACC that instigates behavioral change need not itself be a conflict or difficulty signal.

In the last decade, combined transcranial magnetic stimulation (TMS)-neuroimaging studies have greatly stimulated research in the field of TMS and neuroimaging. Here, we review how TMS can be combined with various neuroimaging techniques to investigate human brain function. When applied during neuroimaging (online approach), TMS can be used to test how focal cortex stimulation acutely modifies the activity and connectivity in the stimulated neuronal circuits. TMS and neuroimaging can also be separated in time (offline approach). A conditioning session of repetitive TMS (rTMS) may be used to induce rapid reorganization in functional brain networks. The temporospatial patterns of TMS-induced reorganization can be subsequently mapped by using neuroimaging methods. Alternatively, neuroimaging may be performed first to localize brain areas that are involved in a given task. The temporospatial information obtained by neuroimaging can be used to define the optimal site and time point of stimulation in a subsequent experiment in which TMS is used to probe the functional contribution of the stimulated area to a specific task. In this review, we first address some general methodologic issues that need to be taken into account when using TMS in the context of neuroimaging. We then discuss the use of specific brain mapping techniques in conjunction with TMS. We emphasize that the various neuroimaging techniques offer complementary information and have different methodologic strengths and weaknesses.

Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available—a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes.

Ventral premotor cortex (PMv) is widely accepted to exert an important influence over primary motor cortex (M1) when hand movements are made. Although study of these interactions has typically focused on their excitatory nature, given its strong connections with both ventral and opercular frontal regions, one feature of PMv's influence over M1 may be inhibitory. Paired-pulse transcranial magnetic stimulation (ppTMS) was used to examine functional interactions between human PMv and M1 during the selection and reprogramming of a naturalistic goal-directed action. One of two cylinders was illuminated on each trial. It was then grasped and picked up. On some trials, however, subjects had to re-program the action as the illuminated cylinder was switched off and the other illuminated simultaneously with reach initiation. At a neurophysiological level, the PMv paired-pulse effect (PPE) on M1 corticospinal activity was facilitatory after the initial target presentation and during movement initiation. When reprogramming was required however, the PPE became strongly inhibitory. This context-dependent change from facilitation to inhibition occurred within 75ms of the change of target. Behaviorally, PMv-M1 ppTMS disrupted reprogramming. Diffusion-weighted magnetic resonance image (DW-MRI) scans were taken of each subject. Intersubject differences in the facilitation–inhibition contrast of PMv–M1 interactions were correlated with fractional anisotropy of white-matter in ventral prefrontal, premotor, and intraparietal brain areas. These results suggest that a network of brain areas centered on PMv inhibits M1 corticospinal activity associated with undesired movements when action plans change.

Highlights d Human brains map abstract relationships among entities from piecemeal learning d Separately learnt dimensions are combined and represented in a 2D social hierarchy d To make novel inferences, HC reinstates a hub that connects two social hierarchies d EC and vmPFC encode Euclidean distances of inferred vectors for novel inferences

Although damage to the medial frontal cortex causes profound decision-making impairments, it has been difficult to pinpoint the relative contributions of key anatomical subdivisions. Here we use function magnetic resonance imaging to examine the contributions of human ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortex (dACC) during sequential choices between multiple alternatives-two key features of choices made in ecological settings. By carefully constructing options whose current value at any given decision was dissociable from their longer term value, we were able to examine choices in current and long-term frames of reference. We present evidence showing that activity at choice and feedback in vmPFC and dACC was tied to the current choice and the best long-term option, respectively. vmPFC, mid-cingulate, and posterior cingulate cortex encoded the relative value between the chosen and next best option at each sequential decision, whereas dACC encoded the relative value of adapting choices from the option with the highest value in the longer term. Furthermore, at feedback we identify temporally dissociable effects that predict repetition of the current choice and adaptation away from the long-term best option in vmPFC and dACC, respectively. These functional dissociations at choice and feedback suggest that sequential choices are subject to competing cortical mechanisms.