The dynamics of functional brain network segregation in feedback-driven learning.

Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.

Changes in global functional network properties predict individual differences in habit formation.

Prior evidence suggests that sensorimotor regions play a crucial role in habit formation. Yet, whether and how their global functional network properties might contribute to a more comprehensive characterization of habit formation still remains unclear. Capitalizing on advances in Elastic Net regression and predictive modeling, we examined whether learning-related functional connectivity alterations distributed across the whole brain could predict individual habit strength. Using the leave-one-subject-out cross-validation strategy, we found that the habit strength score of the novel unseen subjects could be successfully predicted. We further characterized the contribution of both, individual large-scale networks and individual brain regions by calculating their predictive weights. This highlighted the pivotal role of functional connectivity changes involving the sensorimotor network and the cingulo-opercular network in subject-specific habit strength prediction. These results contribute to the understanding the neural basis of human habit formation by demonstrating the importance of global functional network properties especially also for predicting the observable behavioral expression of habits.

The Role of the Angular Gyrus in Goal-directed Behavior-Two Transcranial Magnetic Stimulation Studies Examining Response Outcome Learning and Outcome Anticipation.

Learning the contingencies between a situational context (S), one's own responses (R), and their outcomes (O) and selecting responses according to their anticipated outcomes is the basis of a goal-directed behavior. Previous imaging studies found the angular gyrus (AG) to be correlated to both the representation of R-O associations and outcome-based response selection. Based on this correlational relationship, we investigated the causal link between AG function and goal-directed behavior in offline and online TMS experiments. To this end, we employed an experimental R-O compatibility paradigm testing outcome anticipation during response selection and S-R-O knowledge to probe S-R-O learning. In Experiment 1, we applied 1-Hz rTMS offline to the AG or the vertex before participants performed the experimental tasks. In Experiment 2, we applied online 10-Hz pulse trains to the AG or used sham stimulation during an early action selection stage in half of the trials. In both experiments, the R-O compatibility effect was unaltered when response selection was outcome-based, suggesting no causal role of the AG in outcome anticipation during response selection. However, in both experiments, groups with AG stimulation showed significantly modulated knowledge of S-R-O associations in a posttest. Additionally, in an explorative analysis, we found an induced R-O compatibility effect later in the experiment when response selection was guided by stimulus-response rules, suggesting reduced selectivity of outcome anticipation. We discuss possible compensatory behavioral and brain mechanism as well as specific TMS-related methodical considerations demonstrating important implications for further studies investigating cognitive function by means of TMS.

Focusing on Future Consequences Enhances Self-Controlled Dietary Choices.

Self-controlled dietary decisions, i.e., choosing a healthier food over a tastier one, are a major challenge for many people. Despite the potential profound consequences of frequent poor choices, maintaining a healthy diet proves challenging. This raises the question of how to facilitate self-controlled food decisions to promote healthier choices. The present study compared the influence of implicit and explicit information on food choices and their underlying decision processes. Participants watched two video clips as an implicit manipulation to induce different mindsets. Instructions to focus on either the short-term or long-term consequences of choices served as an explicit manipulation. Participants performed a binary food choice task, including foods with different health and taste values. The choice was made using a computer mouse, whose trajectories we used to calculate the influence of the food properties. Instruction to focus on long-term consequences compared to short-term consequences increased the number of healthy choices, reduced response times for healthy decisions, and increased the influence of health aspects during the decision-making process. The effect of video manipulation showed greater variability. While focusing on long-term consequences facilitated healthy food choices and reduced the underlying decision conflict, the current mindset appeared to have a minor influence.

Instructing item-specific switch probability: expectations modulate stimulus-action priming.

Both active response execution and passive listening to verbal codes (a form of instruction) in single prime trials lead to item-specific repetition priming effects when stimuli re-occur in single probe trials. This holds for task-specific classification (stimulus-classification, SC priming, e.g., apple-small) and action (stimulus-action, SA priming, e.g., apple-right key press). To address the influence of expectation on item-specific SC and SA associations, we tested if item-specific SC and SA priming effects were modulated by the instructed probability of re-encountering individual SC or SA mappings (25% vs. 75% instructed switch probability). Importantly, the experienced item-specific switch probability was always 50%. In Experiment 1 (N = 78), item-specific SA/SC switch  expectations affected SA, but not SC priming effects exclusively following active response execution. Experiment 2 (N = 40) was designed to emphasize SA priming by only including item-specific SC repetitions. This yielded stronger SA priming for 25% vs. 75% expected switch probability, both following response execution as in Experiment 1 and also following verbally coded SA associations. Together, these results suggest that SA priming effects, that is, the encoding and retrieval of SA associations, is modulated by item-specific switch expectation. Importantly, this expectation effect cannot be explained by item-specific associative learning mechanisms, as stimuli were primed and probed only once and participants experienced item-specific repetitions/switches equally often across stimuli independent of instructed switch probabilities. This corroborates and extends previous results by showing that SA priming effects are modulated by  expectation not only based on experienced item-specific switch probabilities, but also on mere instruction.

Costly habitual avoidance is reduced by concurrent goal-directed approach in a modified devaluation paradigm.

Avoidance habits potentially contribute to maintaining maladaptive, costly avoidance behaviors that persist in the absence of threat. However, experimental evidence about costly habitual avoidance is scarce. In two experiments, we tested whether extensively trained avoidance impairs the subsequent goal-directed approach of rewards. Healthy participants were extensively trained to avoid an aversive outcome by performing simple responses to distinct full-screen color stimuli. After the subsequent devaluation of the aversive outcome, participants received monetary rewards for correct responses to neutral object pictures, which were presented on top of the same full-screen colors. These approach responses were either compatible or incompatible with habitual avoidance responses. Notably, the full-screen colors were not relevant to inform approach responses. In Experiment 1, participants were not instructed about post-devaluation stimulus-response-reward contingencies. Accuracy was lower in habit-incompatible than in habit-compatible trials, indicating costly avoidance, whereas reaction times did not differ. In Experiment 2, contingencies were explicitly instructed. Accuracy differences disappeared, but reaction times were slower in habit-incompatible than in habit-compatible trials, indicating low-cost habitual avoidance tendencies. These findings suggest a small but consistent impact of habitual avoidance tendencies on subsequent goal-directed approach. Costly habitual responding could, however, be inhibited when competing goal-directed approach was easily realizable.

Low-Frequency TMS Results in Condition-Related Dynamic Activation Changes of Stimulated and Contralateral Inferior Parietal Lobule.

Non-invasive brain stimulation is a promising approach to study the causal relationship between brain function and behavior. However, it is difficult to interpret behavioral null results as dynamic brain network changes have the potential to prevent stimulation from affecting behavior, ultimately compensating for the stimulation. The present study investigated local and remote changes in brain activity via functional magnetic resonance imaging (fMRI) after offline disruption of the inferior parietal lobule (IPL) or the vertex in human participants via 1 Hz repetitive transcranial magnetic stimulation (rTMS). Since the IPL acts as a multimodal hub of several networks, we implemented two experimental conditions in order to robustly engage task-positive networks, such as the fronto-parietal control network (on-task condition) and the default mode network (off-task condition). The condition-dependent neural after-effects following rTMS applied to the IPL were dynamic in affecting post-rTMS BOLD activity depending on the exact time-window. More specifically, we found that 1 Hz rTMS applied to the right IPL led to a delayed activity increase in both, the stimulated and the contralateral IPL, as well as in other brain regions of a task-positive network. This was markedly more pronounced in the on-task condition suggesting a condition-related delayed upregulation. Thus together, our results revealed a dynamic compensatory reorganization including upregulation and intra-network compensation which may explain mixed findings after low-frequency offline TMS.

Neural representation of newly instructed rule identities during early implementation trials.

By following explicit instructions, humans instantaneously get the hang of tasks they have never performed before. We used a specially calibrated multivariate analysis technique to uncover the elusive representational states during the first few implementations of arbitrary rules such as 'for coffee, press red button' following their first-time instruction. Distributed activity patterns within the ventrolateral prefrontal cortex (VLPFC) indicated the presence of neural representations specific of individual stimulus-response (S-R) rule identities, preferentially for conditions requiring the memorization of instructed S-R rules for correct performance. Identity-specific representations were detectable starting from the first implementation trial and continued to be present across early implementation trials. The increasingly fluent application of novel rule representations was channelled through increasing cooperation between VLPFC and anterior striatum. These findings inform representational theories on how the prefrontal cortex supports behavioral flexibility specifically by enabling the ad-hoc coding of newly instructed individual rule identities during their first-time implementation.

Regulating Craving by Anticipating Positive and Negative Outcomes: A Multivariate Pattern Analysis and Network Connectivity Approach.

During self-control, we may resist short-term temptations in order to reach a favorable future (e.g., resisting cake to stay healthy). The neural basis of self-control is typically attributed to "cold," unemotional cognitive control mechanisms which inhibit affect-related regions via the prefrontal cortex (PFC). Here, we investigate the neural underpinnings of regulating cravings by mentally evoking the positive consequences of resisting a temptation (e.g., being healthy) as opposed to evoking the negative consequences of giving in to a temptation (e.g., becoming overweight). It is conceivable that when using these types of strategies, regions associated with emotional processing [e.g., striatum, ventromedial prefrontal cortex (vmPFC)] are involved in addition to control-related prefrontal and parietal regions. Thirty-one participants saw pictures of unhealthy snacks in the fMRI scanner and, depending on the trial, regulated their craving by thinking of the positive consequences of resisting, or the negative consequences of not resisting. In a control condition, they anticipated the pleasure of eating and thus, allowed the craving to occur (now-condition). In line with previous studies, we found activation of a cognitive control network during self-regulation. In the negative future thinking condition, the insula was more active than in the positive condition, while there were no activations that were stronger in the positive (> negative) future thinking condition. However, additionally, multivariate pattern analysis showed that during craving regulation, information about the valence of anticipated emotions was present in the vmPFC, the posterior cingulate cortex (PCC) and the insula. Moreover, a network including vmPFC and PCC showed higher connectivity during the positive (> negative) future thinking condition. Since these regions are often associated with affective processing, these findings suggest that "hot," affective processes may, at least in certain circumstances, play a role in self-control.

Deterministic response strategies in a trial-and-error learning task.

Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in the presented trial-and-error learning task go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.

Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning.

Recent work has highlighted that multi-voxel pattern analysis (MVPA) can be severely biased when BOLD response estimation involves systematic imbalance in model regressor correlations. This problem occurs in situations where trial types of interest are temporally dependent and the associated BOLD activity overlaps. For example, in learning paradigms early and late learning stage trials are inherently ordered. It has been shown empirically that MVPAs assessing consecutive learning stages can be substantially biased especially when stages are closely spaced. Here, we propose a simple technique that ensures zero bias in item-specific multi-voxel activation patterns for consecutive learning stages with stage being defined by the incremental number of individual item occurrences. For the simpler problem, when MVPA is computed irrespective of learning stage over all item occurrences within a trial sequence, our results confirm that a sufficiently large, randomly selected subset of all possible trial sequence permutations ensures convergence to zero bias - but only when different trial sequences are generated for different subjects. However, this does not help to solve the harder problem to obtain bias-free results for learning-related activation patterns regarding consecutive learning stages. Randomization over all item occurrences fails to ensure zero bias when the full trial sequence is retrospectively divided into item occurrences confined to early and late learning stages. To ensure bias-free MVPA of consecutive learning stages, trial-sequence randomization needs to be done separately for each consecutive learning stage.

Anticipating the good and the bad: A study on the neural correlates of bivalent emotion anticipation and their malleability via attentional deployment.

In everyday life, we often deliberate about affective outcomes of decisions which can be described as ambivalent; i.e. positive and negative at the same time. For example, when looking forward to meet a dear friend at her/his favorite concert although one dislikes the music that is being performed. Thus, anticipation of bivalent emotions and their volitional regulation is an important ingredient of everyday choices. However, previous studies investigating neural substrates involved in anticipating emotional events mostly focused on anticipating either negative emotions (punishment) or positive emotions (reward) in isolation, thus inducing either of them separately. Furthermore, these studies rather focused on the effortful down-regulation of affect (i.e. reducing negative or positive affect), whereas such conflict situations may also require us to deploy attention on and thereby upregulate anticipated emotions in order to resolve a decision conflict (e.g., by focusing on positive consequences while orienting away from negative consequences of that same situation). To address this gap, we performed a series of three fMRI-experiments using simple visual and auditory stimuli in order to (i) determine the neural correlates involved when anticipating a bivalent affective outcome that is both positive and negative at the same time - related to a conflict situation and (ii) investigate their malleability during anticipation via voluntary emotion regulation using attentional focusing. In these studies, we (i) demonstrate that brain areas involved in anticipating positive (ventral striatum) and negative (anterior insula) emotional events are co-activated when anticipating the occurrence of both punishment and reward at the same time and (ii) provide evidence that attention on either the positive or the negative correlates with a shift in activations of these co-activated neural networks and associated anticipated emotions towards either the positive (increased activity in ventral striatum, ventromedial prefrontal cortex, posterior cingulate cortex) or the negative (increased activity in insula) aspect of the upcoming bivalent outcome. In summary, we provide self-report and neural evidence for the assumption that affective brain systems associated with the processing of bivalent anticipated emotions can be voluntarily controlled by cognitive emotion regulation strategies.

On the efficiency of instruction-based rule encoding.

Instructions have long been considered a highly efficient route to knowledge acquisition especially compared to trial-and-error learning. We aimed at substantiating this claim by identifying boundary conditions for such an efficiency gain, including the influence of active learning intention, repeated instructions, and working memory load and span. Our experimental design allowed us to not only assess how well the instructed stimulus-response (S-R) rules were implemented later on, but also to directly measure prior instruction encoding processes. This revealed that instruction encoding was boosted by an active learning intention which in turn entailed better subsequent rule implementation. As should be expected, instruction-based learning took fewer trials than trial-and-error learning to reach a similar performance level. But more importantly, even when performance was measured relative to the identical number of preceding correct implementation trials, this efficiency gain persisted both in accuracy and in speed. This suggests that the naturally greater number of failed attempts in the initial phase of trial-and-error learning also negatively impacted learning in subsequent trials due to the persistence of erroneous memory traces established beforehand. A single instruction trial was sufficient to establish the advantage over trial-and-error learning but repeated instructions were better. Strategic factors and inter-individual differences in WM span - the latter exclusively affecting trial-and-error learning presumably due to the considerably more demanding working memory operations - could reduce or even abolish this advantage, but only in error rates. The same was not true for response time gains suggesting generally more efficient task automatization in instruction-based learning.

Large-scale coupling dynamics of instructed reversal learning.

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions. We hypothesized that residual stimulus-response associations from initial practice might negatively impact the ability to implement novel instructions. Using functional imaging and large-scale connectivity analysis, we found that functional coupling between the CON and DAN was generally at a higher level during initial than reversal learning. Examining the learning-related connectivity dynamics between the CON and DAN in more detail by means of multivariate patterns analyses, we identified a specific subset of connections which showed a particularly high increase in connectivity during initial learning compared to reversal learning. This finding suggests that the CON-DAN connections can be separated into two functionally dissociable yet spatially intertwined subsystems supporting different aspects of short-term task automatization.

Habit strength is predicted by activity dynamics in goal-directed brain systems during training.

Previous neuroscientific research revealed insights into the brain networks supporting goal-directed and habitual behavior, respectively. However, it remains unclear how these contribute to inter-individual differences in habit strength which is relevant for understanding not only normal behavior but also more severe dysregulations between these types of action control, such as in addiction. In the present fMRI study, we trained subjects on approach and avoidance behavior for an extended period of time before testing the habit strength of the acquired stimulus-response associations. We found that stronger habits were associated with a stronger decrease in inferior parietal lobule activity for approach and avoidance behavior and weaker vmPFC activity at the end of training for avoidance behavior, areas associated with the anticipation of outcome identity and value. VmPFC in particular showed markedly different activity dynamics during the training of approach and avoidance behavior. Furthermore, while ongoing training was accompanied by increasing functional connectivity between posterior putamen and premotor cortex, consistent with previous assumptions about the neural basis of increasing habitualization, this was not predictive of later habit strength. Together, our findings suggest that inter-individual differences in habitual behavior are driven by differences in the persistent involvement of brain areas supporting goal-directed behavior during training.

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching).

Learning-Related Brain-Electrical Activity Dynamics Associated with the Subsequent Impact of Learnt Action-Outcome Associations.

Goal-directed behavior relies on the integration of anticipated outcomes into action planning based on acquired knowledge about the current contingencies between behavioral responses (R) and desired outcomes (O) under specific stimulus conditions (S). According to ideomotor theory, bidirectional R-O associations are an integral part of this knowledge structure. Previous EEG studies have identified neural activity markers linked to the involvement of such associations, but the initial acquisition process has not yet been characterized. The present study thus examined brain-electrical activity dynamics during the rapid acquisition of novel bidirectional R-O associations during instructed S-R learning. Within a trial, we inspected response-locked and stimulus-locked activity dynamics in order to identify markers linked to the forward and backward activation of bidirectional R-O associations as they were being increasingly strengthened under forced choice conditions. We found that a post-response anterior negativity following auditory outcomes was increasingly attenuated as a function of the acquired association strength. This suggests that previously reported action-induced sensory attenuation effects under extensively trained free choice conditions can be established within few repetitions of specific R-O pairings under forced choice conditions. Furthermore, we observed the even more rapid development of a post-response but pre-outcome fronto-central positivity which was reduced for high R-O learners which might indicate the rapid deployment of preparatory attention towards predictable outcomes. Finally, we identified a learning-related stimulus-locked activity modulation within the visual P1-N1 latency range which might reflect the multi-sensory integration of the perceived antecedent visual stimulus the anticipated auditory outcome.

Integration and segregation of large-scale brain networks during short-term task automatization.

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

Towards an understanding of the neural dynamics of intentional learning: Considering the timescale.

Recently, Hampshire et al. (2016) published a paper in NeuroImage investigating the involvement of frontal networks in two types of 'intentional learning'. This included the standard type of deterministic feedback-driven trial-and-error learning and another type of intentional learning that has recently been studied in various facets by means of neuroimaging methods under the terms 'instruction-based learning' (Ruge and Wolfensteller, 2010) or 'rapid instructed task learning' (Cole et al., 2010). By differentiating the learning-related functional roles of different lateral frontal cortex networks and the anterior striatum, Hampshire et al. (2016) contributed valuable results to the field. The aim of this commentary is to increase the interpretability of some of their findings by connecting them to what is already known about fronto-striatal activation dynamics and its functional couplings based on related previous studies. We start with an overview of the rapidly diversifying neuroimaging research on the intentional control of learning and behaviour and its historical embedding. Based thereon we discuss ways to reconcile and integrate the new results presented by Hampshire et al. (2016) particularly regarding the nature of fronto-striatal activation dynamics and their functional couplings during instruction-based learning and during deterministic trial-and-error learning. We conclude that it is important to assess neural activation dynamics on multiple time scales in order to characterize short-term learning and automatization processes as they are evolving across the initial learning trials and further across more extended periods of practice trials.

The neural basis of integrating pre- and post-response information for goal-directed actions.

A fundamental prerequisite for goal-directed action is to encode the contingencies between responses (R) producing specific outcomes (O) in specific stimulus conditions (S). The present study aimed to characterize the functional neuroanatomy of different associational sub-components of such S-R-O contingencies during the first few trials of exposure. We devised a novel paradigm that was suited to distinguish BOLD activation patterns related to S-R, R-O, and the full S-R-O contingency. Different from previous studies our experimental design ensured that stimulus-related processes and outcome-related processes were maximally comparable, as both were learned incidentally and lacked intrinsic incentive value, and different from trial-and-error learning situations, outcomes did not serve a special role as performance feedback. We observed contingency-related dissociations between SMA, lateral OFC, and large parts of the reward system including central OFC, anterior striatum and midbrain areas. While the lateral OFC was involved in processing differential outcomes irrespective of a predictive stimulus context, the SMA was specifically engaged when differential outcomes could be predicted by the stimulus. By contrast, the activation pattern of reward system areas suggested that these regions serve a role in integrating non-incentive differential outcome information and incentive common outcome information. Together, these results support the notion that striatal and orbitofrontal regions are involved in outcome-related processes beyond trial-and-error S-R learning, that is, when outcomes are non-incentive and do not serve as reinforcing feedback that drives learning. Furthermore, our results clarify the role of the SMA in outcome-related processes thereby supporting current versions of ideomotor theory.