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.

Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference.

Learning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with 'objective' learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation.

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.

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.

Brain state kinematics and the trajectory of task performance improvement.

Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components. We then used Linear Discriminant Analysis to maximise the mean differences between the low-dimensional signatures of fast-and-slow reaction times and early-and-late learners, while also conserving variance present within these groups. The resultant basis set allowed us to describe meaningful differences between these groups and, importantly, to detail the patterns of brain activity which underpin these differences. Our results demonstrate non-linear interactions between three key brain activation maps with convergent trajectories observed at higher task repetitions consistent with optimization. Furthermore, we show subjects with the greatest reaction time improvements have delayed recruitment of left dorsal and lateral prefrontal cortex, as well as deactivation in parts of the occipital lobe and motor cortex, and that the slowest performers have weaker recruitment of somatosensory association cortex and left ventral visual stream, as well as weaker deactivation in the dorsal lateral prefrontal cortex. Overall our results highlight the utility of a kinematic description of brain states, whereby reformatting data into low-dimensional trajectories sensitive to the subtleties of a task can capture non-linear trends in a tractable manner and permit hypothesis generation at the level of brain states.

Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis.

Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one's own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life 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.

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).

Distinct contributions of lateral orbito-frontal cortex, striatum, and fronto-parietal network regions for rule encoding and control of memory-based implementation during instructed reversal learning.

A key element of behavioral flexibility is to quickly learn to modify or reverse previously acquired stimulus-response associations. Such reversal learning (RL) can either be driven by feedback or by explicit instruction, informing either retrospectively or prospectively about the changed response requirements. Neuroimaging studies have thus far exclusively focused either on feedback-driven RL or on instructed initial learning of novel rules. The present study examined the neural basis of instructed RL as compared to instructed initial learning, separately assessing reversal-related instruction-based encoding processes and reversal-related control processes required for implementing reversed rules under competition from the initially learned rules. We found that instructed RL is partly supported by similar regions as feedback-driven RL, including lateral orbitofrontal cortex (lOFC) and anterior dorsal caudate. Encoding-related activation in both regions determined resilience against response competition during subsequent memory-based reversal implementation. Different from feedback-driven RL, instruction-based RL relied heavily on the generic fronto-parietal cognitive control network--not for encoding but for reversal-related control processes during memory-based implementation. These findings are consistent with a model of partly decoupled, yet interacting, systems of (i) symbolic rule representations that are instantaneously updated upon instruction and (ii) pragmatic representations of reward-associated S-R links mediating the enduring competition from initially learned rules.

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.

Effects of Ginkgo biloba extract EGb 761® on cognitive control functions, mental activity of the prefrontal cortex and stress reactivity in elderly adults with subjective memory impairment - a randomized double-blind placebo-controlled trial.

OBJECTIVE: Cognitive control as well as stress reactivity is assumed to depend on prefrontal dopamine and decline with age. Because Ginkgo biloba extract EGb761 increases prefrontal dopamine in animals, we assessed its effects on cognitive functions related to prefrontal dopamine. METHODS: Effects of 240-mg EGb761 daily on task-set-switching, response-inhibition, delayed response, prospective-memory, task-related fMRI-BOLD-signals and the Trier Social Stress-Test were explored in a randomized, placebo-controlled, double-blind pilot-trial in 61 elderly volunteers with subjective memory impairment. RESULTS: Baseline-FMRI-data showed BOLD-responses in regions commonly activated by the specific tasks. Task-switch-costs decreased with EGb761 compared to placebo (ANOVA-interaction: Group × Time × Switch-Costs p = 0.018, multiple tests uncorrected), indicating improved cognitive flexibility. Go-NoGo-task reaction-times corrected for error-rates indicated a trend for improved response inhibition. No treatment effects were found for the delayed response and prospective-memory tasks and fMRI-data. A non-significant trend indicated a potentially accelerated endocrine stress-recovery. EGb761 was safe and well tolerated. CONCLUSION: We observed indications for improved cognitive flexibility without changes in brain activation, suggesting increased processing efficiency with EGb761. Together with a trend for improved response inhibition results are compatible with mild enhancement of prefrontal dopamine. These conclusions on potential beneficial effect of EGb761 on prefrontal dopaminergic functions should be confirmed by direct measurements. © 2016 The Authors. Human Psychopharmacology: Clinical and Experimental published by John Wiley & Sons, Ltd.

Sparse regularization techniques provide novel insights into outcome integration processes.

By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account. Here, these advanced classification methods were applied to a large fMRI sample (N=70) in order to gain novel insights into the functional localization of outcome integration processes. While the beneficial effect of differential outcomes is well-studied in trial-and-error learning, outcome integration in the context of instruction-based learning has remained largely unexplored. In order to examine neural processes associated with outcome integration in the context of instruction-based learning, two groups of subjects underwent functional imaging while being presented with either differential or ambiguous outcomes following the execution of varying stimulus-response instructions. While no significant univariate group differences were found in the resulting fMRI dataset, L1-regularized (sparse) classifiers performed significantly above chance and also clearly outperformed the standard L2-regularized (dense) Support Vector Machine on this whole-brain between-subject classification task. Moreover, additional L2-regularization via the Elastic Net and spatial regularization by the Graph Net improved interpretability of discriminative weight maps but were accompanied by reduced classification accuracies. Most importantly, classification based on sparse regularization facilitated the identification of highly specific regions differentially engaged under ambiguous and differential outcome conditions, comprising several prefrontal regions previously associated with probabilistic learning, rule integration and reward processing. Additionally, a detailed post-hoc analysis of these regions revealed that distinct activation dynamics underlay the processing of ambiguous relative to differential outcomes. Together, these results show that L1-regularization can improve classification performance while simultaneously providing highly specific and interpretable discriminative activation patterns.

Distinct fronto-striatal couplings reveal the double-faced nature of response-outcome relations in instruction-based learning.

Higher species commonly learn novel behaviors by evaluating retrospectively whether actions have yielded desirable outcomes. By relying on explicit behavioral instructions, only humans can use an acquisition shortcut that prospectively specifies how to yield intended outcomes under the appropriate stimulus conditions. A recent and largely unexplored hypothesis suggests that striatal areas interact with lateral prefrontal cortex (LPFC) when novel behaviors are learned via explicit instruction, and that regional subspecialization exists for the integration of differential response-outcome contingencies into the current task model. Behaviorally, outcome integration during instruction-based learning has been linked to functionally distinct performance indices. This includes (1) compatibility effects, measured in a postlearning test procedure probing the encoding strength of outcome-response (O-R) associations, and (2) increasing response slowing across learning, putatively indicating active usage of O-R associations for the online control of goal-directed action. In the present fMRI study, we examined correlations between these behavioral indices and the dynamics of fronto-striatal couplings in order to mutually constrain and refine the interpretation of neural and behavioral measures in terms of separable subprocesses during outcome integration. We found that O-R encoding strength correlated with LPFC-putamen coupling, suggesting that the putamen is relevant for the formation of both S-R habits and habit-like O-R associations. By contrast, response slowing as a putative index of active usage of O-R associations correlated with LPFC-caudate coupling. This finding highlights the relevance of the caudate for the online control of goal-directed action also under instruction-based learning conditions, and in turn clarifies the functional relevance of the behavioral slowing effect.

Characterising the declarative-procedural transformation in instruction-based learning.

Many accounts of instruction-based learning assume that initial declarative representations are transformed into executable procedural ones, so as to enable instruction implementation. We tested the hypothesis that declarative-procedural transformation should be bound to a specific response modality and not transferable across different modalities. In Experiment 1, novel stimulus-response instructions had to be implemented either verbally or manually either once or three times. Modality-specific procedural encoding was probed via a subsequent implicit priming test. This involved the same stimuli but required a response that could be either compatible or incompatible with the originally instructed response using either the same or a different response modality. We found that procedural encoding was modality-specific as indicated by a stronger repetition-dependent increase of the compatibility effect when response modality was unchanged. Explicit test performance, serving as a marker of declarative encoding, was independent of modality transition and it was uncorrelated with implicit test performance. Unexpectedly, the implicit priming test also revealed a small yet significant transfer to the response modality that was previously not overtly implemented, likely reflecting covert response "simulation". To examine if covertly simulated responding occurs even when instruction implementation is omitted altogether, we conducted Experiment 2. Subjects merely viewed novel stimulus-response instructions prior to testing. Again, we found evidence for procedural encoding of the non-implemented instructions. Moreover, a direct comparison of both experiments revealed higher test scores (both implicit and explicit) for previously non-implemented instructions than for previously implemented instructions. This calls for theoretical reconciliation with diverging previous study results.

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.

Functional integration processes underlying the instruction-based learning of novel goal-directed behaviors.

How does the human brain translate symbolic instructions into overt behavior? Previous studies suggested that this process relies on a rapid control transition from the lateral prefrontal cortex (LPFC) to the anterior striatum (aSTR) and premotor cortex (PMC). The present fMRI study investigated whether the transfer from symbolic to pragmatic stimulus-response (S-R) rules relies on changes in the functional coupling among these and other areas and to which extent action goal representations might get integrated within this symbolic-pragmatic transfer. Goal integration processes were examined by manipulating the contingency between actions and differential outcomes (i.e. action goals). We observed a rapid strengthening of the functional coupling between the LPFC and the basal ganglia (aSTR and putamen) and orbitofrontal cortex (OFC) as well as between the LPFC and the anterior dorsal PMC (pre-PMd), the anterior inferior parietal lobule (aIPL), and the posterior superior parietal lobule (pSPL). Importantly, only some of these functional integration processes were sensitive to the outcome contingency manipulation, including LPFC couplings with aSTR, OFC, aIPL, and pre-PMd. This suggests that the symbolic-pragmatic rule transfer is governed by principles of both, instrumental learning (increasingly tighter coupling between LPFC and aSTR/OFC) and ideomotor learning (increasingly tighter coupling between LPFC and aIPL/pre-PMd). By contrast, increased functional coupling between LPFC and putamen was insensitive to outcome contingency possibly indicating an early stage of habit formation under instructed learning conditions.

Priming of visual cortex by temporal attention? The effects of temporal predictability on stimulus(-specific) processing in early visual cortical areas.

In recent studies it has been shown that temporal predictability of expected events alters processing in perception and action. Yet, the neural mechanism(s) by which temporal predictability biases this processing is to date little understood. Therefore, in the present fMRI study we investigated how temporal predictability affects neural processing in visual cortical areas. For this, thirty-four participants either categorized the gender or the movement direction of vertically or horizontally moving faces in different blocks of trials. Temporal predictability of stimulus onset was manipulated by the presence or absence of an auditory alerting signal validly predicting stimulus onset. The behavioral data revealed a clear performance benefit for the presence of an alerting signal. Neuroimaging results showed that irrespective of the currently performed task temporal predictability significantly reduced activation in the primary visual cortex. This activation reduction correlated with the alerting signal-related performance benefit. Furthermore, we did not find a selective influence of increased temporal predictability on target-specific visual processing (faces or movement) in the respective material-specific visual brain areas. Together, these findings suggest an increased task-unspecific readiness by the alerting signal that might result in more efficient transmission of stimulus codes into response codes.

The many faces of preparatory control in task switching: reviewing a decade of fMRI research.

A large body of behavioural research has used the cued task-switching paradigm to characterize the nature of trial-by-trial preparatory adjustments that enable fluent task implementation when demands on cognitive flexibility are high. This work reviews the growing number of fMRI studies on the same topic, mostly focusing on the central hypothesis that preparatory adjustments should be indicated by enhanced prefrontal and parietal BOLD activation in task switch when compared with task repeat trials under conditions that enable advance task preparation. The evaluation of this straight-forward hypothesis reveals surprisingly heterogeneous results regarding both the precise localization and the very existence of switch-related preparatory activation. Explanations for these inconsistencies are considered on two levels. First, we discuss methodological issues regarding (i) the possible impact of different fMRI-specific experimental design modifications and (ii) statistical uncertainty in the context of massively multivariate imaging data. Second, we discuss explanations related to the multidimensional nature of task preparation itself. Specifically, the precise localization and the size of switch-related preparatory activation might depend on the differential interplay of hierarchical control via abstract task goals and attentional versus action-directed preparatory processes. We argue that different preparatory modes can be adopted relying either on advance goal activation alone or on the advance resolution of competition within action sets or attentional sets. Importantly, while either mode can result in a reduction of behavioral switch cost, only the latter two are supposed to be associated with enhanced switch versus repeat BOLD activation in prepared trial conditions.