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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.

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Current models of multitasking assume that dual-task performance and the degree of multitasking are affected by cognitive control strategies. In particular, cognitive control is assumed to regulate the amount of shielding of the prioritised task from crosstalk from the secondary task. We investigated whether and how task shielding is influenced by mood states. Participants were exposed to two short film clips, one inducing high and one inducing low arousal, of either negative or positive content. Negative mood led to stronger shielding of the prioritised task (i.e., less crosstalk) than positive mood, irrespective of arousal. These findings support the assumption that emotional states determine the parameters of cognitive control and play an important role in regulating dual-task performance.

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Goal-directed behavior is based on representations of contingencies between a certain situation (S), a certain (re)action (R) and a certain outcome (O). These S-R-O representations enable flexible response selection in different situations according to the currently pursued goal. Importantly however, the successful formation of such representations is a necessary but not sufficient precondition for goal-directed behavior which additionally requires the actual usage of the contingency information for action control. The present fMRI study aimed at identifying the neural basis of each of these two aspects: representing vs. explicitly using experienced S-R-O contingencies. To this end, we created three experimental conditions: S-R-O contingency present and used for outcome-based response selection, S-R-O contingency present but not used, and S-R-O contingency absent. The comparison between conditions with and without S-R-O contingency revealed that the angular gyrus is relevant for representing S-R-O contingencies. The explicit usage of learnt S-R-O representations in turn was associated with increased functional coupling between angular gyrus and several subcortical (hippocampus, caudate head), prefrontal (lateral orbitofrontal cortex (OFC), rostrolateral prefrontal cortex (RLPFC)) and cerebellar areas, which we suggest represent different explicit and implicit processes of goal-directed action control. Hence, we ascribe a central role to the angular gyrus in associating actions to their sensory outcomes which is used to guide behavior through coupling of the angular gyrus with multiple areas related to different aspects of action control.

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.

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.