The ability to self-evaluate performance derives from metacognition, enabling us to monitor and control our behaviour. Metacognition is generally thought to accompany decision making automatically and effortlessly, with inefficiencies in metacognitive judgment attributed to intrinsic properties of the computations that support it. Contrary to this view, we show that metacognitive judgments are mentally demanding. Apparent inefficiencies can arise from avoidance of metacognitive effort when people engage in rational trade-offs between the costs and benefits of metacognition. We developed a flexible, effort-based decision making paradigm that allowed participants to trade-off monetary rewards to make metacognitive judgments easier. First, individuals sacrificed rewards to simplify confidence self-evaluation, indicating that confidence ratings are mentally effortful. Second, we show that incentivising self-evaluation reduces confidence leaks, a well-characterised bias in metacognitive judgments widely regarded as a persistent metacognitive inefficiency. Our results challenge the assumption that metacognitive judgments are effortless and motivate a re-evaluation of metacognitive inefficiency, especially in populations where metacognition is presumed to be inherently deficient.

Dexterous finger movements are critical for both everyday and specialized tasks. However, acquiring such skills is challenging, as it requires accurate sequence memory and fine finger coordination. Existing haptic training systems typically employ demonstration feedback, which physically guides correct movements, or post-error correction, which intervenes after errors occur. While effective, these approaches can reduce learners' autonomy or expose novices to repeated errors, which can harm motivation. We introduce FIXical I/O, a magnetic hand exoskeleton that enables three error feedback strategies by combining real-time motion sensing with electromagnet-based actuation: Preemptive Error Correction (nudging fingers away from incorrect actions), Preemptive Error Blocking (constraining erroneous movements before execution), and Post-Error Correction. We conducted a user study comparing these strategies in terms of learning performance and subjective experiences, such as perceived performance and sense of agency, thereby demonstrating the benefits of Preemptive Error Correction and providing its design implications.

Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus–response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over non-critical features. Using EEG decoding across three days of practice, we found that practice enhanced context-specific conjunctive representations and progressively made their neural dynamics more stable and abstract—consistent with a practice-optimized representational geometry.

Making context-dependent decisions incurs cognitive costs. Cognitive control studies have investigated the nature of such costs from both computational and neural perspectives. In this paper, we offer an information-theoretic account of the costs associated with context-dependent decisions. According to this account, the brain's limited capacity to store context-dependent policies necessitates "compression" of policies into internal representations with an upper bound on codelength, quantified by an information-theoretic measure (policy complexity). These representations are decoded into actions by sequentially inspecting each bit, such that longer codes take more time to decode. When a response deadline is imposed, the account predicts that policy complexity should increase with the deadline. Higher policy complexity is associated with several behavioral signatures: higher accuracy, lower variability, and lower perseveration. Using EEG, we found neural signatures consistent with policy complexity in prefrontal cortex activity.

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection at different states in neural trajectories. The results show that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilize in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

Working memory (WM) is a goal-directed memory system that actively maintains a limited amount of task-relevant information to serve the current goal. By this definition, WM maintenance should be terminated after the goal is accomplished, spontaneously removing no-longer-relevant information from WM. Past studies have failed to provide direct evidence of spontaneous removal of WM content by allowing participants to engage in a strategic reallocation of WM resources to competing information within WM. By contrast, we provide direct neural and behavioral evidence that visual WM content can be largely removed less than 1 second after it becomes obsolete, in the absence of a strategic allocation of resources (total N = 442 adults).

For flexible goal-directed behavior, prioritizing and selecting a specific action among multiple candidates are often important. Working memory has long been assumed to play a role in prioritization and planning, while bridging cross-temporal contingencies during action selection. However, studies of working memory have mostly focused on memory for single components of an action plan, such as a rule or a stimulus, rather than management of all of these elements during planning. Using EEG decoding, we show that people can maintain multiple conjunctive representations of planned actions in parallel, that these representations are prioritized according to expected utility, and that selective output gating determines which action is ultimately executed.

Action selection appears to rely on conjunctive representations that nonlinearly integrate task-relevant features. Here, we tested a corollary of this hypothesis: that such representations are also intricately involved during attempts to stop an action—a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the electroencephalogram signal in a combined rule-selection and stop-signal paradigm. Across two experiments (N = 57), we found that the strength of decoded conjunctive representations prior to the stop signal uniquely predicted trial-by-trial stopping success, and that these representations were selectively suppressed following the onset of the stop signal.

Cognitive control allows us to think and behave flexibly based on our context and goals. At the heart of theories of cognitive control is a control representation that enables the same input to produce different outputs contingent on contextual factors. In this review, we focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. We discuss the implications of this trade-off for cognitive control, briefly review current neuroscience findings regarding the dimensionality of control representations in the brain—particularly the prefrontal cortex—and conclude by highlighting open questions and crucial directions for future research.

People can use abstract rules to flexibly configure and select actions for specific situations, yet how exactly rules shape actions toward specific sensory and/or motor requirements remains unclear. Both research from animal models and human-level theories of action control point to the role of highly integrated, conjunctive representations, sometimes referred to as event files. These representations are thought to combine rules with other, goal-relevant sensory and motor features in a nonlinear manner and represent a necessary condition for action selection. However, so far, no methods exist to track such representations in humans during action selection with adequate temporal resolution. Here, we applied time-resolved representational similarity analysis to the spectral-temporal profiles of EEG signals while participants performed a cued, rule-based action selection task. We found that conjunctive representations were the strongest and most robust predictor of trial-by-trial variability in action selection performance, consistent with the hypothesis that conjunctive representations are a necessary condition for action selection.

Humans have a unique ability to perceive shape in different ways. Although we naturally estimate objective (physical) shape in our daily interactions with the world, we are also capable of estimating projective (retinal) shape, especially when attempting to accurately draw objects and scenes. In four experiments, we demonstrated robust effects of 3D context on shape perception. Using a binocular stereo paradigm, we presented rectangular surfaces of varying widths alone or embedded in a polyhedron. We found that the presence of even a small amount of 3D context aids objective judgments but hinders projective judgments, whereas a lack of context had the opposite effect. These results demonstrate that the typical presence of 3D context aids shape perception (shape constancy) while simultaneously making the projective judgments necessary for realistic drawing more difficult.

Theoretical considerations and results from individual differences studies suggest that working memory and conflict resolution are interrelated functions. Yet, there is little direct evidence suggesting that they actually share common cognitive resources. To study how overcoming conflict influences the maintenance of working memory representations and vice versa, we conducted four experiments using a dual-task paradigm in which both working memory load and level of conflict were independently manipulated. Across the four experiments, we found no consistent interaction between level of conflict and working memory load on working memory performance. These findings present at best weak evidence for the hypothesis that the maintenance of task goals in working memory is critical for successful conflict resolution.

In competitive situations, winning depends on selecting actions that surprise the opponent. Such unpredictable action can be generated based on representations of the opponent's strategy and choice history (model-based counter-prediction) or by choosing actions in a memory-free, stochastic manner. Across five different experiments using a variant of a matching-pennies game with simulated and human opponents, we found that people toggle between these two strategies, using model-based selection when recent wins signal the appropriateness of the current model, but reverting to stochastic selection following losses. Also, after wins, feedback-related mid-frontal EEG activity reflected information about the opponent's global and local strategy and predicted upcoming choices. After losses, this activity was nearly absent—indicating that the internal model is suppressed after negative feedback.

Models of action control assume that attentional control settings regulate the processing of lower-level stimulus/response representations. Yet, little is known about how exactly control and sensory/response representations relate to each other to produce goal-directed behavior. Addressing this question requires time-resolved information about the strength of the different, potentially overlapping representations, on a trial-by-trial basis. Using a cued task-switching paradigm, we show that information about relevant representations can be extracted through decoding analyses from the scalp EEG signal with high temporal resolution. Peaks in representational strength—indexed through decoding accuracy—proceeded from superficial task cues, to stimulus locations, to features/responses. In addition, attentional-set representations were prominent throughout almost the entire processing cascade, and trial-to-trial variations in task-set strength emerged as a remarkably strong predictor of variability in performance, both within and between individuals.

Despite strong theoretical reasons for assuming that abstract representations organize complex action sequences in terms of subplans (chunks) and sequential positions, we lack methods to directly track such content-independent, hierarchical representations in humans. We applied time-resolved, multivariate decoding analysis to the pattern of rhythmic EEG activity that was registered while participants planned and executed individual elements from pre-learned, structured sequences. Across three experiments, the theta and alpha-band activity coded basic elements and abstract control representations, in particular, the ordinal position of basic elements, but also the identity and position of chunks. Further, a robust representation of higher level, chunk identity information was only found in individuals with above-median working memory capacity, potentially providing a neural-level explanation for working-memory differences in sequential performance. Our results suggest that by decoding oscillatory activity we can track how the cognitive system traverses through the states of a hierarchical control structure.

Selection and preparation of action plans (task sets) is often assumed to occur in working memory (WM). Yet, the absence of consistent evidence that WM capacity and task selection efficiency are correlated raises questions about the functional relationship between these two aspects of executive control. We used the EEG-derived contralateral delay activity (CDA) to index the WM load of task sets. We found a CDA set size effect for high-WM, but not low-WM, individuals when S-R sets were novel. In contrast, when only four task sets were presented throughout the experiment, we observed a sustained yet set size-independent use of WM for high-WM participants. Moreover, a second experiment showed an increase of the CDA in situations with task conflict. Combined, these results indicate that even highly familiar S-R settings are maintained in WM, albeit in a compressed manner, presumably through cues to long-term memory representations.

Trial-to-trial carry-over of task sets (i.e., task-set inertia) is often considered a primary reason for task-switch costs. Yet, we know little about the dynamics of such carry-over effects—in particular how much they are driven by the most recent trial rather than characterized by a more continuous memory gradient. Using eye-tracking in a 3-task switching paradigm, we found strong evidence for more interference from recent than from less-recent tasks, and that interference from pre-switch trials contributed substantially to the overall pattern of response-time switch costs. Task-set carry-over was dominated by the most-recent trial when subjects could expect task repetitions, but when tasks were selected randomly, interference from the most recent trial decreased while interference from less-recent trials increased. Carry-over interference dynamics were thus characterized both by a gradual recency gradient and expectations about task-transition probabilities.

It is almost a truism that language aids serial-order control through self-cuing of upcoming sequential elements. We measured speech onset latencies as subjects performed hierarchically organized task sequences while "thinking aloud" each task label. Surprisingly, speech onset latencies and response times were highly synchronized, a pattern that is not consistent with the hypothesis that speaking aids proactive retrieval of upcoming sequential elements during serial-order control. We also found that when instructed to do so, subjects were able to speak task labels prior to presentation of response-relevant stimuli and that this substantially reduced RT signatures of retrieval—however, at the cost of more sequencing errors. Thus, while proactive retrieval is possible in principle, in natural situations it seems to be prevented through a strong tendency to synchronize speech and action. We suggest that this tendency may support context updating rather than proactive control.