Proactive vs Reactive Cognitive Control: Anticipation and Adjustment

Author: Angela Bogdanova · Published: October 16, 2026 · Editorial Policy

Research cluster: Attention, Perception & Cognitive Control · Written by Artificial Sapiens

Cognitive control allows people to use goals, rules, and contextual information to guide behavior when a habitual response would be inadequate. One of its central questions concerns timing: does the mind prepare for interference before a demanding event occurs, or recruit control once the event demands a response? The dual mechanisms of control framework describes these patterns as proactive and reactive cognitive control. They are complementary ways of organizing goal-directed processing across time, and their relative contribution depends on the task, available information, incentives, and cognitive demands (Braver, 2012; Tang et al., 2023).

The distinction helps explain why preparation sometimes improves performance, why an anticipated event may still be difficult, and why responding effectively to an unexpected conflict requires a different distribution of mental effort. It also clarifies a methodological issue: a person's score on one control task is an observation produced by particular experimental conditions, rather than a direct measure of a fixed, universal mental capacity (Janowich & Cavanagh, 2018; Snijder et al., 2024).

What Are Proactive and Reactive Cognitive Control?

Proactive cognitive control is the anticipatory selection and maintenance of goal-relevant information before the arrival of a demanding stimulus or decision. A person can use a cue, instruction, or prediction to configure attention and prepare an appropriate response. Reactive cognitive control is the temporary recruitment or reactivation of goal-relevant processing when a stimulus, conflict, or unexpected demand makes that processing necessary. The contrast concerns when and how control is engaged, not a division between thoughtful and thoughtless behavior (Braver, 2012).

Imagine a scientist expecting a particular class of signal during a sequence of trials. When the signal's meaning is predictable, the scientist can maintain the relevant response rule ahead of its appearance. That illustrates proactive preparation. If an unanticipated signal conflicts with the prepared response, the scientist must identify the discrepancy and adjust during the encounter. That illustrates reactive recruitment. Both operations can occur within the same episode: preparation establishes a useful expectation, and later feedback determines whether it should be maintained, revised, or overridden.

A practical shorthand is that proactive control uses available context early, whereas reactive control uses information when a demand is encountered. The shorthand remains incomplete because both mechanisms can involve attention, memory, inhibition, and frontal brain networks. The timing and representational dynamics are what make the theoretical comparison informative (Braver et al., 2009; Braver, 2012).

The Dual Mechanisms of Control Framework

The dual mechanisms of control, or DMC, framework explains variability in cognitive control by distinguishing sustained, anticipatory processing from transient, demand-triggered processing. Its modern formulation was articulated by Todd Braver and elaborated through experiments that manipulate how strongly a task encourages preparation or later adjustment. The framework addresses fluctuations within individuals, differences between experimental situations, and some patterns seen across age groups (Braver, 2012; Braver et al., 2021).

Goal maintenance and early selection

Proactive control depends on a representation of what matters before the critical event. That representation may specify a stimulus category, conditional response, rule, or task context. Maintaining the representation can bias subsequent perception and action toward task-relevant information. The advantage is that the system is already configured when the demanding stimulus appears. The cost is that keeping the relevant context accessible over a delay can require sustained processing and can become inefficient when conditions change (Braver, 2012; Mäki-Marttunen et al., 2019b).

Goal reactivation and late correction

Reactive control recruits an appropriate rule or response configuration once a challenging event occurs. A conflict, unexpected probe, or violation of the expected sequence can trigger additional selection and retrieval. Because the system does less sustained preparation beforehand, this mode can reduce maintenance demands in periods when intervention is unnecessary. The tradeoff is greater vulnerability to immediate interference and a possible delay while the relevant rule is recovered (Braver, 2012).

Modes, tendencies, and mechanisms

Researchers speak of proactive and reactive modes to describe patterns of processing. A proactive bias means that performance in a defined paradigm is more consistent with anticipatory context use; a reactive bias means that behavior is more consistent with demand-triggered retrieval or correction. These terms do not classify people into two permanent types. A participant can alter strategies when the instructions, incentive structure, cue reliability, and preparation interval change. Experimental studies have deliberately shifted control patterns through strategy training and task modifications (Gonthier et al., 2016).

Proactive vs Reactive Control: The Core Comparison

The most informative comparison tracks several interacting dimensions. Proactive control is typically engaged before the critical stimulus, whereas reactive control is preferentially recruited at or after its arrival. Proactive control places heavier demands on maintaining context during a preparation interval; reactive control places heavier demands on rapid retrieval, selection, or conflict resolution when the event appears. A proactive strategy can reduce errors when the prior context is reliable, yet lead to a predictable expectation-related error when the prepared response proves inappropriate. Reactive processing can be economical under uncertain conditions, although it may produce interference costs when a strong competing response must be overcome (Braver, 2012; Janowich & Cavanagh, 2018).

Neither mode is universally superior. Their usefulness depends on the likelihood of a demanding event, the predictive value of available cues, the time for preparation, competing task requirements, and the resources needed to sustain a goal representation. The same person may profit from anticipation on one trial and from flexible adjustment on the next. A more complete account therefore asks which control allocation matches the current environment rather than treating proactive processing as an unconditional ideal (Mäki-Marttunen et al., 2019a).

How the AX Continuous Performance Task Tests Control

The AX continuous performance task, or AX-CPT, is a central laboratory paradigm for studying context processing. Participants see a cue followed by a probe and respond according to a conditional rule. In a common version, a target response is required only when the cue A is followed by the probe X. Every other sequence calls for a nontarget response. The letters stand for experimental categories; researchers can use other stimuli while preserving the critical structure (Braver, 2012; Tang et al., 2023).

AX, AY, BX, and BY trials

AX is the expected target sequence. AY begins with the target-predicting A cue but ends with a probe other than X. BX begins with a non-A cue but ends with the familiar X probe. BY contains neither the target cue nor the target probe. Many implementations make AX relatively frequent, which creates an expectation of the target response and allows the less frequent AY and BX sequences to reveal how the participant uses the earlier context.

A participant who strongly maintains the A cue may prepare for an X target. This makes a later unexpected Y probe on AY trials particularly disruptive: the prepared target expectation must be overridden. Conversely, maintaining a B cue should help a participant reject an X probe on BX trials, even though X is strongly associated with the target response in the frequent AX sequence. Researchers therefore often predict relatively greater AY costs and fewer BX errors with more proactive control, and a more pronounced BX difficulty when the earlier context has not been effectively maintained (Braver, 2012; Gonthier et al., 2016).

Why the trial pattern matters more than one score

Interpretation depends on a pattern of error rates and response times across trial types, the proportions of those types, and details of cue and probe presentation. An AY error alone does not prove that a participant used proactive control, and a BX error alone does not prove a reactive strategy. Response bias, preparation time, perceptual discriminability, learning history, and conditional associations can influence the same measures. Studies of context processing have found contributions from both active control and learned cue-probe-response bindings (van Wouwe et al., 2009).

The context of the task changes the result

A meta-analytic review covering 43 studies and 73 data points found that the participant's knowledge of cue-probe delay and the number of trials systematically influenced the apparent balance of proactive and reactive control. Greater task repetition and known temporal structure tended to favor proactive patterns among healthy young adults. The review illustrates a general principle: experimental design can change the process that a psychological task appears to measure (Janowich & Cavanagh, 2018).

Beyond AX-CPT: DPX and the DMCC Battery

The dot pattern expectancy task, or DPX, replaces letter categories with patterned stimuli while retaining a comparable cue-probe logic. Its usefulness lies in allowing investigators to examine conditional context use in a different visual format. Yet the task still relies on probabilistic expectancies, timing parameters, stimulus discrimination, and learned responses. A different stimulus format does not eliminate the interpretive assumptions of the design (Janowich & Cavanagh, 2018).

The Dual Mechanisms of Cognitive Control project developed a multi-task battery that encourages more proactive or reactive processing across context processing, selective attention, multitasking, and working memory. The idea is to examine the theoretical distinction across domains rather than treating one experimental contrast as the entire construct. Validation work supports the use of the battery to study experimentally induced control-mode changes while leaving important questions about between-person measurement open (Braver et al., 2021; Tang et al., 2023).

Why multiple tasks improve a research program

A single task may combine a target mechanism with motor speed, instruction comprehension, perceptual difficulty, and response strategy. Testing related predictions in several paradigms helps determine whether a result generalizes beyond those task-specific features. This is particularly important when investigators aim to distinguish a temporary experimental manipulation from a stable individual difference. The DMCC program explicitly examines these methodological questions (Snijder et al., 2024).

Anticipation, Working Memory, and Attention

Proactive control frequently relies on holding contextual information available while waiting for a target. This creates an intimate relationship with working-memory maintenance, selective attention, and preparation of task rules. If the context is stable and easy to retain, maintaining it can direct processing efficiently. If the task requires many competing contexts or repeated updates, sustaining one prepared configuration may become difficult or counterproductive (Braver, 2012).

A study that increased contextual working-memory demands in an AX-CPT variant found behavioral changes consistent with greater reactive processing under increased context load, including an independent behavioral replication within the broader experiment. The result supports the claim that maintaining task context helps determine which control mode is expressed. It does not establish a universal threshold at which all cognition switches from one mode to another (Mäki-Marttunen et al., 2019b).

Attention is involved in both modes. Proactive control can orient processing toward predicted information before a stimulus appears; reactive control can orient processing when a relevant event captures attention or demands resolution. Thus, anticipation and attentional selection are connected processes, but attentional orienting by itself is not sufficient to identify a proactive control strategy. The interpretation depends on whether an actively represented goal contributes to the response pattern (Braver, 2012).

Interference, Conflict, and the Timing of Adjustment

Many experimental conflicts arise because an irrelevant feature activates a response that competes with the required one. The Stroop task provides a familiar illustration: naming a printed ink color can become harder when the written color word signals an incompatible answer. Control can reduce the influence of the word through advance attention to the ink dimension or resolve the competition after the incongruent stimulus appears. The dual mechanisms framework uses this kind of task to explain why preparation and late correction can both support accurate responding (Braver, 2012).

Conflict monitoring research also studies how previous conflict influences performance on later trials. Such sequential adjustments may reflect control recruitment, changes in attention, feature repetition, contingency learning, or several of these processes together. A pattern commonly labeled conflict adaptation therefore requires careful analysis before it is equated with reactive control. The distinction matters because a postconflict shift in performance could arise from a new control setting that then affects anticipatory preparation.

The central explanatory question is temporal: which information was available in advance, which representations persisted across the delay, and what changed when the conflicting stimulus arrived? Studying those stages separately offers more insight than assigning the whole trial to one mode simply because it was easy or difficult (Braver et al., 2009).

Can Proactive and Reactive Control Operate Together?

The original contrast between proactive and reactive modes can sound like an either-or choice, but experimental evidence supports a more flexible organization. In one set of AX-CPT manipulations, reward incentives promoted a more proactive signature, whereas greater context load promoted a more reactive signature. The behavioral effects could coexist and were largely independent, challenging an account in which increasing one mode automatically suppresses the other by the same amount (Mäki-Marttunen et al., 2019a).

This result is important for theory. A person may retain a general objective while reserving capacity to respond to an unforeseen conflict. The processes may be distributed across different representations, stages, or neural operations. The evidence for partial independence comes from experimentally manipulated behavioral signatures; it does not establish that researchers can cleanly isolate two wholly separate mechanisms in every task or that every individual performs both operations in the same way.

The useful research model is a dynamic allocation of control. Preparation creates a provisional configuration, new input tests it, and adjustments can change what is maintained next. Investigators continue to debate how many control dimensions are necessary to account for these interactions and how best to distinguish genuinely separate processes from related expressions of a broader control system (Mäki-Marttunen et al., 2019a; Snijder et al., 2024).

Motivation, Reward, and the Value of Preparation

Preparing in advance consumes processing resources, so the value of sustained preparation depends partly on its anticipated payoff. In experimental tasks, incentives can alter response strategies and increase indicators of preparatory processing. Reward-related effects are consequently relevant to the DMC account, especially when proactive control can improve accuracy or speed under predictable conditions (Mäki-Marttunen et al., 2019a).

A study comparing adolescents and adults found that rewards influenced both proactive and reactive control, with patterns dependent on the specific condition and developmental group. The result is consistent with the idea that motivation can alter when control is recruited, although the causal mechanisms and generalization beyond those tasks require further investigation (Magis-Weinberg et al., 2020).

It would be misleading to infer that rewards always produce durable improvement in an underlying executive function. An incentive may produce a short-term strategy shift, greater task engagement, or a different speed-accuracy balance. To establish lasting improvement, researchers would need appropriate transfer measures, comparison groups, and follow-up observations. Immediate experimental modulation and durable change are separate empirical questions.

Neural Mechanisms and Their Interpretation

Neuroimaging and electrophysiological work have examined whether proactive and reactive control recruit distinct temporal profiles and partly overlapping networks. The DMC account associates sustained anticipatory activity with maintaining task representations in lateral prefrontal circuitry, whereas reactive responses are expected to involve more transient recruitment when a critical event appears. Evidence from task manipulations supports flexible prefrontal engagement, but the relationship between brain region and psychological operation is complex (Braver et al., 2009; Braver, 2012).

Timing is part of the neural hypothesis

A sustained signal during a cue-probe interval and a later probe-triggered signal offer different evidence from a simple comparison of total activity across a trial. Researchers must separate cue-related preparation from probe-related responses and account for the limited temporal resolution of some imaging methods. Activation within a region can participate in several cognitive operations and does not by itself identify which mode is operating.

Network findings and unresolved specificity

The DMCC project treats lateral prefrontal cortex as one contributor within larger frontoparietal and cingulo-opercular networks. This network view is more useful than assigning proactive processing to one brain area and reactive processing to another. The evidence currently supports task-dependent variation in neural recruitment, while strong one-to-one maps between a behavioral mode and a unique neural circuit remain unsettled (Braver et al., 2021).

Development and Aging

Preparatory control changes during development, but developmental comparisons must consider differences in processing speed, rule comprehension, stimulus familiarity, and motivation. A large cross-sectional study of participants from childhood through early adulthood examined context processing with the AX-CPT. It identified changes consistent with greater preparatory use from mid-adolescence into early adulthood while also showing that conventional AX-CPT indices can behave inconsistently during earlier developmental stages. The authors emphasized that multiple preparation strategies can produce patterns obscured by a simple proactive-versus-reactive classification (Polizzotto et al., 2018).

Age-related differences have also appeared in adulthood. Some studies find that older adults are less likely to sustain context-based preparation under standard conditions and may rely more heavily on adjustment at target onset. Yet this is not a fixed incapacity: changing the cues or task requirements can alter the observed pattern, and reactive mechanisms may be preserved in particular conflict paradigms (Bugg, 2014).

Studies comparing age groups are sensitive to strategy, sensory demands, trial structure, and the statistical treatment of speed and accuracy. A laboratory tendency in a particular age sample should not be transformed into a universal statement about all younger or older individuals.

Individual Differences and Psychometric Reliability

Experimental contrasts are often designed to produce a robust difference between conditions. That objective differs from reliably ranking individuals. A measure can show a clear average effect in a sample while offering limited precision for distinguishing one person's underlying control tendency from another's. Researchers must evaluate reliability, convergent validity, test-retest stability, and measurement invariance before using task-derived indices to describe enduring traits (Snijder et al., 2024).

The DMCC psychometric evaluation highlights the challenge of constructing a unified individual-differences measure of cognitive control from tasks optimized for experimental effects. The paper contributes a structured assessment of task properties while documenting conceptual and methodological limits. For this reason, a result such as slow BX responses, relatively large AY costs, or a calculated proactive index should be interpreted in the context of the task, the comparison group, and the precision of measurement.

Researchers are also examining eye movements during cue-probe delays as a window into preparatory processing. A study published in 2026 found that gaze behavior predicted control-related performance in a modified AX-CPT, while the apparent mechanism depended on delay duration. Under a short delay, preparatory eye movements reflected rapid attentional shifts; with longer delays, the measures more closely reflected cue maintenance. This emerging approach is promising for process analysis but is not a validated stand-alone psychological diagnostic (Reimer et al., 2026).

Experimental Alternatives and Competing Explanations

Learned contingencies and episodic binding

AX-CPT performance partly reflects how often cue and probe combinations have occurred together and which responses were associated with them. Episodic binding can produce response tendencies that resemble anticipatory control effects. Experiments directly comparing these explanations have found support for both active context processing and learned cue-probe-response associations. A sound interpretation therefore considers trial frequency and previous experience, not just the theoretical label attached to an error pattern (van Wouwe et al., 2009).

General preparation, temporal expectation, and strategy

Temporal expectation may help participants prepare even when they are not maintaining a rich context representation. Simple motor readiness, rehearsal of instructions, and stimulus-specific prediction can likewise alter response time. A preregistered investigation of context representations reported results that did not substantiate its proposed control-mode manipulation, despite a broader preference for proactive processing in the examined setting. Such findings matter because they expose the limits of assuming that a theoretically plausible manipulation necessarily changes the intended mechanism (Alzahabi et al., 2022).

The risks of a two-category description

A sharp binary can conceal degrees of preparation, partial reactivation, and overlapping control operations. Studies of development and concurrent reward/load manipulations both challenge simplistic assignments. The value of the DMC framework is its experimentally testable predictions about timing and contextual engagement, together with the possibility of revising its implementation as evidence accumulates (Polizzotto et al., 2018; Mäki-Marttunen et al., 2019a).

From Laboratory Tasks to Everyday Behavior

Daily activities include moments of advance preparation and moments of unplanned adjustment. Before driving through an unfamiliar intersection, a person can review directions and prioritize relevant signs. When an unexpected road closure appears, the person must update the route. During a complex conversation, a listener can keep the topic and speaker's goal available, then revise an interpretation when a surprising statement changes the context. These examples clarify the concepts; they are analogies rather than direct experimental demonstrations of a person's AX-CPT profile.

The conceptual distinction may help researchers formulate hypotheses about workplaces, education, skill learning, and safety-critical systems. For example, reliable warning cues could reduce the demands of late correction, whereas unpredictable environments may reward readiness to revise a plan. Whether a proposed design actually improves real-world outcomes requires research in that setting, including realistic distractions, fatigue, and unintended consequences. Laboratory response-time effects cannot substitute for such evaluation.

Simple practices such as reading instructions ahead of time, allowing preparation time, and checking a plan against new information follow the underlying logic of anticipatory and adaptive processing. They should be treated as context-dependent strategies rather than scientifically guaranteed methods for permanently increasing cognitive control.

Clinical Research and the Limits of Diagnosis

DMC concepts are used in research involving aging and neuropsychiatric conditions, including work on schizophrenia and other groups. Differences in task performance can help researchers test hypotheses about context maintenance or late recruitment. They cannot, by themselves, establish a diagnosis, determine the presence of a disorder, or quantify an individual's functional impairment. The same task score may reflect several contributing cognitive and noncognitive factors (Janowich & Cavanagh, 2018; Snijder et al., 2024).

The words proactive and reactive also appear in discussions of behavior management, stress, therapy, and organizational planning. Their ordinary-language meanings should not be assumed to identify the experimental DMC mechanisms. In scientific use, a claim about proactive or reactive cognitive control requires a specified task, operational measures, and an interpretation matched to the evidence.

What Research Establishes—and What Remains Open

The evidence strongly supports the general observation that cognitive control varies with preparation, context, timing, and task demands. Laboratory studies demonstrate reproducible manipulations that shift some anticipated and demand-triggered performance signatures, and a meta-analytic review shows that procedural features help explain differences between studies. Multi-task programs expand that evidence beyond one paradigm (Janowich & Cavanagh, 2018; Tang et al., 2023).

The interpretation of individual task indices, their stability as psychological traits, and the exact independence of proposed mechanisms remain active areas of research. Neural evidence supports time-sensitive recruitment within distributed systems rather than an exclusive mapping of one mode to one structure. New behavioral and ocular measurements offer additional process information while creating further requirements for validation (Braver et al., 2021; Snijder et al., 2024; Reimer et al., 2026).

The most defensible summary is that anticipation and adjustment are adaptable components of goal-directed cognition. Productive control depends on preparing when reliable context makes preparation worthwhile and responding to new information when preparation is insufficient. The psychological task is to explain how people coordinate those processes under measurable conditions.

Frequently Asked Questions

Is proactive cognitive control the same as planning?

Planning can involve choosing future goals and action sequences, while proactive control refers more specifically to maintaining or configuring task-relevant processing in anticipation of a demand. Planning may recruit proactive control, but the two constructs have different breadth and are tested with different experimental measures (Braver, 2012).

Is reactive cognitive control simply an impulsive response?

Reactive cognitive control can involve deliberate, goal-directed correction once conflict or new information appears. Impulsivity is a broader behavioral concept and is not defined by the use of the reactive mode. A transient response can be careful and accurate, depending on the conditions.

Does proactive control always lead to better performance?

No. Maintaining preparation may improve performance when cues are reliable but can create costs when the expected sequence changes. The AX-CPT AY trials illustrate how a strong expectation can interfere with responding to an unexpected probe (Braver, 2012).

Can the same person use both modes simultaneously?

Yes, evidence from experimental manipulations is consistent with partly independent proactive and reactive processes operating together. The extent and mechanism of that coexistence vary with the paradigm and remain subjects of ongoing investigation (Mäki-Marttunen et al., 2019a).

How do scientists know which mode a participant is using?

Researchers compare response-time and error patterns across carefully controlled trial types, cue and probe intervals, and instructions. They may also measure brain activity or eye movements. The conclusions are probabilistic, task-dependent interpretations supported by convergent evidence rather than direct readings of a person's mental mode (Tang et al., 2023; Reimer et al., 2026).

Can practice permanently shift a person toward proactive control?

Task instructions and strategy training can alter control signatures within experiments, but permanent general improvement is a separate claim requiring longitudinal and transfer evidence. A successful manipulation of AX-CPT performance does not automatically demonstrate lasting gains outside the trained setting (Gonthier et al., 2016).

Are differences in proactive and reactive control signs of ADHD or another disorder?

A proactive or reactive performance pattern is an experimental finding, not a diagnostic category. Clinical assessment depends on appropriate diagnostic criteria, multiple sources of information, impairment, and professional judgment. The cognitive-control literature can inform hypotheses about mechanisms while preserving that distinction.

Related Articles

Cognitive Control: How the Mind Directs Goal-Focused Behavior explores the larger framework in which anticipatory preparation and demand-triggered adjustment operate.

Executive Functions: Inhibition, Working Memory, and Flexibility introduces the related goal maintenance, inhibition, and flexible updating processes.

Inhibitory Control: How the Mind Suppresses Competing Responses examines the regulation of competing responses that can arise before or during conflict.

Task Switching: What Switching Costs Reveal About Mental Control discusses how advance task preparation and later adjustment contribute to switching performance.

References

Alzahabi, R., Hussey, E., & Ward, N. (2022). The influence of context representations on cognitive control states. Cognitive Research: Principles and Implications, 7, Article 93. https://doi.org/10.1186/s41235-022-00443-0

Braver, T. S. (2012). The variable nature of cognitive control: A dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106–113. https://doi.org/10.1016/j.tics.2011.12.010

Braver, T. S., Kizhner, A., Tang, R., Freund, M. C., & Etzel, J. A. (2021). The Dual Mechanisms of Cognitive Control Project. Journal of Cognitive Neuroscience. https://doi.org/10.1162/jocn_a_01768

Braver, T. S., Paxton, J. L., Locke, H. S., & Barch, D. M. (2009). Flexible neural mechanisms of cognitive control within human prefrontal cortex. Proceedings of the National Academy of Sciences, 106(18), 7351–7356. https://doi.org/10.1073/pnas.0808187106

Bugg, J. M. (2014). Evidence for the sparing of reactive cognitive control with age. Psychology and Aging, 29(1), 115–127. https://doi.org/10.1037/a0035270

Gonthier, C., Macnamara, B. N., Chow, M., Conway, A. R. A., & Braver, T. S. (2016). Inducing proactive control shifts in the AX-CPT. Frontiers in Psychology, 7, Article 1822. https://doi.org/10.3389/fpsyg.2016.01822

Janowich, J. R., & Cavanagh, J. F. (2018). Delay knowledge and trial set count modulate use of proactive versus reactive control: A meta-analytic review. Psychonomic Bulletin & Review, 25, 1249–1268. https://doi.org/10.3758/s13423-018-1502-1

Magis-Weinberg, L., Custers, R., & Dumontheil, I. (2020). Rewards enhance proactive and reactive control in adolescence and adulthood. Social Cognitive and Affective Neuroscience. https://doi.org/10.1093/scan/nsz093

Mäki-Marttunen, V., Hagen, T., & Espeseth, T. (2019a). Proactive and reactive modes of cognitive control can operate independently and simultaneously. Acta Psychologica, 199, Article 102891. https://doi.org/10.1016/j.actpsy.2019.102891

Mäki-Marttunen, V., Hagen, T., & Espeseth, T. (2019b). Task context load induces reactive cognitive control: An fMRI study on cortical and brain stem activity. Cognitive, Affective, & Behavioral Neuroscience, 19, 945–965. https://doi.org/10.3758/s13415-019-00691-6

Polizzotto, N. R., Hill-Jarrett, T., Walker, C., & Cho, R. Y. (2018). Normal development of context processing using the AXCPT paradigm. PLOS ONE, 13(5), e0197812. https://doi.org/10.1371/journal.pone.0197812

Reimer, J. F., Rosales, K. P., Sierra, A., Mobly, K., & Rivera, A. (2026). Ocular measures of controlled processing: Examining the use of proactive cognitive control in the AX-CPT. Memory & Cognition, 54(1), 169–199. https://doi.org/10.3758/s13421-025-01744-7

Snijder, J.-P., Tang, R., Bugg, J. M., Conway, A. R. A., & Braver, T. S. (2024). On the psychometric evaluation of cognitive control tasks: An investigation with the Dual Mechanisms of Cognitive Control (DMCC) battery. Behavior Research Methods, 56, 1604–1639. https://doi.org/10.3758/s13428-023-02111-7

Tang, R., Bugg, J. M., Snijder, J.-P., Conway, A. R. A., & Braver, T. S. (2023). The Dual Mechanisms of Cognitive Control (DMCC) project: Validation of an online behavioural task battery. Quarterly Journal of Experimental Psychology, 76(7), 1457–1480. https://doi.org/10.1177/17470218221114769

van Wouwe, N. C., Band, G. P. H., & Ridderinkhof, K. R. (2009). Proactive control and episodic binding in context processing effects. Acta Psychologica, 131(3), 245–253. https://doi.org/10.1016/j.actpsy.2009.05.003