You are reading a book in your local coffeeshop, when your focus gets broken by the couple at the next table, passionately discussing mortgage rates. To minimise this interruption your brain engages in ‘cognitive control’, resolving conflicts between competing stimuli to prioritise one over another.

Having finally regained your focus, another distraction emerges, this time of a different nature. Does your brain use the same mental mechanisms as before, and therefore a common brain circuit? Or does each kind of stimulus require a specific process?

Tasks that involve successively presenting different distractors can help explore these questions by testing for a process known as generalization: if the same mental mechanism underpins the resolution of all conflicts, distractors should become easier to ignore after the first trial.

Based on this paradigm, Yang et al. recorded brain activity during a modified version of a spatial Stroop-Simon task. Participants were asked to press a left or right button based on whether an arrow was pointing up or down, with both the vertical and horizontal position of the symbol potentially causing interference. For instance, accurate decision-making may be impaired when an arrow ‘down’ the bottom of the screen is pointing up (Stroop effect); or when participants must press the left button for an arrow shown on their right (Simon effect). Overall, the arrows could appear in 10 possible locations, giving rise to five types of conflicts with a unique blend of Stroop and Simon effects, with different levels of similarity.

The results showed that the degree to which conflicts could generalize to each other depended on their similarity: the more similar the conflicts, the easier it was to resolve one after having faced another. This is contrary to previous views suggesting that different conflict types either entirely generalized or could not generalize at all.

In addition, the analyses revealed that the neural networks involved in resolving each conflict type were organised in a continuous manner within a region called the prefrontal cortex. This pattern resembles how spatial information is arranged in the brain, prompting Yang et al. to suggest that cognitive control also falls under a set of principles known as cognitive space representations.

Overall, the methodology employed in this work could prove useful to researchers from other fields who also investigate whether various stimuli are processed via the same or different neural networks.

Cognitive control enables humans to behave purposefully by modulating neural processing to resolve conflicts between task-relevant and task-irrelevant information. For example, when naming the color of the word ‘BLUE’ printed in red ink, we are likely to be distracted by the word meaning, because reading a word is highly automatic in daily life. To keep our attention on the color, we need to mobilize the cognitive control processes to resolve the conflict between the color and word by boosting/suppressing the processing of color/word meaning. As task-relevant and task-irrelevant information can come from different sources, the sources of conflicts and how they should be resolved can vary greatly (Kornblum et al., 1990). For example, the conflict may occur between items of sensory information, such as between a red light and a police officer signaling cars to pass. Alternatively, conflict may occur between sensory and motor information, such as when a voice on the left asks you to turn right. A key unsolved question in cognitive control is how our brain efficiently resolves these different types of conflicts.

A first step to addressing this question is to examine the commonalities and/or dissociations across different types of conflicts that can be categorized into different domains. Examples of the domains of conflicts include experimental paradigm (Freitas et al., 2007; Magen and Cohen, 2007), sensory modality (Hazeltine et al., 2011; Yang et al., 2017), or conflict type regarding the dimensional overlap of conflict processes (Jiang and Egner, 2014; Liu et al., 2004).

Two solutions to resolving different conflict types are proposed. They differ based on whether the same cognitive control mechanisms are applied across domains. On the one hand, the domain-general cognitive control theories posit that the frontoparietal cortex adaptively encodes task information and can thus flexibly implement control strategies for different types of conflicts. This is supported by the generalizable control adjustment (i.e., encountering a conflict trial from one type can facilitate conflict resolution of another type) (Freitas et al., 2007; Kan et al., 2013) and similar neural patterns (Peterson et al., 2002; Wu et al., 2020) across distinct conflict tasks. A broader domain-general view holds that the frontoparietal brain regions/networks are widely involved in multiple control demands well beyond the conflict domain (Assem et al., 2020; Cole et al., 2013), which explains the remarkable flexibility in human behaviors. However, since domain-general processes are by definition likely shared by different tasks, when we need to handle multiple task demands at the same time, the efficiency of both tasks would be impaired due to resource competition or interference (Musslick and Cohen, 2021). Therefore, the domain-general processes are evolutionarily less advantageous for humans to deal with the diverse situations requiring high efficiency (Cosmides and Tooby, 1994). On the other hand, the domain-specific theories argue that different types of conflicts are handled by distinct cognitive control processes (e.g., where and how information processing should be modulated) (Egner, 2008; Kim et al., 2012). However, according to the domain-specific view, the diverse conflict situations require a multitude of preexisting control processes, which is biologically implausible (Abrahamse et al., 2016).

To reconcile the two theories, researchers recently proposed that cognitive control might be a mixture of domain-general and domain-specific processes. For instance, Freitas and Clark, 2015 found that trial-by-trial adjustment of control can generalize across two conflict domains to different degrees, leading to domain-general (strong generalization) or domain-specific (weak or no generalization) conclusions depending on the task settings of the consecutive conflicts. Similarly, different brain networks may show domain-generality (i.e., representing multiple conflicts) or domain-specificity (i.e., representing individual conflicts separately) (Jiang and Egner, 2014; Li et al., 2017). Even within the same brain area (e.g., medial frontal cortex), Fu et al., 2022 found that the neural population activity can be factorized into orthogonal dimensions encoding both domain-general and domain-specific conflict information, which can be selectively read out by downstream brain regions. While the mixture view provides an explanation for the contradictory findings (Braem et al., 2014), it suffers the same criticism as domain-specific cognitive control theories as it still requires many cognitive control processes to fully cover all possible conflicts.

A key to reconciling domain-general and domain-specific cognitive control is to organize the large number of conflict types using a system with limited, dissociable dimensions. A construct with a similar function is the cognitive space (Bellmund et al., 2018), which extends the idea of cognitive map (Behrens et al., 2018) to the representation of abstract information. Critically, the cognitive space view holds that the representations of different abstract information are organized continuously and the representational geometry in the cognitive space is determined by the similarity among the represented information (Bellmund et al., 2018).

In the human brain, it has been shown that abstract (Behrens et al., 2018; Schuck et al., 2016) and social (Park et al., 2020) information can be represented in a cognitive space. For example, social hierarchies with two independent scores (e.g., popularity and competence) can be represented in a 2D cognitive space (one dimension for each score), such that each social item can be located by its score in the two dimensions (Park et al., 2020). In the field of cognitive control, recent studies have begun to conceptualize different control states within a cognitive space (Badre et al., 2021). For example, Fu et al., 2022 mapped different conflict conditions to locations in a low-/high-dimensional cognitive space to demonstrate the domain-general/domain-specific problems; Grahek et al., 2022 used a cognitive space model of cognitive control settings to explain behavioral changes in the speed-accuracy tradeoff. However, the cognitive spaces proposed in these studies were only applicable to a limited number of control states involved in their designs. Therefore, it remains unclear whether there is a cognitive space that can explain the large number of control states, similar to that of the spatial location (Bellmund et al., 2018) and non-spatial knowledge (Behrens et al., 2018). A challenge to answering this question lies in how to construct control states with continuous levels of similarity. Our recent work (Yang et al., 2021) showed that it is possible to manipulate continuous conflict similarity by using a mixture of two independent conflict types with varying ratios, which can be used to further examine the behavioral and neural evidence for the cognitive space view. It is also unclear how the cognitive space of cognitive control is encoded in the brain, although that of spatial locations and non-spatial abstract knowledge has been relatively well investigated in the medial temporal lobe, medial prefrontal, and orbitofrontal system (Behrens et al., 2018; Bellmund et al., 2018). Recent research has suggested that the abstract task structure could be encoded and implemented by the frontoparietal network (Vaidya and Badre, 2022; Vaidya et al., 2021), but whether a similar neural system encodes the cognitive space of cognitive control remains untested.

The present study aimed to test the geometry of cognitive space in conflict representation. Specifically, we hypothesize that different types of conflicts are represented as points in a cognitive space. Importantly, the distance between the points, which reflects the geometry of the cognitive space, scales with the difference in the sources of the conflicts being represented by the points. The dimensions in the cognitive space of conflicts can be the aforementioned domains, in which domain-specific cognitive control processes are defined. For a specific type of conflict, its location in the cognitive space can be parameterized using a limited number of coordinates, which reflect how much control is needed for each of the domain-specific cognitive control processes. The cognitive space can also represent different types of conflicts with low dimensionality (Badre et al., 2021; MacDowell et al., 2022). Different domains can be represented conjunctively in a single cognitive space to achieve domain-general cognitive control as conflicts from different sources can be resolved using the same set of cognitive control processes. We further hypothesize that the cognitive space representing different types of conflicts may be located in the frontoparietal network due to its essential roles in conflict resolution (Freund et al., 2021a; Fu et al., 2022) and abstract task representation (Vaidya and Badre, 2022).

In this study, we adjusted the paradigm from our previous study (Yang et al., 2021) by including transitions of trials from five different conflict types, which enabled us to test whether these conflict types are organized in a cognitive space (Figure 1A). Specifically, on each trial, an arrow, pointing either upward or downward, was presented on one of the 10 possible locations on the screen. Participants were required to respond to the pointing direction of the arrow (up or down) by pressing either the left or right key. Importantly, conflicts from two sources can occur in this task. On one hand, the vertical location of the arrow can be incongruent with the direction (e.g., an up-pointing arrow on the lower half of the screen), resulting spatial Stroop conflict (Liu et al., 2004; Lu and Proctor, 1995). On the other hand, the horizontal location of the arrow can be incongruent with the response key (e.g., an arrow requiring left response presented on the right side of the screen), thus causing Simon conflict (Lu and Proctor, 1995; Simon and Small, 1969). As the arrow location rotates from the horizontal axis to the vertical axis, spatial Stroop conflict increases, and Simon conflict decreases. Therefore, the 10 possible locations of the arrow give rise to five conflict types with unique blend of spatial Stroop and Simon conflicts (Yang et al., 2021). As the increase in spatial Stroop conflict is highly correlated with the decrease in Simon conflict, we can use a 1D cognitive space to represent all five conflict types.

Figure 1

Download asset Open asset

One way to parameterize (i.e., defining a coordinate system) the cognitive space is to encode each conflict type by the angle of the axis connecting its two possible stimulus locations (Figure 1B). Within this cognitive space, the similarity between two conflict types can be quantified as the cosine value of their angular difference (Figure 1C). The rationale behind defining conflict similarity based on combinations of different conflict sources, such as spatial-Stroop and Simon, stems from the evidence that these sources undergo independent processing (Egner, 2008; Li et al., 2014; Liu et al., 2010; Wang et al., 2014). Identifying these distinct sources is critical in efficiently resolving diverse conflicts. If the conflict types are organized as a cognitive space in the brain, the similarity between conflict types in the cognitive space should be reflected in both the behavior and similarity in the neural representations of conflict types. Our data from two experiments using this experimental design support both predictions: using behavioral data, we found that the influence of congruency (i.e., whether the task-relevant and task-irrelevant information indicate the same response) from the previous trial to the next trial increases with the conflict similarity between the two trials. Using fMRI data, we found that more similar conflicts showed higher multivariate pattern similarity in the right dorsolateral prefrontal cortex (dlPFC).

Understanding how different types of conflicts are resolved is essential to answer how cognitive control achieves adaptive behavior. However, the dichotomy between domain-general and/or domain-specific processes presents a dilemma (Braem et al., 2014; Egner, 2008). Reconciliation of the two views also suffers from the inability to fully address how different conflicts can be resolved by a limited set of cognitive control processes. In this study, we hypothesized that this issue can be addressed if conflicts are organized as a cognitive space. Leveraging the well-known dissociation between the spatial Stroop and Simon conflicts (Li et al., 2014; Liu et al., 2010; Wang et al., 2014), we designed five conflict types that are systematically different from each other. The cognitive space hypothesis predicted that the representational proximity/distance between two conflict types scales with their similarities/dissimilarities, which was tested at both behavioral and neural levels. Behaviorally, we found that the CSEs were linearly modulated by conflict similarity between consecutive trials, replicating and extending our previous study (Yang et al., 2021). BOLD activity patterns in the right dlPFC further showed that the representational similarity between conflict types was modulated by their conflict similarity, and that strength of the modulation was positively associated with the modulation of conflict similarity on the behavioral CSE. We also observed that activity in two brain regions (right IPS and right dlPFC) was parametrically modulated by the conflict type difference, though they did not directly explain the behavioral results. Additionally, we found that the visual regions encoded the spatial orientation of the stimulus location, which might provide the essential concrete information to determine the conflict type. Together, these results suggest that conflicts may be organized in a cognitive space that enables a limited set of cognitive control processes to resolve a wide variety of distinct types of conflicts.

Conventionally, the domain-general view of control suggests a common representation for different types of conflicts (Figure 9, left), while the domain-specific view suggests dissociated representations for different types (Figure 9, right). Previous research on this topic often adopts a binary manipulation of conflicts (Braem et al., 2014) (i.e., each domain only has one conflict type) and gathered evidence for the domain-general/-specific view with the presence/absence of CSE, respectively. Here, we parametrically manipulated the similarity of conflict types and found the CSE systematically vary with conflict similarity level, demonstrating that cognitive control is neither purely domain-general nor purely domain-specific, but can be reconciled as a cognitive space (Bellmund et al., 2018; Figure 9, middle). The model comparison analysis also showed that the cognitive-space model explained the representation in right DLPFC better than the domain-general or domain-specific models. Specifically, the cognitive space provides a solution to use a single cognitive space organization to encode different types of conflicts that are close (domain-general) or distant (domain-specific) to each other. It also shows the potential for how various conflict types can be coded using limited resources (i.e., as different points in a low-dimensional cognitive space), as suggested by its out-of-sample predictability. Moreover, geometry can also emerge in the cognitive space (Fu et al., 2022), which will allow for decomposition of a conflict type (e.g., how much conflict in each of the dimensions in the cognitive space) so that it can be mapped into the limited set of cognitive control processes. Such geometry enables fast learning of cognitive control settings from similar conflict types by providing a measure of similarity (e.g., as distance in space).

Figure 9

Download asset Open asset

If the dimensionality of the cognitive space of conflict is extremely high, the cognitive space solution would suffer the same criticism as the domain-specificity theory. We argue that the dimensionality is manageable for the human brain as task information unrelated to differentiating conflicts can be removed. For example, the Simon conflict can be represented in a space consisting of spatial location, stimulus information, and responses. Thus, the dimensionality of the cognitive space of conflicts should not exceed the number of represented features. The dimensionality can be further reduced as humans selectively represent a small number of features when learning task representations (e.g., spatial information is reduced to the horizontal dimension from the 3D space we live in) (Niv, 2019). Consistently, we observed a low dimensional (1.19D) space representing the five conflict types. This is expected since the only manipulated variable is the angular distance between conflict types. The reduced dimensionality does not only require less effort to represent the conflict, but also facilitates generalization of cognitive control settings among different conflict types (Badre et al., 2021).

Although our finding of cognitive space in the right dlPFC differs from other cognitive space studies (Constantinescu et al., 2016; Park et al., 2020; Schuck et al., 2016) that highlighted the orbitofrontal and hippocampus regions, it is consistent with the cognitive control literature. The prefrontal cortex has long been believed to be a key region of cognitive control representation (Mansouri et al., 2007; Miller and Cohen, 2001; Milner, 1963) and is widely engaged in multiple task demands (Cole et al., 2013; Duncan, 2010). However, it is not until recently that the multivariate representation in this region has been examined. For instance, Vaidya et al., 2021 reported that frontal regions represented latent states that are organized hierarchically. Freund et al., 2021a showed that dlPFC encoded the target and congruency in a typical color-word Stroop task. Taken together, we suggest that the right dlPFC might flexibly encode a variety of cognitive spaces to meet the dynamic task demands. In addition, we found no such representation in the left dlPFC, indicating a possible lateralization. Previous studies showed that the left dlPFC was related to the expectancy-related attentional set upregulation, while the right dlPFC was related to the online adjustment of control (Friehs et al., 2020; Vanderhasselt et al., 2009), which is consistent with our findings. Moreover, the right PFC also represents a composition of single rules (Reverberi et al., 2012), which may explain how the spatial Stroop and Simon types can be jointly encoded in a single space.

Previous researchers have proposed an ‘expected value of control (EVC)’ theory, which posits that the brain can evaluate the cost and benefit associated with executing control for a demanding task, such as the conflict task, and specify the optimal control strength (Shenhav et al., 2013). For instance, Grahek et al., 2022 found that more frequently switching goals when doing a Stroop task were achieved by adjusting smaller control intensity. Our work complements the EVC theory by further investigating the neural representation of different conflict conditions and how these representations can be evaluated to facilitate conflict resolution. We found that different conflict conditions could be efficiently represented in a cognitive space encoded by the right dlPFC, and participants with stronger cognitive space representation have also adjusted their conflict control to a greater extent based on the conflict similarity (Figure 6C). The finding suggests that the cognitive space organization of conflicts guides cognitive control to adjust behavior. Previous studies have shown that participants may adopt different strategies to represent a task, with the model-based strategies benefitting goal-related behaviors more than the model-free strategies (Rmus et al., 2022). Similarly, we propose that cognitive space could serve as a mental model to assist fast learning and efficient organization of cognitive control settings. Specifically, the cognitive space representation may provide a principle for how our brain evaluates the expected cost of switching and the benefit of generalization between states and selects the path with the best cost–benefit tradeoff (Abrahamse et al., 2016; Shenhav et al., 2013). The proximity between two states in cognitive space could reflect both the expected cognitive demand required to transition and the useful mechanisms to adapt from. The closer the two conditions are in cognitive space, the lower the expected switching cost and the higher the generalizability when transitioning between them. With the organization of a cognitive space, a new conflict can be quickly assigned a location in the cognitive space, which will facilitate the development of cognitive control settings for this conflict by interpolating nearby conflicts and/or projecting the location to axes representing different cognitive control processes, thus leading to a stronger CSE when following a more similar conflict condition. On the other hand, without a cognitive space, there would be no measure of similarity between conflicts on different trials, hence limiting the ability of fast learning of cognitive control setting from similar trials.

The cognitive space in the right dlPFC appears to be an abstraction of concrete information from the visual regions. We found that the right V1 and V2 encoded the spatial orientation of the target location (Figure 7) and showed strong representational connectivity with the right dlPFC (Figure 8), suggesting that there might be information exchange between these regions. We speculate that the representation of spatial orientation may have provided the essential perceptual information to determine the conflict type (Figure 1) and thus served as the critical input for the cognitive space. The conflict-type representation further incorporates the stimulus–response mapping rules to the spatial orientation representation, so that vertically symmetric orientations can be recognized as the same conflict type (Figure 4). In other words, the representation of conflict type involves the compression of perceptual information (Flesch et al., 2022), which is consistent with the idea of a low-dimensional representation of cognitive control (Badre et al., 2021; MacDowell et al., 2022). The compression and abstraction processes might be why the frontoparietal regions are the top of hierarchy of information processing (Gilbert and Li, 2013) and why the frontoparietal regions are widely engaged in multiple task demands (Duncan, 2013).

Although the spatial orientation information in our design could be helpful to the construction of cognitive space, the cognitive space itself was independent of the stimulus-level representation of the task. We found the conflict similarity modulation on CSE did not change with more training, indicating that the cognitive space did not depend on strategies that could be learned through training. Instead, the cognitive space should be determined by the intrinsic similarity structure of the task design. For example, a previous study (Freitas and Clark, 2015) has found that the CSE across different versions of spatial Stroop and flanker tasks was stronger than that across either of the two conflicts and Simon. In their designs, the stimulus similarity was controlled at the same level, so the difference in CSE was only attributable to the similar dimensional overlap between Stroop and flanker tasks, in contrast to the Simon task. Furthermore, recent studies showed that the cognitive space generally exists to represent structured latent states (e.g., Vaidya and Badre, 2022), mental strategy cost (Grahek et al., 2022), and social hierarchies (Park et al., 2020). Therefore, cognitive space is likely a universal strategy that can be applied to different scenarios.

With conventional univariate analyses, we observed that the overall congruency effect was located at the medial frontal region (i.e., pre-SMA), which is consistent with previous studies (Botvinick et al., 2004; Fu et al., 2022). Beyond that, we also found regions that can be parametrically modulated by conflict-type difference, including right IPS and right dlPFC (modulated by Simon difference). The right lateralization of these regions is consistent with a previous finding (Li et al., 2017). The parametric encoding of conflict also mirrors prior research showing the parametric encoding of task demands (Dagher et al., 1999; Ritz and Shenhav, 2023). The scaling of brain activities based on conflict difference is potentially important to the representational organization of different types of conflicts. However, we did not observe their brain–behavioral relevance. One possible reason is that the conflict (dis)similarity is a combination of (dis)similarity of spatial Stroop and Simon conflicts, but each univariate region only reflects difference along a single conflict domain. Also likely, the representational geometry is more of a multivariate problem than what univariate activities can capture (Freund et al., 2021b). Future studies may adopt approaches such as repetition suppression-induced fMRI adaptation (Badre et al., 2021) to test the role of univariate activities in task representations.

Recently an interesting debate has arisen concerning whether cognitive control should be considered as a process or a representation (Freund et al., 2021b). Traditionally, cognitive control has been predominantly viewed as a process. However, the study of its representation has gained more and more attention. While it may not be as straightforward as the visual representation (e.g., creating a mental image from a real image in the visual area), cognitive control can have its own form of representation. An influential theory, Marr, 1982 three-level model proposed that representation serves as the algorithm of the process to achieve a goal based on the input. In other words, representation can encompass a dynamic process rather than being limited to static stimuli. Building on this perspective, we posit that the representation of cognitive control consists of an array of dynamic representations embedded within the overall process. A similar idea has been proposed that the representation of task profiles can be progressively constructed with time in the brain (Kikumoto and Mayr, 2020). Moreover, we anticipate that the representation of cognitive space is most prominently involved at two critical stages to guide the transference of behavioral CSE. The first stage involves the evaluation of control demands, where the representational distance/similarity between previous and current trials influences the adjustment of cognitive control. The second stage pertains to control execution, where the switch from one control state to another follows a path within the cognitive space. However, we were unable to fully distinguish between these two stages due to the low temporal resolution of fMRI signals in our study. Future research seeking to delve deeper into this question may benefit from methodologies with higher temporal resolutions, such as EEG and MEG.

Several interesting questions remains to be answered. For example, is the dimension of the unified space across conflict-inducing tasks solely determined by the number of conflict sources? Is this unified space adaptively adjusted within the same brain region? Can we effectively map any sources of conflict with completely different stimuli into a single space? Does the cognitive space vary from population to population, such as between the normal people and patients?

All data needed to evaluate the conclusions are present in the paper. Raw MRI data (in BIDS format), behavioral data and analysis codes can be accessed at https://osf.io/4b3wd/.

1. 1. Yang G
   2. Wu H
   3. Li Q
   4. Liu X
   5. Fu Z
   6. Jiang J

   (2022) Open Science Framework

   Conflicts are represented in a cognitive space to reconcile domain-general and domain-specific cognitive control.

   https://doi.org/10.17605/OSF.IO/4B3WD

1. 1. Abraham A
   2. Pedregosa F
   3. Eickenberg M
   4. Gervais P
   5. Mueller A
   6. Kossaifi J
   7. Gramfort A
   8. Thirion B
   9. Varoquaux G

   (2014) Machine learning for neuroimaging with scikit-learn

   Frontiers in Neuroinformatics 8:14.

   https://doi.org/10.3389/fninf.2014.00014

   • PubMed
   • Google Scholar
2. 1. Abrahamse E
   2. Braem S
   3. Notebaert W
   4. Verguts T

   (2016) Grounding cognitive control in associative learning

   Psychological Bulletin 142:693–728.

   https://doi.org/10.1037/bul0000047

   • PubMed
   • Google Scholar
3. 1. Akçay Ç
   2. Hazeltine E

   (2011) Domain-specific conflict adaptation without feature repetitions

   Psychonomic Bulletin & Review 18:505–511.

   https://doi.org/10.3758/s13423-011-0084-y

   • PubMed
   • Google Scholar
4. 1. Assem M
   2. Glasser MF
   3. Van Essen DC
   4. Duncan J

   (2020) A Domain-general cognitive core defined in multimodally parcellated human cortex

   Cerebral Cortex 30:4361–4380.

   https://doi.org/10.1093/cercor/bhaa023

   • PubMed
   • Google Scholar
5. 1. Avants BB
   2. Epstein CL
   3. Grossman M
   4. Gee JC

   (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

   Medical Image Analysis 12:26–41.

   https://doi.org/10.1016/j.media.2007.06.004

   • PubMed
   • Google Scholar
6. 1. Badre D
   2. Bhandari A
   3. Keglovits H
   4. Kikumoto A

   (2021) The dimensionality of neural representations for control

   Current Opinion in Behavioral Sciences 38:20–28.

   https://doi.org/10.1016/j.cobeha.2020.07.002

   • PubMed
   • Google Scholar
7. 1. Behrens TEJ
   2. Muller TH
   3. Whittington JCR
   4. Mark S
   5. Baram AB
   6. Stachenfeld KL
   7. Kurth-Nelson Z

   (2018) What is a cognitive map

   Organizing Knowledge for Flexible Behavior. Neuron 100:490–509.

   https://doi.org/10.1016/j.neuron.2018.10.002

   • PubMed
   • Google Scholar
8. 1. Behzadi Y
   2. Restom K
   3. Liau J
   4. Liu TT

   (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

   NeuroImage 37:90–101.

   https://doi.org/10.1016/j.neuroimage.2007.04.042

   • PubMed
   • Google Scholar
9. 1. Bellmund JLS
   2. Gärdenfors P
   3. Moser EI
   4. Doeller CF

   (2018) Navigating cognition: spatial codes for human thinking

   Science 362:eaat6766.

   https://doi.org/10.1126/science.aat6766

   • PubMed
   • Google Scholar
10. 1. Benjamini Y
    2. Hochberg Y

    (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

    Journal of the Royal Statistical Society 57:289–300.

    https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    • Google Scholar
11. 1. Blake KR
    2. Gangestad S

    (2020) On Attenuated interactions, measurement error, and statistical power: guidelines for social and personality psychologists

    Personality & Social Psychology Bulletin 46:1702–1711.

    https://doi.org/10.1177/0146167220913363

    • PubMed
    • Google Scholar
12. 1. Botvinick MM
    2. Cohen JD
    3. Carter CS

    (2004) Conflict monitoring and anterior cingulate cortex: an update

    Trends in Cognitive Sciences 8:539–546.

    https://doi.org/10.1016/j.tics.2004.10.003

    • Google Scholar
13. 1. Braem S
    2. Abrahamse EL
    3. Duthoo W
    4. Notebaert W

    (2014) What determines the specificity of conflict adaptation? A review, critical analysis, and proposed synthesis

    Frontiers in Psychology 5:1134.

    https://doi.org/10.3389/fpsyg.2014.01134

    • PubMed
    • Google Scholar
14. 1. Braem S
    2. Bugg JM
    3. Schmidt JR
    4. Crump MJC
    5. Weissman DH
    6. Notebaert W
    7. Egner T

    (2019) Measuring adaptive control in conflict tasks

    Trends in Cognitive Sciences 23:769–783.

    https://doi.org/10.1016/j.tics.2019.07.002

    • Google Scholar
15. 1. Chen J
    2. Leong YC
    3. Honey CJ
    4. Yong CH
    5. Norman KA
    6. Hasson U

    (2017a) Shared memories reveal shared structure in neural activity across individuals

    Nature Neuroscience 20:115–125.

    https://doi.org/10.1038/nn.4450

    • Google Scholar
16. 1. Chen G
    2. Taylor PA
    3. Shin YW
    4. Reynolds RC
    5. Cox RWJN

    (2017b) Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling

    NeuroImage 147:825–840.

    https://doi.org/10.1016/j.neuroimage.2016.08.029

    • Google Scholar
17. 1. Cole MW
    2. Reynolds JR
    3. Power JD
    4. Repovs G
    5. Anticevic A
    6. Braver TS

    (2013) Multi-task connectivity reveals flexible hubs for adaptive task control

    Nature Neuroscience 16:1348–1355.

    https://doi.org/10.1038/nn.3470

    • PubMed
    • Google Scholar
18. 1. Constantinescu AO
    2. O’Reilly JX
    3. Behrens TEJ

    (2016) Organizing conceptual knowledge in humans with a gridlike code

    Science 352:1464–1468.

    https://doi.org/10.1126/science.aaf0941

    • Google Scholar
19. Book

    1. Cosmides L
    2. Tooby J

    (1994) Origins of domain specificity: the evolution of functional organization

    In: Hirschfeld LA, Gelman SA, editors. Mapping the Mind: Domain Specificity in Cognition and Culture. Cambridge: Cambridge University Press. pp. 85–116.

    https://doi.org/10.1017/CBO9780511752902

    • Google Scholar
20. 1. Cox RW
    2. Hyde JS

    (1997) Software tools for analysis and visualization of fMRI data

    NMR in Biomedicine 10:171–178.

    https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L

    • Google Scholar
21. 1. Dagher A
    2. Owen AM
    3. Boecker H
    4. Brooks DJ

    (1999) Mapping the network for planning: a correlational PET activation study with the Tower of London task

    Brain 122 (Pt 10):1973–1987.

    https://doi.org/10.1093/brain/122.10.1973

    • PubMed
    • Google Scholar
22. 1. Duncan J

    (2010) The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour

    Trends in Cognitive Sciences 14:172–179.

    https://doi.org/10.1016/j.tics.2010.01.004

    • Google Scholar
23. 1. Duncan J

    (2013) The structure of cognition: attentional episodes in mind and brain

    Neuron 80:35–50.

    https://doi.org/10.1016/j.neuron.2013.09.015

    • PubMed
    • Google Scholar
24. Preprint

    1. Durnez J
    2. Blair R
    3. Poldrack RA

    (2018) Neurodesign: optimal experimental designs for task fMRI

    bioRxiv.

    https://doi.org/10.1101/119594

    • Google Scholar
25. 1. Egner T

    (2007) Congruency sequence effects and cognitive control

    Cognitive, Affective, & Behavioral Neuroscience 7:380–390.

    https://doi.org/10.3758/CABN.7.4.380

    • Google Scholar
26. 1. Egner T
    2. Delano M
    3. Hirsch J

    (2007) Separate conflict-specific cognitive control mechanisms in the human brain

    NeuroImage 35:940–948.

    https://doi.org/10.1016/j.neuroimage.2006.11.061

    • PubMed
    • Google Scholar
27. 1. Egner T

    (2008) Multiple conflict-driven control mechanisms in the human brain

    Trends in Cognitive Sciences 12:374–380.

    https://doi.org/10.1016/j.tics.2008.07.001

    • Google Scholar
28. 1. Esteban O
    2. Markiewicz CJ
    3. Blair RW
    4. Moodie CA
    5. Isik AI
    6. Erramuzpe A
    7. Kent JD
    8. Goncalves M
    9. DuPre E
    10. Snyder M
    11. Oya H
    12. Ghosh SS
    13. Wright J
    14. Durnez J
    15. Poldrack RA
    16. Gorgolewski KJ

    (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI

    Nature Methods 16:111–116.

    https://doi.org/10.1038/s41592-018-0235-4

    • PubMed
    • Google Scholar
29. 1. Flesch T
    2. Juechems K
    3. Dumbalska T
    4. Saxe A
    5. Summerfield C

    (2022) Orthogonal representations for robust context-dependent task performance in brains and neural networks

    Neuron 110:1258–1270.

    https://doi.org/10.1016/j.neuron.2022.01.005

    • PubMed
    • Google Scholar
30. 1. Fonov VS
    2. Evans AC
    3. McKinstry RC
    4. Almli CR
    5. Collins DL

    (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

    NeuroImage 47:S102.

    https://doi.org/10.1016/S1053-8119(09)70884-5

    • Google Scholar
31. 1. Freitas AL
    2. Bahar M
    3. Yang S
    4. Banai R

    (2007) Contextual adjustments in cognitive control across tasks

    Psychological Science 18:1040–1043.

    https://doi.org/10.1111/j.1467-9280.2007.02022.x

    • PubMed
    • Google Scholar
32. 1. Freitas AL
    2. Clark SL

    (2015) Generality and specificity in cognitive control: conflict adaptation within and across selective-attention tasks but not across selective-attention and Simon tasks

    Psychological Research 79:143–162.

    https://doi.org/10.1007/s00426-014-0540-1

    • PubMed
    • Google Scholar
33. 1. Freund MC
    2. Bugg JM
    3. Braver TS

    (2021a) A representational similarity analysis of cognitive control during color-word stroop

    The Journal of Neuroscience 41:7388–7402.

    https://doi.org/10.1523/JNEUROSCI.2956-20.2021

    • PubMed
    • Google Scholar
34. 1. Freund MC
    2. Etzel JA
    3. Braver TS

    (2021b) Neural coding of cognitive control: the representational similarity analysis approach

    Trends in Cognitive Sciences 25:622–638.

    https://doi.org/10.1016/j.tics.2021.03.011

    • Google Scholar
35. 1. Friehs MA
    2. Klaus J
    3. Singh T
    4. Frings C
    5. Hartwigsen G

    (2020) Perturbation of the right prefrontal cortex disrupts interference control

    NeuroImage 222:117279.

    https://doi.org/10.1016/j.neuroimage.2020.117279

    • PubMed
    • Google Scholar
36. 1. Fu Z
    2. Beam D
    3. Chung JM
    4. Reed CM
    5. Mamelak AN
    6. Adolphs R
    7. Rutishauser U

    (2022) The geometry of domain-general performance monitoring in the human medial frontal cortex

    Science 376:9922.

    https://doi.org/10.1126/science.abm9922

    • Google Scholar
37. 1. Gilbert CD
    2. Li W

    (2013) Top-down influences on visual processing

    Nature Reviews. Neuroscience 14:350–363.

    https://doi.org/10.1038/nrn3476

    • PubMed
    • Google Scholar
38. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
39. 1. Gorgolewski K
    2. Burns CD
    3. Madison C
    4. Clark D
    5. Halchenko YO
    6. Waskom ML
    7. Ghosh SS

    (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python

    Frontiers in Neuroinformatics 5:13.

    https://doi.org/10.3389/fninf.2011.00013

    • PubMed
    • Google Scholar
40. Book

    1. Grahek I
    2. Leng X
    3. Fahey MP
    4. Yee D
    5. Shenhav A

    (2022)

    Empirical and computational evidence for Reconfiguration costs during within-task adjustments in cognitive control

    In: Grahek I, Leng X, Musslick S, Shenhav A, editors. Proceedings of the Annual Meeting of the Cognitive Science Society. Merced: University of California Press. pp. 1–9.

    • Google Scholar
41. 1. Greve DN
    2. Fischl B

    (2009) Accurate and robust brain image alignment using boundary-based registration

    NeuroImage 48:63–72.

    https://doi.org/10.1016/j.neuroimage.2009.06.060

    • Google Scholar
42. 1. Hazeltine E
    2. Lightman E
    3. Schwarb H
    4. Schumacher EH

    (2011) The boundaries of sequential modulations: evidence for set-level control

    Journal of Experimental Psychology. Human Perception and Performance 37:1898–1914.

    https://doi.org/10.1037/a0024662

    • PubMed
    • Google Scholar
43. 1. Hommel B
    2. Proctor RW
    3. Vu K-PL

    (2004) A feature-integration account of sequential effects in the Simon task

    Psychological Research 68:1–17.

    https://doi.org/10.1007/s00426-003-0132-y

    • Google Scholar
44. 1. Ito T
    2. Murray JD

    (2023) Multitask representations in the human cortex transform along a sensory-to-motor hierarchy

    Nature Neuroscience 26:306–315.

    https://doi.org/10.1038/s41593-022-01224-0

    • PubMed
    • Google Scholar
45. 1. Jenkinson M
    2. Smith S

    (2001) A global optimisation method for robust affine registration of brain images

    Medical Image Analysis 5:143–156.

    https://doi.org/10.1016/s1361-8415(01)00036-6

    • PubMed
    • Google Scholar
46. 1. Jenkinson M
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images

    NeuroImage 17:825–841.

    https://doi.org/10.1006/nimg.2002.1132

    • Google Scholar
47. 1. Jiang J
    2. Egner T

    (2014) Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain

    Cerebral Cortex 24:1793–1805.

    https://doi.org/10.1093/cercor/bht029

    • PubMed
    • Google Scholar
48. 1. Jiang J
    2. Summerfield C
    3. Egner T

    (2016) Visual prediction error spreads across object features in human visual cortex

    The Journal of Neuroscience 36:12746–12763.

    https://doi.org/10.1523/JNEUROSCI.1546-16.2016

    • PubMed
    • Google Scholar
49. 1. Jiang J
    2. Wang SF
    3. Guo W
    4. Fernandez C
    5. Wagner AD

    (2020) Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns

    Nature Communications 11:2053.

    https://doi.org/10.1038/s41467-020-15928-z

    • PubMed
    • Google Scholar
50. 1. Kan IP
    2. Teubner-Rhodes S
    3. Drummey AB
    4. Nutile L
    5. Krupa L
    6. Novick JM

    (2013) To adapt or not to adapt: the question of domain-general cognitive control

    Cognition 129:637–651.

    https://doi.org/10.1016/j.cognition.2013.09.001

    • PubMed
    • Google Scholar
51. 1. Kikumoto A
    2. Mayr U

    (2020) Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection

    PNAS 117:10603–10608.

    https://doi.org/10.1073/pnas.1922166117

    • PubMed
    • Google Scholar
52. 1. Kim C
    2. Chung C
    3. Kim J

    (2012) Conflict adjustment through domain-specific multiple cognitive control mechanisms

    Brain Research 1444:55–64.

    https://doi.org/10.1016/j.brainres.2012.01.023

    • Google Scholar
53. 1. Kornblum S
    2. Hasbroucq T
    3. Osman A

    (1990) Dimensional overlap: cognitive basis for stimulus-response compatibility--a model and taxonomy

    Psychological Review 97:253–270.

    https://doi.org/10.1037/0033-295x.97.2.253

    • PubMed
    • Google Scholar
54. 1. Kriegeskorte N
    2. Mur M
    3. Bandettini P

    (2008) Representational similarity analysis - connecting the branches of systems neuroscience

    Frontiers in Systems Neuroscience 2:4.

    https://doi.org/10.3389/neuro.06.004.2008

    • PubMed
    • Google Scholar
55. 1. Lanczos C

    (1964) Evaluation of noisy data

    Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis 1:76–85.

    https://doi.org/10.1137/0701007

    • Google Scholar
56. 1. Li Q
    2. Nan W
    3. Wang K
    4. Liu X

    (2014) Independent processing of stimulus-stimulus and stimulus-response conflicts

    PLOS ONE 9:e89249.

    https://doi.org/10.1371/journal.pone.0089249

    • Google Scholar
57. 1. Li Q
    2. Yang G
    3. Li Z
    4. Qi Y
    5. Cole MW
    6. Liu X

    (2017) Conflict detection and resolution rely on a combination of common and distinct cognitive control networks

    Neuroscience and Biobehavioral Reviews 83:123–131.

    https://doi.org/10.1016/j.neubiorev.2017.09.032

    • PubMed
    • Google Scholar
58. 1. Liu X
    2. Banich MT
    3. Jacobson BL
    4. Tanabe JL

    (2004) Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI

    NeuroImage 22:1097–1106.

    https://doi.org/10.1016/j.neuroimage.2004.02.033

    • PubMed
    • Google Scholar
59. 1. Liu X
    2. Park Y
    3. Gu X
    4. Fan J

    (2010) Dimensional overlap accounts for independence and integration of stimulus—response compatibility effects

    Attention, Perception, & Psychophysics 72:1710–1720.

    https://doi.org/10.3758/APP.72.6.1710

    • Google Scholar
60. 1. Lu C
    2. Proctor RW

    (1995) The influence of irrelevant location information on performance: a review of the Simon and spatial Stroop effects

    Psychonomic Bulletin & Review 2:174–207.

    https://doi.org/10.3758/BF03210959

    • Google Scholar
61. 1. MacDowell CJ
    2. Tafazoli S
    3. Buschman TJ

    (2022) A Goldilocks theory of cognitive control: Balancing precision and efficiency with low-dimensional control states

    Current Opinion in Neurobiology 76:102606.

    https://doi.org/10.1016/j.conb.2022.102606

    • PubMed
    • Google Scholar
62. 1. MacLeod CM

    (1991) Half a century of research on the Stroop effect: an integrative review

    Psychological Bulletin 109:163–203.

    https://doi.org/10.1037/0033-2909.109.2.163

    • PubMed
    • Google Scholar
63. 1. Magen H
    2. Cohen A

    (2007) Modularity beyond perception: evidence from single task interference paradigms

    Cognitive Psychology 55:1–36.

    https://doi.org/10.1016/j.cogpsych.2006.09.003

    • Google Scholar
64. 1. Mansouri FA
    2. Buckley MJ
    3. Tanaka K

    (2007) Mnemonic function of the dorsolateral prefrontal cortex in conflict-induced behavioral adjustment

    Science 318:987–990.

    https://doi.org/10.1126/science.1146384

    • PubMed
    • Google Scholar
65. Book

    1. Marr D

    (1982)

    Vision: A Computational Investigation into the Human Representation and Processing of Visual Information

    Henry Holt and Co., Inc.

    • Google Scholar
66. 1. Miller EK
    2. Cohen JD

    (2001) An integrative theory of prefrontal cortex function

    Annual Review of Neuroscience 24:167–202.

    https://doi.org/10.1146/annurev.neuro.24.1.167

    • PubMed
    • Google Scholar
67. 1. Milner B

    (1963) Effects of different brain lesions on card sorting - role of frontal lobes

    Archives of Neurology 9:90–100.

    https://doi.org/10.1001/archneur.1963.00460070100010

    • Google Scholar
68. 1. Musslick S
    2. Cohen JD

    (2021) Rationalizing constraints on the capacity for cognitive control

    Trends in Cognitive Sciences 25:757–775.

    https://doi.org/10.1016/j.tics.2021.06.001

    • PubMed
    • Google Scholar
69. 1. Nili H
    2. Wingfield C
    3. Walther A
    4. Su L
    5. Marslen-Wilson W
    6. Kriegeskorte N

    (2014) A toolbox for representational similarity analysis

    PLOS Computational Biology 10:e1003553.

    https://doi.org/10.1371/journal.pcbi.1003553

    • PubMed
    • Google Scholar
70. 1. Niv Y

    (2019) Learning task-state representations

    Nature Neuroscience 22:1544–1553.

    https://doi.org/10.1038/s41593-019-0470-8

    • PubMed
    • Google Scholar
71. 1. Park SA
    2. Miller DS
    3. Nili H
    4. Ranganath C
    5. Boorman ED

    (2020) Map making: constructing, combining, and inferring on abstract cognitive maps

    Neuron 107:1226–1238.

    https://doi.org/10.1016/j.neuron.2020.06.030

    • Google Scholar
72. 1. Peterson BS
    2. Kane MJ
    3. Alexander GM
    4. Lacadie C
    5. Skudlarski P
    6. Leung H-C
    7. May J
    8. Gore JC

    (2002) An event-related functional MRI study comparing interference effects in the Simon and Stroop tasks

    Cognitive Brain Research 13:427–440.

    https://doi.org/10.1016/S0926-6410(02)00054-X

    • Google Scholar
73. 1. Polk TA
    2. Drake RM
    3. Jonides JJ
    4. Smith MR
    5. Smith EE

    (2008) Attention enhances the neural processing of relevant features and suppresses the processing of irrelevant features in humans: a functional magnetic resonance imaging study of the Stroop task

    The Journal of Neuroscience 28:13786–13792.

    https://doi.org/10.1523/JNEUROSCI.1026-08.2008

    • PubMed
    • Google Scholar
74. 1. Power JD
    2. Mitra A
    3. Laumann TO
    4. Snyder AZ
    5. Schlaggar BL
    6. Petersen SE

    (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI

    NeuroImage 84:320–341.

    https://doi.org/10.1016/j.neuroimage.2013.08.048

    • PubMed
    • Google Scholar
75. 1. Reverberi C
    2. Görgen K
    3. Haynes J-D

    (2012) Compositionality of rule representations in human prefrontal cortex

    Cerebral Cortex 22:1237–1246.

    https://doi.org/10.1093/cercor/bhr200

    • Google Scholar
76. Preprint

    1. Ritz H
    2. Shenhav A

    (2022) Humans reconfigure target and distractor processing to address distinct task demands

    bioRxiv.

    https://doi.org/10.1101/2021.09.08.459546

    • Google Scholar
77. Preprint

    1. Ritz H
    2. Shenhav A

    (2023) Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

    bioRxiv.

    https://doi.org/10.1101/2022.12.01.518771

    • Google Scholar
78. 1. Rmus M
    2. Ritz H
    3. Hunter LE
    4. Bornstein AM
    5. Shenhav A

    (2022) Humans can navigate complex graph structures acquired during latent learning

    Cognition 225:105103.

    https://doi.org/10.1016/j.cognition.2022.105103

    • Google Scholar
79. 1. Satterthwaite FE

    (1946)

    An approximate distribution of estimates of variance components

    Biometrics 2:110–114.

    • PubMed
    • Google Scholar
80. 1. Schmidt JR
    2. Weissman DH

    (2014) Congruency sequence effects without feature integration or contingency learning confounds

    PLOS ONE 9:e102337.

    https://doi.org/10.1371/journal.pone.0102337

    • Google Scholar
81. 1. Schuck NW
    2. Cai MB
    3. Wilson RC
    4. Niv Y

    (2016) Human orbitofrontal cortex represents a cognitive map of state space

    Neuron 91:1402–1412.

    https://doi.org/10.1016/j.neuron.2016.08.019

    • Google Scholar
82. 1. Shenhav A
    2. Botvinick MM
    3. Cohen JD

    (2013) The expected value of control: an integrative theory of anterior cingulate cortex function

    Neuron 79:217–240.

    https://doi.org/10.1016/j.neuron.2013.07.007

    • PubMed
    • Google Scholar
83. 1. Simon JR
    2. Small AM

    (1969) Processing auditory information: interference from an irrelevant cue

    The Journal of Applied Psychology 53:433–435.

    https://doi.org/10.1037/h0028034

    • PubMed
    • Google Scholar
84. 1. Torres-Quesada M
    2. Funes MJ
    3. Lupiáñez J

    (2013) Dissociating proportion congruent and conflict adaptation effects in a Simon-Stroop procedure

    Acta Psychologica 142:203–210.

    https://doi.org/10.1016/j.actpsy.2012.11.015

    • PubMed
    • Google Scholar
85. 1. Tusche A
    2. Böckler A
    3. Kanske P
    4. Trautwein F-M
    5. Singer T

    (2016) Decoding the charitable brain: empathy, perspective taking, and attention shifts differentially predict altruistic giving

    The Journal of Neuroscience 36:4719–4732.

    https://doi.org/10.1523/JNEUROSCI.3392-15.2016

    • PubMed
    • Google Scholar
86. 1. Tustison NJ
    2. Avants BB
    3. Cook PA
    4. Zheng Y
    5. Egan A
    6. Yushkevich PA
    7. Gee JC

    (2010) N4ITK: improved N3 bias correction

    IEEE Transactions on Medical Imaging 29:1310–1320.

    https://doi.org/10.1109/TMI.2010.2046908

    • PubMed
    • Google Scholar
87. 1. Vaidya AR
    2. Jones HM
    3. Castillo J
    4. Badre D

    (2021) Neural representation of abstract task structure during generalization

    eLife 10:e63226.

    https://doi.org/10.7554/eLife.63226

    • PubMed
    • Google Scholar
88. 1. Vaidya AR
    2. Badre D

    (2022) Abstract task representations for inference and control

    Trends in Cognitive Sciences 26:484–498.

    https://doi.org/10.1016/j.tics.2022.03.009

    • PubMed
    • Google Scholar
89. 1. Vanderhasselt M-A
    2. De Raedt R
    3. Baeken C

    (2009) Dorsolateral prefrontal cortex and stroop performance: tackling the lateralization

    Psychonomic Bulletin & Review 16:609–612.

    https://doi.org/10.3758/PBR.16.3.609

    • Google Scholar
90. 1. Wager TD
    2. Nichols TE

    (2003) Optimization of experimental design in fMRI: a general framework using a genetic algorithm

    NeuroImage 18:293–309.

    https://doi.org/10.1016/s1053-8119(02)00046-0

    • PubMed
    • Google Scholar
91. 1. Walther A
    2. Nili H
    3. Ejaz N
    4. Alink A
    5. Kriegeskorte N
    6. Diedrichsen J

    (2016) Reliability of dissimilarity measures for multi-voxel pattern analysis

    NeuroImage 137:188–200.

    https://doi.org/10.1016/j.neuroimage.2015.12.012

    • PubMed
    • Google Scholar
92. 1. Wang K
    2. Li Q
    3. Zheng Y
    4. Wang H
    5. Liu X

    (2014) Temporal and spectral profiles of stimulus-stimulus and stimulus-response conflict processing

    NeuroImage 89:280–288.

    https://doi.org/10.1016/j.neuroimage.2013.11.045

    • PubMed
    • Google Scholar
93. 1. Wu T
    2. Spagna A
    3. Chen C
    4. Schulz KP
    5. Hof PR
    6. Fan J

    (2020) Supramodal Mechanisms of the Cognitive control network in uncertainty processing

    Cerebral Cortex 30:6336–6349.

    https://doi.org/10.1093/cercor/bhaa189

    • PubMed
    • Google Scholar
94. 1. Yang G
    2. Nan W
    3. Zheng Y
    4. Wu H
    5. Li Q
    6. Liu X

    (2017) Distinct cognitive control mechanisms as revealed by modality-specific conflict adaptation effects

    Journal of Experimental Psychology. Human Perception and Performance 43:807–818.

    https://doi.org/10.1037/xhp0000351

    • PubMed
    • Google Scholar
95. 1. Yang G
    2. Xu H
    3. Li Z
    4. Nan W
    5. Wu H
    6. Li Q
    7. Liu X

    (2021) The congruency sequence effect is modulated by the similarity of conflicts

    Journal of Experimental Psychology. Learning, Memory, and Cognition 47:1705–1719.

    https://doi.org/10.1037/xlm0001054

    • PubMed
    • Google Scholar
96. 1. Zhang Y
    2. Brady M
    3. Smith S

    (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

    IEEE Transactions on Medical Imaging 20:45–57.

    https://doi.org/10.1109/42.906424

    • PubMed
    • Google Scholar

Author details

1. Guochun Yang

   1. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
   3. Department of Psychological and Brain Sciences, University of Iowa, Iowa City, United States
   4. Cognitive Control Collaborative, University of Iowa, Iowa City, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0516-8772

2. Haiyan Wu

   Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8869-6636

3. Qi Li

   Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

4. Xun Liu

   1. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   liux@psych.ac.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1366-8926

5. Zhongzheng Fu

   Department of Neurological Surgery, Unversity of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2572-6284

6. Jiefeng Jiang

   1. Department of Psychological and Brain Sciences, University of Iowa, Iowa City, United States
   2. Cognitive Control Collaborative, University of Iowa, Iowa City, United States

   Contribution

   Supervision, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

• 1,464

  views

• 196

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.