‘Cognitive control’ describes a collection of neurocognitive mechanisms that allow us to use internal goals and ongoing context to strategically bias the manner in which we process information (Miller and Cohen, 2001; Egner, 2017). For instance, depending on current goals, humans can flexibly switch or update ‘task-sets’ that allow them to shift between different aspects of a stimulus to which they attend and respond (Monsell, 2003). Here, a task-set involves the mental representation of task-relevant stimuli, responses, and their corresponding stimulus-response mappings, allowing for an appropriate action to be executed in response to a stimulus (cf. Monsell, 2003; Kiesel et al., 2010). Cognitive control thus grants the organism considerable behavioral flexibility, but it also incurs costs, in that controlled processing is slow and effortful (Norman and Shallice, 1986): to wit, it takes longer to perform a trial when a shift in the task-set is required than when staying with the same set –– known as ‘switch costs’ –– which is thought to reflect additional cognitive processing, including the updating of the task-set and the resolution of proactive interference (i.e., the previously activated task-set’s interference with the retrieval of the currently required task-set) (Rogers and Monsell, 1995; Badre and Wagner, 2006).

A central question about cognitive control concerns its regulation –– that is, how does the brain determine when and how much control should be applied (Botvinick et al., 2001)? The broad suggestion is that people predict forthcoming task demands and adjust processing accordingly (e.g., Shenhav et al., 2013; Egner, 2014; Jiang et al., 2014; Abrahamse et al., 2016; Waskom et al., 2017). Importantly, such expectations about task demands can be driven by two sources: explicit predictions provided by external cues (Rogers and Monsell, 1995; Dreisbach et al., 2002; Badre and Wagner, 2006) and internally generated, trial history-based predictions, which are typically implicit (Dreisbach and Haider, 2006; Mayr, 2006; Bugg and Crump, 2012; Egner, 2014; Chiu and Egner, 2017).

Previous behavioral studies showed that the two types of predictions appear to drive control simultaneously. In particular, trial history based predictions impact cognitive control even in cases where these predictions are redundant, as in the presence of 100% valid external cues for selecting the correct control strategy (e.g., Alpay et al., 2009; Correa et al., 2009; Kemper et al., 2016). However, it is not presently known whether and how the brain reconciles explicit external and (typically implicit) internal predictions. Here, we sought to characterize the computational and neural mechanisms that mediate the joint influence of external, cued-based, and internal, cognitive history-based (i.e., a participant’s history of cognitive processing involved in performing prior trials on this task) predictions about future task demands that drive the engagement of cognitive control. Specifically, we sought to characterize how internal and external predictions jointly affect proactive task-set updating. Note that while we make the assumption that expectations regarding forthcoming task demand can mitigate the costs of switching tasks, we here do not aim to distinguish between reduced switch costs due to improved task-set reconfiguration (Rogers and Monsell, 1995) versus improved resolution of mnemonic interference, or ‘task-set inertia’ (Allport et al., 1994; Badre and Wagner, 2006). Rather, we consider both of these processes integral components of successful task-set updating (Qiao et al., 2017).

We combined computational modeling of behavior on a probabilistic variant of the classic cued task switching paradigm (Dreisbach et al., 2002) with functional magnetic resonance imaging (fMRI) in healthy humans. On each trial, participants performed one of two perceptual decision tasks on an array of moving, colored dots (Figure 1). Crucially, prior to the presentation of the trial’s task cue and stimulus, an explicit probabilistic pre-cue informed participants of the likelihood that the forthcoming trial would require performing one task (color categorization) or the other (motion categorization) (Figure 1). This pre-cue provided an explicit cue-induced task prediction that could be used to guide preparatory task-set updating, and be contrasted with trial history-based, internally generated predictions about the forthcoming task.

Figure 1

Download asset Open asset

While the benefit of the explicit cue was represented by its predictive value, trial history was uninformative about the probability of task switching, as the task sequence was randomized. There was thus no objective benefit to generating trial history-based predictions. However, based on prior studies, we nevertheless anticipated that participants would form (likely implicit) internal expectations for forthcoming trials based on trial-history (e.g., Huettel et al., 2002), and this design ensured that trial-history and cue-based predictions were independent of each other. This allowed us to quantify the influence of each type of prediction in order to adjudicate between different possible models of control guidance, including a ‘rational’ model that is driven purely by the informative explicit cue.

We then leveraged the concurrently acquired fMRI data to trace neural representations of task and control demand predictions. Here, the first major question is whether externally and internally driven task predictions drive behavior in parallel or are integrated in a joint neural representation of task prediction. Second, it has been proposed that control processes, like the switching of a task-set, are guided by predictions of control demand (Shenhav et al., 2013; Egner, 2014; Jiang et al., 2014; Abrahamse et al., 2016; Waskom et al., 2017). Based on this proposal and the theory of dual mechanisms of cognitive control (Braver, 2012), we sought to characterize neural representations of proactive switch demand (the likelihood of having to switch tasks), which is determined by the relationship between the predicted forthcoming task and the task that was performed on the previous trial. Finally, our protocol also allowed us to assess the neural substrates of reactive cognitive control (specifically, reactive switch demand), based on the mismatch between task predictions and actual requirements at the time of task-stimulus onset.

To preview the results, task-switching behavior was jointly driven by internally generated and cue-induced task predictions and, strikingly, the impact of the former was stronger than that of the latter. Moreover, at the time of pre-cue onset, the fMRI data revealed an integrated representation of the joint external and internal predictions in left dorsolateral prefrontal cortex (dlPFC). This prediction informed a representation of proactive switch demand in the frontoparietal control network, and, at the time of task stimulus presentation, the prediction error associated with these joint predictions (i.e., reactive switch demand) was encoded in the dorsomedial prefrontal cortex (dmPFC), including the anterior cingulate cortex (ACC). Collectively, these data suggest that experientially acquired and explicitly cued expectations of control demand are reconciled in dlPFC and dmPFC to jointly guide the implementation of cognitive control.

The human brain is capable of anticipating forthcoming task demand and exerting proactive control to reconfigure information processing in line with those predictions (Shenhav et al., 2013; Egner, 2014; Jiang et al., 2014; Abrahamse et al., 2016; Waskom et al., 2017). In the present study, we sought to characterize how this regulation of anticipatory control is implemented in the context of concurrent external, cue-based, and internally generated, cognitive history-based predictions of forthcoming control demand in the form of task-set updating. By directly manipulating and formally modeling cue-based and cognitive history-based task predictions, we demonstrated that behavior was driven by joint predictions from external and internal sources, and that these predictions were integrated in (left) dlPFC prior to task stimulus onset. The discrepancy between the joint prediction and the previous-trial task, which signals the demand of proactively shifting task-set, was represented in frontoparietal regions belonging to the frontoparietal control or multiple-demand network, along with the insula, putamen and visual areas. Moreover, upon task stimulus presentation, reactive updating of task-set based on the joint task-demand prediction error was found to be encoded most prominently in the dmPFC.

We conducted a rigorous test of the reliance on internally generated predictions by making them non-informative. Despite of the lack of validity in predicting the forthcoming task, trial history-based, internally generated task prediction exhibited a strong modulation on RTs. This modulation was non-trivial, being about 3 times as strong as the modulation of the informative cue-induced task prediction at the group level. Notably, the finding that behavioral modulation by previous trials could be traced up to three trials back (Figure 2E–H) indicates that the modulation of internally generated prediction extends well beyond the scope of the classic task switch effect, which typically concerns only the immediately preceding trial. Formal model comparison also confirmed that the reliance on internally generated task prediction cannot be explained by the classic task switch effect alone.

While these results are consistent with some prior behavioral studies that documented effects of internal, trial history-based predictions even in the presence of 100% informative external cues (Alpay et al., 2009; Correa et al., 2009; Kemper et al., 2016), they are nevertheless surprising, because the strong reliance on the non-informative internally generated task prediction seems to contradict the premise of using such predictions – that is, to optimize the engagement of cognitive control. However, we argue that these results indicate that the process of applying proactive cognitive control based on concurrent internal and cue-based control demand predictions is itself a result of optimization based on a cost/benefit analysis, as proposed by an influential recent model of control regulation, the expected value of control (EVC) model (Shenhav et al., 2013; Shenhav et al., 2017). This model considers not only the potential benefits of applying top-down control but also the assumed inherent costs (or effort) of doing so (cf. Kool et al., 2010), proposing that the engagement of cognitive control is driven by a cost/benefit analysis that optimizes the predicted gain of applying control relative to its cost. When applied to the conundrum of the apparent overreliance on internal, trial-history based versus, external, cue-based control predictions, the EVC model would predict that this situation could come about if applying internal control predictions incurred a smaller cognitive effort cost than employing external control predictions.

The implied lower costs for internally generated prediction than cue-induced prediction may originate from two, not mutually exclusive sources. First, the cue-induced prediction requires learning and retrieving the associations between an external graphical pre-cue and its corresponding task-set prediction, which may incur a higher cost than internally generated prediction. Second, it is reasonable to assume that internally generated predictions that anticipate the near future to resemble the recent past represent a default cognitive strategy grounded in evolutionary adaptation to an environment of high temporal auto-correlation of sensory inputs (Dong and Atick, 1995). Accordingly, it has been shown that ongoing perception (Fischer and Whitney, 2014) and decision-making (Cheadle et al., 2014) are strongly reliant on recent experience. The present results suggest that the same is true for cognitive control processes: the recent past is (implicitly) employed as a powerful predictor of the immediate future (see Egner, 2014; Jiang et al., 2015b; Waskom et al., 2017). If such self-generated predictions of task demand represent a default mode of control regulation, their generation is likely cheap and, importantly, it may require substantial cognitive effort to override such predictions, which would be another means of rendering the use of the explicit external cue more costly.

An alternative account could be that the reliance on P_int originated from P_cue. Specifically, if the learning of P_cue for each pre-cue is achieved by learning from previous trials sharing the same pre-cue, the ‘informativeness ‘of the pre-cue specific trial history may render the whole trial history informative, hence promoting the reliance on P_int. As the learning of P_cue asymptotes, P_cue would become disentangled from P_int. This account leads to the key prediction that the reliance on P_int (i.e., 1-β) should be higher in the beginning of the task than later on, as the gradual detachment of P_cue from trial history would decrease the reliance on trial history over time. However, the lack of difference in β estimates between the first and last 3 runs of the fMRI session does not support this notion. One possibility is that this type of process may have occurred during the stair-casing procedure, and hence did not end up affecting the fMRI session.

The internally generated predictions we observed here took the form of increased expectations of task repetition with increasing run length of a given task (also known as the ‘hot-hand fallacy’; Gilovich et al., 1985). This contrasts with another intuitive possibility, whereby participants could have predicted a change in task with increasing run length of task repetitions (known as the ‘gambler’s fallacy’; Jarvik, 1951). The fact that we observed the former rather than the latter is in line with our assumption that the internal predictions we measure here are likely implicit in nature, as previous work has demonstrated a dissociation between explicit outcome predictions following the gambler’s fallacy and implicit predictions, as expressed in behavior, following the hot-hand fallacy (Perruchet, 1985; Jiménez and Méndez, 2013; Perruchet, 2015). Note also that participants’ tendency to follow either fallacy should depend on their beliefs as to whether events are generated randomly (in gambling) or non-randomly (the hot hand). Thus, the fact that internally generated predictions followed the hot-hand pattern further corroborates that participants (likely implicitly) assumed the trial history to be non-random.

The EVC model also predicts that the relative reliance on internally vs. externally generated predictions, which are the outcome of a cost-benefit analysis, will change as a function of the cost and/or the benefit of engaging the more informative externally generated predictions. Further studies are encouraged to explore other manipulations of benefit and cost (e.g., monetary reward) and test how such manipulations shift the reliance on the informative but costly explicit predictions.

The fMRI data shed light on how the two types of task predictions jointly guide cognitive control in the brain. First, rather than distinct anatomical sources exerting parallel influences on behavior, we detected an integrated representation of task predictions at the onset of the pre-cue in left dlPFC. This is in line with this region’s well-established role in representing task rules and strategies (for review, see Sakai, 2008; cf. Waskom et al., 2014; Waskom et al., 2017). Additionally, based on this joint prediction, the anticipated switch demand or amount of required task-set updating was represented in the frontoparietal control network, insula, and dorsal striatum. These regions have long been implicated in task-set regulation, as revealed initially by studies comparing neural activity on task-switch with task-repeat cues or trials (reviewed in Ruge et al., 2013), and more recently by studies using multivariate pattern analysis to decode the currently active task-set from frontoparietal cortex (Woolgar et al., 2011; Waskom et al., 2014; Loose et al., 2017; Qiao et al., 2017). The present results move beyond these findings by showing that more than just encoding a currently active task rule, the frontoparietal control network is engaged in predicting control demands, in the shape of representing the degree to which a current set has to be updated to suit the forthcoming task (or switch likelihood).

After the onset of the task cue and task stimulus, encoding of the prediction error associated with that joint prediction was found in the dmPFC/ACC, which is consistent with a large literature demonstrating ACC and dmPFC encoding of prediction error in a variety of different contexts (Holroyd and Coles, 2002; Ito et al., 2003; Matsumoto et al., 2007; Alexander and Brown, 2011; Ullsperger et al., 2014). The representation of joint prediction error also reflects the degree to which the task-set has to be updated reactively; thus, this finding is also consistent with the theory that the ACC monitors performance and signals the need for adjustments in cognitive control (Botvinick et al., 2001; Ridderinkhof et al., 2004). Finally, we also documented that the neural substrates of prediction error from internally generated predictions in the present study matched those of a recent study where those self-generated predictions were related to probabilistically predictable task sequences (Waskom et al., 2017). This finding indicates that internal predictions derived from a non-predictive trial sequence are encoded in much the same fashion as those derived from a predictable sequence.

Our findings also exhibit a clear temporal segregation between the frontoparietal encoding of the proactive switch demand at the onset of pre-cue (Figure 5A) and the dmPFC/ACC encoding of reactive switch demand when the cue and task stimulus were presented (Figure 6A). This temporal and functional differentiation maps closely onto the ‘dual mechanisms of cognitive control’ framework (Braver, 2012), which distinguishes between proactive, anticipatory, and reactive, compensatory application of control. The present results strongly suggest that the lateral frontoparietal control network guides proactive control implementation, whereas the dmPFC/ACC detects the prediction error of control demand prediction, and signals the need of (or perhaps implements) reactive control to match the actual (rather than anticipated) cognitive control demand. In support of this contention, in a recent study that explicitly distinguished between proactive and reactive control in a conflict task, a left IFG area overlapping the IFG area in Figure 5 was also implicated in using proactive control demand predictions (Jiang et al., 2015a). Furthermore, disrupting function in this area using transcranial magnetic stimulation selectively diminished the effects of learned control predictions on behavior (Muhle-Karbe et al., 2018).

Finally, the current findings suggest a novel extension of the EVC theory, which originally addressed a cost/benefit analysis of engaging control after prediction of control demands are formed (Shenhav et al., 2013). The present results suggest that a cost/benefit optimization process is also applicable to the preceding stage, where different inputs to the control demand prediction process are reconciled. This demand prediction reconciliation process would not only precede the putative EVC calculation, but should also directly influence it, and do so in a unidirectional, hierarchical fashion. More generally, in light of recent theoretic proposals of the hierarchical architecture of cognitive control (Koechlin and Summerfield, 2007; Badre and Nee, 2018), an overarching optimization procedure may simultaneously consider the costs and benefits of multiple cognitive control processes across different levels in the hierarchy, in order to guide goal-directed behavior while taking into account both the benefit and the cost of engaging cognitive control.

In conclusion, we combined a probabilistic cued task switching with computational modeling and neuroimaging to show that concurrent externally and internally derived predictions of cognitive control demand are reconciled to form a joint prediction. Behavior was dominated by internal, trial-history based predictions, likely due to the lower cost of generating or higher cost of overriding these predictions. The integrated prediction was encoded in dlPFC and guided proactive cognitive control over task-set updating, which was represented in the frontoparietal control network. Subsequently, if actual control demand deviates from predicted demand, the dmPFC/ACC is engaged to compensate the prediction error of the joint prediction, and guide reactive updating. In this manner, the present findings reveal that flexible human behavior depends on multiple regulatory processes that govern cognitive control.

Statistical maps for all whole-brain fMRI analyses have been uploaded to https://neurovault.org/collections/3732/. Original behavioral and fMRI data are available at https://openneuro.org/datasets/ds001493. The MATLAB source code for the task and key analyses has been made available on GitHub (https://github.com/JiefengJiang/eLife2018; copy archived at https://github.com/elifesciences-publications/eLife2018).

1. 1. Jiefeng Jiang
   2. Anthony D Wagner
   3. Tobias Egner

   (2018) Statistical maps for all whole-brain fMRI analyses

   Publicly available at the NeuroVault database. (accession no. 3732).

   https://neurovault.org/collections/3732/
2. 1. Jiefeng Jiang
   2. Anthony D Wagner
   3. Tobias Egner

   (2018) Original behavioral and fMRI data

   Publicly available at the OpenNeuro website.

   https://openneuro.org/datasets/ds001493

1. 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
2. 1. Alexander WH
   2. Brown JW

   (2011) Medial prefrontal cortex as an action-outcome predictor

   Nature Neuroscience 14:1338–1344.

   https://doi.org/10.1038/nn.2921

   • PubMed
   • Google Scholar
3. Book

   1. Allport A
   2. Styles EA
   3. Hsieh S

   (1994)

   Shifting intentional set: Exploring the dynamic control of tasks

   In: Umilta C, Moscovitvh M, editors. Conscious and Nonconscious Information Processing: Attention and Performance XV. Cambridge, MA: MIT Press. pp. 421–452.

   • Google Scholar
4. 1. Alpay G
   2. Goerke M
   3. Stürmer B

   (2009) Precueing imminent conflict does not override sequence-dependent interference adaptation

   Psychological Research Psychologische Forschung 73:803–816.

   https://doi.org/10.1007/s00426-008-0196-9

   • PubMed
   • Google Scholar
5. 1. Badre D
   2. Nee DE

   (2018) Frontal cortex and the hierarchical control of behavior

   Trends in Cognitive Sciences 22:170–188.

   https://doi.org/10.1016/j.tics.2017.11.005

   • PubMed
   • Google Scholar
6. 1. Badre D
   2. Wagner AD

   (2006) Computational and neurobiological mechanisms underlying cognitive flexibility

   PNAS 103:7186–7191.

   https://doi.org/10.1073/pnas.0509550103

   • PubMed
   • Google Scholar
7. 1. Botvinick MM
   2. Braver TS
   3. Barch DM
   4. Carter CS
   5. Cohen JD

   (2001) Conflict monitoring and cognitive control

   Psychological Review 108:624–652.

   https://doi.org/10.1037/0033-295X.108.3.624

   • PubMed
   • Google Scholar
8. 1. Brainard DH

   (1997) The psychophysics toolbox

   Spatial Vision 10:433–436.

   https://doi.org/10.1163/156856897X00357

   • PubMed
   • Google Scholar
9. 1. Braver TS

   (2012) The variable nature of cognitive control: a dual mechanisms framework

   Trends in Cognitive Sciences 16:106–113.

   https://doi.org/10.1016/j.tics.2011.12.010

   • PubMed
   • Google Scholar
10. 1. Bugg JM
    2. Crump MJ

    (2012) In support of a distinction between voluntary and Stimulus-Driven control: a review of the literature on proportion congruent effects

    Frontiers in Psychology 3:367.

    https://doi.org/10.3389/fpsyg.2012.00367

    • PubMed
    • Google Scholar
11. 1. Cheadle S
    2. Wyart V
    3. Tsetsos K
    4. Myers N
    5. de Gardelle V
    6. Herce Castañón S
    7. Summerfield C

    (2014) Adaptive gain control during human perceptual choice

    Neuron 81:1429–1441.

    https://doi.org/10.1016/j.neuron.2014.01.020

    • PubMed
    • Google Scholar
12. 1. Chiu YC
    2. Jiang J
    3. Egner T

    (2017) The caudate nucleus mediates learning of Stimulus-Control state associations

    The Journal of Neuroscience 37:1028–1038.

    https://doi.org/10.1523/JNEUROSCI.0778-16.2016

    • PubMed
    • Google Scholar
13. 1. Chiu Y-C
    2. Egner T

    (2017) Cueing cognitive flexibility: item-specific learning of switch readiness

    Journal of Experimental Psychology: Human Perception and Performance 43:1950–1960.

    https://doi.org/10.1037/xhp0000420

    • Google Scholar
14. 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
15. 1. Correa A
    2. Rao A
    3. Nobre AC

    (2009) Anticipating conflict facilitates controlled stimulus-response selection

    Journal of Cognitive Neuroscience 21:1461–1472.

    https://doi.org/10.1162/jocn.2009.21136

    • PubMed
    • Google Scholar
16. 1. Daw ND
    2. Niv Y
    3. Dayan P

    (2005) Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

    Nature Neuroscience 8:1704–1711.

    https://doi.org/10.1038/nn1560

    • PubMed
    • Google Scholar
17. 1. Dong DW
    2. Atick JJ

    (1995) Statistics of natural time-varying images

    Network: Computation in Neural Systems 6:345–358.

    https://doi.org/10.1088/0954-898X_6_3_003

    • Google Scholar
18. 1. Dreisbach G
    2. Haider H
    3. Kluwe RH

    (2002) Preparatory processes in the task-switching paradigm: evidence from the use of probability cues

    Journal of Experimental Psychology: Learning, Memory, and Cognition 28:468–483.

    https://doi.org/10.1037/0278-7393.28.3.468

    • PubMed
    • Google Scholar
19. 1. Dreisbach G
    2. Haider H

    (2006) Preparatory adjustment of cognitive control in the task switching paradigm

    Psychonomic Bulletin & Review 13:334–338.

    https://doi.org/10.3758/BF03193853

    • PubMed
    • Google Scholar
20. 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
21. 1. Egner T

    (2014) Creatures of habit (and control): a multi-level learning perspective on the modulation of congruency effects

    Frontiers in Psychology 5:1247.

    https://doi.org/10.3389/fpsyg.2014.01247

    • PubMed
    • Google Scholar
22. Book

    1. Egner T

    (2017)

    The Wiley Handbook of Cognitive Control

    Oxford: Wiley-Blackwell.

    • Google Scholar
23. 1. Fischer J
    2. Whitney D

    (2014) Serial dependence in visual perception

    Nature Neuroscience 17:738–743.

    https://doi.org/10.1038/nn.3689

    • PubMed
    • Google Scholar
24. 1. Frank MJ
    2. Loughry B
    3. O'Reilly RC

    (2001) Interactions between frontal cortex and basal ganglia in working memory: a computational model

    Cognitive, Affective, & Behavioral Neuroscience 1:137–160.

    https://doi.org/10.3758/CABN.1.2.137

    • PubMed
    • Google Scholar
25. 1. Gilovich T
    2. Vallone R
    3. Tversky A

    (1985) The hot hand in basketball: On the misperception of random sequences

    Cognitive Psychology 17:295–314.

    https://doi.org/10.1016/0010-0285(85)90010-6

    • Google Scholar
26. 1. Holroyd CB
    2. Coles MGH

    (2002) The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity

    Psychological Review 109:679–709.

    https://doi.org/10.1037/0033-295X.109.4.679

    • PubMed
    • Google Scholar
27. 1. Huettel SA
    2. Mack PB
    3. McCarthy G

    (2002) Perceiving patterns in random series: dynamic processing of sequence in prefrontal cortex

    Nature Neuroscience 5:485–490.

    https://doi.org/10.1038/nn841

    • PubMed
    • Google Scholar
28. 1. Ito S
    2. Stuphorn V
    3. Brown JW
    4. Schall JD

    (2003) Performance monitoring by the anterior cingulate cortex during saccade countermanding

    Science 302:120–122.

    https://doi.org/10.1126/science.1087847

    • PubMed
    • Google Scholar
29. 1. Jarvik ME

    (1951) Probability learning and a negative recency effect in the serial anticipation of alternative symbols

    Journal of Experimental Psychology 41:291–297.

    https://doi.org/10.1037/h0056878

    • PubMed
    • Google Scholar
30. 1. Jiang J
    2. Heller K
    3. Egner T

    (2014) Bayesian modeling of flexible cognitive control

    Neuroscience & Biobehavioral Reviews 46:30–43.

    https://doi.org/10.1016/j.neubiorev.2014.06.001

    • PubMed
    • Google Scholar
31. 1. Jiang J
    2. Beck J
    3. Heller K
    4. Egner T

    (2015a) An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

    Nature Communications 6:8165.

    https://doi.org/10.1038/ncomms9165

    • PubMed
    • Google Scholar
32. 1. Jiang J
    2. Brashier NM
    3. Egner T

    (2015b) Memory meets control in hippocampal and striatal binding of stimuli, responses, and attentional control states

    Journal of Neuroscience 35:14885–14895.

    https://doi.org/10.1523/JNEUROSCI.2957-15.2015

    • Google Scholar
33. 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
34. 1. Jiménez L
    2. Méndez A

    (2013) It is not what you expect: dissociating conflict adaptation from expectancies in a stroop task

    Journal of Experimental Psychology: Human Perception and Performance 39:271–284.

    https://doi.org/10.1037/a0027734

    • Google Scholar
35. 1. Jimura K
    2. Cazalis F
    3. Stover ER
    4. Poldrack RA

    (2014) The neural basis of task switching changes with skill acquisition

    Frontiers in Human Neuroscience 8:339.

    https://doi.org/10.3389/fnhum.2014.00339

    • PubMed
    • Google Scholar
36. 1. Kemper M
    2. Gaschler R
    3. Schwager S
    4. Schubert T

    (2016) The benefit of expecting no conflict--stronger influence of self-generated than cue-induced conflict expectations on stroop performance

    Acta Psychologica 163:135–141.

    https://doi.org/10.1016/j.actpsy.2015.11.008

    • PubMed
    • Google Scholar
37. 1. Kiesel A
    2. Steinhauser M
    3. Wendt M
    4. Falkenstein M
    5. Jost K
    6. Philipp AM
    7. Koch I

    (2010) Control and interference in task switching--a review

    Psychological Bulletin 136:849–874.

    https://doi.org/10.1037/a0019842

    • PubMed
    • Google Scholar
38. 1. Koechlin E
    2. Summerfield C

    (2007) An information theoretical approach to prefrontal executive function

    Trends in Cognitive Sciences 11:229–235.

    https://doi.org/10.1016/j.tics.2007.04.005

    • PubMed
    • Google Scholar
39. 1. Kool W
    2. McGuire JT
    3. Rosen ZB
    4. Botvinick MM

    (2010) Decision making and the avoidance of cognitive demand

    Journal of Experimental Psychology: General 139:665–682.

    https://doi.org/10.1037/a0020198

    • PubMed
    • Google Scholar
40. 1. Loose LS
    2. Wisniewski D
    3. Rusconi M
    4. Goschke T
    5. Haynes JD

    (2017) Switch-Independent task representations in frontal and parietal cortex

    The Journal of Neuroscience 37:8033–8042.

    https://doi.org/10.1523/JNEUROSCI.3656-16.2017

    • PubMed
    • Google Scholar
41. 1. Matsumoto M
    2. Matsumoto K
    3. Abe H
    4. Tanaka K

    (2007) Medial prefrontal cell activity signaling prediction errors of action values

    Nature Neuroscience 10:647–656.

    https://doi.org/10.1038/nn1890

    • PubMed
    • Google Scholar
42. 1. Mayr U

    (2006) What matters in the cued task-switching paradigm: tasks or cues?

    Psychonomic Bulletin & Review 13:794–799.

    https://doi.org/10.3758/BF03193999

    • PubMed
    • Google Scholar
43. 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
44. 1. Monsell S

    (2003) Task switching

    Trends in Cognitive Sciences 7:134–140.

    https://doi.org/10.1016/S1364-6613(03)00028-7

    • PubMed
    • Google Scholar
45. 1. Muhle-Karbe PS
    2. Jiang J
    3. Egner T

    (2018) Causal evidence for Learning-Dependent frontal lobe contributions to cognitive control

    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 38:962–973.

    https://doi.org/10.1523/JNEUROSCI.1467-17.2017

    • PubMed
    • Google Scholar
46. 1. Mumford JA
    2. Turner BO
    3. Ashby FG
    4. Poldrack RA

    (2012) Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses

    NeuroImage 59:2636–2643.

    https://doi.org/10.1016/j.neuroimage.2011.08.076

    • PubMed
    • Google Scholar
47. 1. Norman DA
    2. Shallice T

    (1986)

    Consciousness and Self-Regulation (Schwarz GE, Shapiro D

    Attention to action: willed and automatic control of behavior, Consciousness and Self-Regulation (Schwarz GE, Shapiro D, New York, Plenum Press, 10.1007/978-1-4757-0629-1_1.

    • Google Scholar
48. 1. Perruchet P

    (1985)

    A pitfall for the expectancy theory of human eyelid conditioning

    The Pavlovian Journal of Biological Science 20:163–170.

    • PubMed
    • Google Scholar
49. 1. Perruchet P

    (2015) Dissociating conscious expectancies from automatic link formation in associative learning: a review on the so-called perruchet effect

    Journal of Experimental Psychology: Animal Learning and Cognition 41:105–127.

    https://doi.org/10.1037/xan0000060

    • Google Scholar
50. 1. Qiao L
    2. Zhang L
    3. Chen A
    4. Egner T

    (2017) Dynamic Trial-by-Trial recoding of Task-Set representations in the frontoparietal cortex mediates behavioral flexibility

    The Journal of Neuroscience 37:11037–11050.

    https://doi.org/10.1523/JNEUROSCI.0935-17.2017

    • PubMed
    • Google Scholar
51. 1. Ridderinkhof KR
    2. Ullsperger M
    3. Crone EA
    4. Nieuwenhuis S

    (2004) The role of the medial frontal cortex in cognitive control

    Science 306:443–447.

    https://doi.org/10.1126/science.1100301

    • PubMed
    • Google Scholar
52. 1. Rogers RD
    2. Monsell S

    (1995) Costs of a predictible switch between simple cognitive tasks

    Journal of Experimental Psychology: General 124:207–231.

    https://doi.org/10.1037/0096-3445.124.2.207

    • Google Scholar
53. 1. Ruge H
    2. Jamadar S
    3. Zimmermann U
    4. Karayanidis F

    (2013) The many faces of preparatory control in task switching: reviewing a decade of fMRI research

    Human Brain Mapping 34:12–35.

    https://doi.org/10.1002/hbm.21420

    • PubMed
    • Google Scholar
54. 1. Sakai K

    (2008) Task set and prefrontal cortex

    Annual Review of Neuroscience 31:219–245.

    https://doi.org/10.1146/annurev.neuro.31.060407.125642

    • PubMed
    • Google Scholar
55. 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
56. 1. Shenhav A
    2. Musslick S
    3. Lieder F
    4. Kool W
    5. Griffiths TL
    6. Cohen JD
    7. Botvinick MM

    (2017) Toward a rational and mechanistic account of mental effort

    Annual Review of Neuroscience 40:99–124.

    https://doi.org/10.1146/annurev-neuro-072116-031526

    • PubMed
    • Google Scholar
57. 1. Stephan KE
    2. Penny WD
    3. Daunizeau J
    4. Moran RJ
    5. Friston KJ

    (2009) Bayesian model selection for group studies

    NeuroImage 46:1004–1017.

    https://doi.org/10.1016/j.neuroimage.2009.03.025

    • PubMed
    • Google Scholar
58. 1. Ullsperger M
    2. Danielmeier C
    3. Jocham G

    (2014) Neurophysiology of performance monitoring and adaptive behavior

    Physiological Reviews 94:35–79.

    https://doi.org/10.1152/physrev.00041.2012

    • PubMed
    • Google Scholar
59. 1. Waskom ML
    2. Kumaran D
    3. Gordon AM
    4. Rissman J
    5. Wagner AD

    (2014) Frontoparietal representations of task context support the flexible control of goal-directed cognition

    Journal of Neuroscience 34:10743–10755.

    https://doi.org/10.1523/JNEUROSCI.5282-13.2014

    • PubMed
    • Google Scholar
60. 1. Waskom ML
    2. Frank MC
    3. Wagner AD

    (2017) Adaptive engagement of cognitive control in Context-Dependent decision making

    Cerebral Cortex 27:1270–1284.

    https://doi.org/10.1093/cercor/bhv333

    • PubMed
    • Google Scholar
61. 1. Woolgar A
    2. Hampshire A
    3. Thompson R
    4. Duncan J

    (2011) Adaptive coding of task-relevant information in human frontoparietal cortex

    Journal of Neuroscience 31:14592–14599.

    https://doi.org/10.1523/JNEUROSCI.2616-11.2011

    • PubMed
    • Google Scholar
62. 1. Xue G
    2. Dong Q
    3. Chen C
    4. Lu Z
    5. Mumford JA
    6. Poldrack RA

    (2010) Greater neural pattern similarity across repetitions is associated with better memory

    Science 330:97–101.

    https://doi.org/10.1126/science.1193125

    • PubMed
    • Google Scholar
63. 1. Yeo BT
    2. Krienen FM
    3. Sepulcre J
    4. Sabuncu MR
    5. Lashkari D
    6. Hollinshead M
    7. Roffman JL
    8. Smoller JW
    9. Zöllei L
    10. Polimeni JR
    11. Fischl B
    12. Liu H
    13. Buckner RL

    (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

    Journal of Neurophysiology 106:1125–1165.

    https://doi.org/10.1152/jn.00338.2011

    • PubMed
    • Google Scholar

Author details

1. Jiefeng Jiang

   Department of Psychology, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft

   For correspondence

   jiefeng.jiang@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4264-6382

2. Anthony D Wagner

   1. Department of Psychology, Stanford University, Stanford, United States
   2. Neuroscience Program, Stanford University, Stanford, United States

   Contribution

   Supervision, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0624-4543

3. Tobias Egner

   1. Center for Cognitive Neuroscience, Duke University, Durham, United States
   2. Department of Psychology and Neuroscience, Duke University, Durham, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7956-3241

• 2,423

  views

• 378

  downloads

• 24

  citations

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