An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

Jiang, Jiefeng; Beck, Jeffrey M.; Heller, Katherine; Egner, Tobias

doi:10.1038/ncomms9165

Cited by 128 publications

(159 citation statements)

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“…A subsequent encounter of the same stimulus then triggers the retrieval of that memory representation, which includes the associated control states (Abrahamse, Braem, Notebaert, & Verguts, 2016; Egner, 2014). The neural substrates mediating this binding of bottom-up stimulus features with abstract control states have been localized to the hippocampus and the dorsal striatum in the context of conflict processing (Chiu, Jiang, & Egner, 2017; Jiang, Brashier, & Egner, 2015; Jiang et al, 2015). These two structures appear to play complementary roles in mediating S-C learning.…”

Section: Discussionmentioning

confidence: 99%

Cueing cognitive flexibility: Item-specific learning of switch readiness.

Chiu¹,

Egner²

2017

Journal of Experimental Psychology: Human Perception and Perfor

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The rich behavioral repertoire of the human species derives from our ability to flexibly reconfigure processing strategies (task sets) in response to changing requirements. This updating of task sets is effortful, as reflected by longer response times when switching a task than repeating it (switch costs). However, some recent data suggest that switch costs can be reduced by cueing switch readiness bottom-up, by associating particular stimuli with frequent switch requirements. This type of “stimulus-control (S-C) learning” would be highly adaptive, as it combines the speed of automatic (bottom-up) processing with the flexibility and generalizability of controlled (top-down) processing. However, it is unclear whether S-C learning of switch readiness is truly possible, and what the underlying mechanisms are. Here we address these questions by pairing specific stimuli with a need to update task-sets either frequently or rarely. In all three experiments, we observe robust item-specific switch probability (ISSP) effects as revealed by smaller switch costs for frequent switch items than for rare switch items. By including a neutral condition, we also show that the ISSP effect is primarily driven by S-C learning reducing switch costs in frequent switch items. Furthermore, by employing three tasks in Experiment 3, we establish that the ISSP effect reflects an enhancement of general switch readiness, rather than of the readiness to switch to a specific alternate task. These results firmly establish that switch readiness is malleable by item-specific S-C learning processes, documenting that a generalizable state of cognitive flexibility can be primed by a bottom-up stimulus.

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Section: Discussionmentioning

confidence: 99%

Cueing cognitive flexibility: Item-specific learning of switch readiness.

Chiu¹,

Egner²

2017

Journal of Experimental Psychology: Human Perception and Perfor

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“…Increasingly, however, anterior cingulate and insular cortices are recognized as more broadly deployed—assigning salience for the coordination of dynamic interaction of large-scale neural networks in response to contextual demands (Jiang et al, 2015; Medford & Critchley, 2010; Menon & Uddin, 2010; (Jiang et al, 2015; Medford & Critchley, 2010; Menon & Uddin, 2010; Power et al, 2013; Sridharan et al, 2008). Specific to the critical role of the cingulo-opercular network in cognitive control is its coordination with the frontal-parietal network to function as a superordinate or “multiple demand” cognitive control/processing network (Duncan, 2010; Duncan & Owen 2000; Müller et al, 2015).…”

Section: Clues To Core Cognitive Control Dysfunction Common Across Psmentioning

confidence: 99%

“…Additionally lesions to nodes of one of the subnetworks hampers functional connectivity within that network, while integrity of the other network remains relatively preserved (Nomura et al, 2012). Recent work on causal interactions between these subnetworks during cognitive tasks (Cai et al, 2015; Chen et al, 2015; Jiang et al, 2015) suggests the anterior insula amplifies salience detection in the anterior and mid-cingulate in a manner proportional to both cognitive demand and individual capacity, and, in turn prompts activation of the broader frontal-parietal network, particularly lateral frontal regions (dlPFC/IFJ) and parietal cortex. Furthermore, Cieslik and colleagues (2013) performed a coactivation-based parcellation of the left lateral frontal cortex across cognitive paradigms.…”

Section: Clues To Core Cognitive Control Dysfunction Common Across Psmentioning

confidence: 99%

“…In contrast, patient hyperactivation was observed in a more posterior region of the dACC extending to supplementary motor area. Increased mid-cingulate/pre-SMA among patients relative to control participants may reflect a compensatory process for maintaining intact performance amidst deficiencies in other multiple demand network nodes (i.e., adaptively balancing proactive versus reactive control; Jiang et al, 2015). …”

Section: Clues To Core Cognitive Control Dysfunction Common Across Psmentioning

confidence: 99%

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Transdiagnostic impairment of cognitive control in mental illness

McTeague

Goodkind

Etkin

2016

Journal of Psychiatric Research

255

180

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Intact cognitive control or executive function has characteristic patterns in both behavior and functional neurocircuitry. Functional neuroimaging studies have shown that a frontal-cingulate-parietal-insular (i.e., “multiple demand”) network forms a common functional substrate undergirding successful adaptation to diverse cognitive processing demands. Separate work on intact neurocognitive performance implicates a higher order factor that largely explains performance across domains and may reflect trait cognitive control capacity. In the current review we highlight findings from respective psychiatric disorders (i.e., psychotic, bipolar and unipolar depressive, anxiety, and substance use disorders) suggesting that cognitive control perturbations amidst psychopathology are most pronounced within these common brain and behavioral indices of adaptive cognitive functioning and moreover, are evident across disorders (i.e., transdiagnostically). Specifically, within each of the disorder classes impairments are consistent in the multiple demand network across a wide range of cognitive tasks. While severity varies between disorders, broad as opposed to domain-specific impairments consistently emerge in neurocognitive performance. Accumulating findings have revealed that phenotypically diverse psychiatric disorders share a common factor or vulnerability to dysfunction that is in turn related to broad neurocognitive deficits. Furthermore, we have observed that regions of the multiple demand network, which overlap with the salience network (dorsal anterior cingulate and bilateral anterior insula) are characterized by reduced gray matter transdiagnostically and predict weaker neurocognitive performance. In summary, transdiagnostic (as opposed to disorder-specific) patterns of symptomatic distress and neurocognitive performance deficits, concurrent with parallel anomalies of brain structure and function may largely contribute to the real-world socio-occupational impairment common across disorders.

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“…Temporary extended increases in prediction errors indicate volatile environments and lead to a fast adjustment of internal predictions and behavior ( Jiang, Beck, Heller, & Egner, 2015;Chumbley et al, 2014;Schiffer, Ahlheim, Wurm, & Schubotz, 2012;Friston, Daunizeau, & Kiebel, 2009;Behrens, Woolrich, Walton, & Rushworth, 2007). Recent studies provide evidence that longer timescale tonic DA modulates the extent to which prior action outcome biases phasic DA release and hence future action selection ( Yu, FitzGerald, & Friston, 2013;Humphries, Khamassi, & Gurney, 2012;Beeler, Daw, Frazier, & Zhuang, 2010).…”

Section: Introductionmentioning

confidence: 99%

Frontostriatal Contribution to the Interplay of Flexibility and Stability in Serial Prediction

Trempler

Schiffer

El-Sourani

et al. 2017

Journal of Cognitive Neuroscience

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Abstract■ Surprising events may be relevant or irrelevant for behavior, requiring either flexible adjustment or stabilization of our model of the world and according response strategies. Cognitive flexibility and stability in response to environmental demands have been described as separable cognitive states, associated with activity of striatal and lateral prefrontal regions, respectively. It so far remains unclear, however, whether these two states act in an antagonistic fashion and which neural mechanisms mediate the selection of respective responses, on the one hand, and a transition between these states, on the other. In this study, we tested whether the functional dichotomy between striatal and prefrontal activity applies for the separate functions of updating (in response to changes in the environment, i.e., switches) and shielding (in response to chance occurrences of events violating expectations, i.e., drifts) of current predictions. We measured brain activity using fMRI while 20 healthy participants performed a task that required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press while sequence omissions (drifts) had to be ignored. We further varied the probability of switches and drifts to assess the neural network supporting the transition between flexible and stable cognitive states as a function of recent performance history in response to environmental demands. Flexible switching between models was associated with activation in medial pFC (BA 9 and BA 10), whereas stable maintenance of the internal model corresponded to activation in the lateral pFC (BA 6 and inferior frontal gyrus). Our findings extend previous studies on the interplay of flexibility and stability, suggesting that different prefrontal regions are activated by different types of prediction errors, dependent on their behavioral requirements. Furthermore, we found that striatal activation in response to switches and drifts was modulated by participants' successful behavior toward these events, suggesting the striatum to be responsible for response selections following unpredicted stimuli. Finally, we observed that the dopaminergic midbrain modulates the transition between different cognitive states, thresholded by participants' individual performance history in response to temporal environmental demands. ■

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An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

Cited by 128 publications

References 54 publications

Cueing cognitive flexibility: Item-specific learning of switch readiness.

Cueing cognitive flexibility: Item-specific learning of switch readiness.

Transdiagnostic impairment of cognitive control in mental illness

Frontostriatal Contribution to the Interplay of Flexibility and Stability in Serial Prediction

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