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

Jiang J, Beck J, Heller K, Egner T - Nat Commun (2015)

Bottom Line: Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate.The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices.These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive Neuroscience, Duke University, PO Box 90999, Durham, North Carolina 27708, USA.

ABSTRACT
The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences.

No MeSH data available.


Modulation of volatility on predicted control demand (conflict level, N=21).(a) Searchlights in the caudate track the model's prediction of conflict level (in red, P<0.05 corrected, one sample t-test) (b) The flexible control model, highlighting in red the information processing mechanisms related to the modulation of volatility on predicted conflict level. (c) Individual modulation of volatility on caudate activity-derived LR. Each horizontal bar represents a participant.
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f4: Modulation of volatility on predicted control demand (conflict level, N=21).(a) Searchlights in the caudate track the model's prediction of conflict level (in red, P<0.05 corrected, one sample t-test) (b) The flexible control model, highlighting in red the information processing mechanisms related to the modulation of volatility on predicted conflict level. (c) Individual modulation of volatility on caudate activity-derived LR. Each horizontal bar represents a participant.

Mentions: In the flexible control model, the volatility-driven flexible LR informs the prediction of control demand (conflict). We therefore next sought to identify the neural substrates of conflict prediction, by conducting a whole-brain search for brain regions whose activation vectors could account for significant variance in the (residual) conflict prediction variable vector (f). We found that the body of the (right) dorsal caudate nucleus tracked the model's predicted conflict levels (Fig. 4a; P<0.05, corrected) via a heterogeneous (multivariate) coding scheme (Fig. 1b). Additionally, predicted conflict levels were also encoded in a homogeneous (univariate) fashion in the left inferior parietal lobule, left superior frontal gyrus and right paracentral lobule, where larger predicted conflict levels were associated with higher activity. We next performed an additional, independent analysis to cross-validate these putative neural substrates of volatility-driven updating of predicted conflict.


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

Jiang J, Beck J, Heller K, Egner T - Nat Commun (2015)

Modulation of volatility on predicted control demand (conflict level, N=21).(a) Searchlights in the caudate track the model's prediction of conflict level (in red, P<0.05 corrected, one sample t-test) (b) The flexible control model, highlighting in red the information processing mechanisms related to the modulation of volatility on predicted conflict level. (c) Individual modulation of volatility on caudate activity-derived LR. Each horizontal bar represents a participant.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595591&req=5

f4: Modulation of volatility on predicted control demand (conflict level, N=21).(a) Searchlights in the caudate track the model's prediction of conflict level (in red, P<0.05 corrected, one sample t-test) (b) The flexible control model, highlighting in red the information processing mechanisms related to the modulation of volatility on predicted conflict level. (c) Individual modulation of volatility on caudate activity-derived LR. Each horizontal bar represents a participant.
Mentions: In the flexible control model, the volatility-driven flexible LR informs the prediction of control demand (conflict). We therefore next sought to identify the neural substrates of conflict prediction, by conducting a whole-brain search for brain regions whose activation vectors could account for significant variance in the (residual) conflict prediction variable vector (f). We found that the body of the (right) dorsal caudate nucleus tracked the model's predicted conflict levels (Fig. 4a; P<0.05, corrected) via a heterogeneous (multivariate) coding scheme (Fig. 1b). Additionally, predicted conflict levels were also encoded in a homogeneous (univariate) fashion in the left inferior parietal lobule, left superior frontal gyrus and right paracentral lobule, where larger predicted conflict levels were associated with higher activity. We next performed an additional, independent analysis to cross-validate these putative neural substrates of volatility-driven updating of predicted conflict.

Bottom Line: Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate.The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices.These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive Neuroscience, Duke University, PO Box 90999, Durham, North Carolina 27708, USA.

ABSTRACT
The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences.

No MeSH data available.