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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.


Experimental task and simulation and behavioural results (N=21).(a) Example stimuli and timing of presentation. This example depicts an incongruent trial, followed by a congruent trial. (b) Individual mean model LRs, plotted as a function of run type. Each line represents a subject. (c) The time course of group mean LR and s.e.m. in the first and last 10 trials of volatile (in blue) and stable (in red) blocks. Note that in this graph, which averages over all blocks, the difference in LR at the beginning of the blocks was driven by volatile blocks 2–4 in the volatile runs, as in these blocks the LR had already been raised by preceding volatile blocks. (d) Time courses of the underlying proportion congruency (in black) and the corresponding predicted conflict level (in red) of an example run. (e) Individual RS, plotted as a function of congruency. Each line represents one subject. Note that higher RT equals lower RS. (Con=congruent trials; Inc=incongruent trials). (f) Group mean RS and s.e.m., centred across trials for each subject, plotted as a function of unsigned prediction error of congruency (control prediction error).
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f2: Experimental task and simulation and behavioural results (N=21).(a) Example stimuli and timing of presentation. This example depicts an incongruent trial, followed by a congruent trial. (b) Individual mean model LRs, plotted as a function of run type. Each line represents a subject. (c) The time course of group mean LR and s.e.m. in the first and last 10 trials of volatile (in blue) and stable (in red) blocks. Note that in this graph, which averages over all blocks, the difference in LR at the beginning of the blocks was driven by volatile blocks 2–4 in the volatile runs, as in these blocks the LR had already been raised by preceding volatile blocks. (d) Time courses of the underlying proportion congruency (in black) and the corresponding predicted conflict level (in red) of an example run. (e) Individual RS, plotted as a function of congruency. Each line represents one subject. Note that higher RT equals lower RS. (Con=congruent trials; Inc=incongruent trials). (f) Group mean RS and s.e.m., centred across trials for each subject, plotted as a function of unsigned prediction error of congruency (control prediction error).

Mentions: fMRI data were acquired while subjects (N=21) performed a face-word Stroop conflict task61018, in which they responded to the gender of face images via button-presses and tried to disregard overlaid gender word labels (Fig. 2a), which could be either congruent (for example, ‘male' superimposed on a male face) or incongruent (for example, ‘female' superimposed on a male face). To investigate the flexible adjustment of cognitive control, we furthermore varied the relative dependence on short-term versus long-term trial history required for achieving optimal prediction of control demand. In the volatile control demand condition, the trial sequence sampled alternately from distributions with a 20 or 80% proportion of incongruent trials, switching every 20 trials over a run of 80 trials. Conversely, in the stable control demand condition, the underlying proportion of incongruent trials remained unchanged (either 20 or 80%) for a run of 80 trials. In the volatile condition, more frequent change of the proportion of incongruent trials (that is, control demand) should encourage a higher LR in the prediction of conflict, as compared with the stable condition. Note that the overall incidence of congruent and incongruent trials was equal (0.5) across stable and volatile runs (see Methods).


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)

Experimental task and simulation and behavioural results (N=21).(a) Example stimuli and timing of presentation. This example depicts an incongruent trial, followed by a congruent trial. (b) Individual mean model LRs, plotted as a function of run type. Each line represents a subject. (c) The time course of group mean LR and s.e.m. in the first and last 10 trials of volatile (in blue) and stable (in red) blocks. Note that in this graph, which averages over all blocks, the difference in LR at the beginning of the blocks was driven by volatile blocks 2–4 in the volatile runs, as in these blocks the LR had already been raised by preceding volatile blocks. (d) Time courses of the underlying proportion congruency (in black) and the corresponding predicted conflict level (in red) of an example run. (e) Individual RS, plotted as a function of congruency. Each line represents one subject. Note that higher RT equals lower RS. (Con=congruent trials; Inc=incongruent trials). (f) Group mean RS and s.e.m., centred across trials for each subject, plotted as a function of unsigned prediction error of congruency (control prediction error).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Experimental task and simulation and behavioural results (N=21).(a) Example stimuli and timing of presentation. This example depicts an incongruent trial, followed by a congruent trial. (b) Individual mean model LRs, plotted as a function of run type. Each line represents a subject. (c) The time course of group mean LR and s.e.m. in the first and last 10 trials of volatile (in blue) and stable (in red) blocks. Note that in this graph, which averages over all blocks, the difference in LR at the beginning of the blocks was driven by volatile blocks 2–4 in the volatile runs, as in these blocks the LR had already been raised by preceding volatile blocks. (d) Time courses of the underlying proportion congruency (in black) and the corresponding predicted conflict level (in red) of an example run. (e) Individual RS, plotted as a function of congruency. Each line represents one subject. Note that higher RT equals lower RS. (Con=congruent trials; Inc=incongruent trials). (f) Group mean RS and s.e.m., centred across trials for each subject, plotted as a function of unsigned prediction error of congruency (control prediction error).
Mentions: fMRI data were acquired while subjects (N=21) performed a face-word Stroop conflict task61018, in which they responded to the gender of face images via button-presses and tried to disregard overlaid gender word labels (Fig. 2a), which could be either congruent (for example, ‘male' superimposed on a male face) or incongruent (for example, ‘female' superimposed on a male face). To investigate the flexible adjustment of cognitive control, we furthermore varied the relative dependence on short-term versus long-term trial history required for achieving optimal prediction of control demand. In the volatile control demand condition, the trial sequence sampled alternately from distributions with a 20 or 80% proportion of incongruent trials, switching every 20 trials over a run of 80 trials. Conversely, in the stable control demand condition, the underlying proportion of incongruent trials remained unchanged (either 20 or 80%) for a run of 80 trials. In the volatile condition, more frequent change of the proportion of incongruent trials (that is, control demand) should encourage a higher LR in the prediction of conflict, as compared with the stable condition. Note that the overall incidence of congruent and incongruent trials was equal (0.5) across stable and volatile runs (see Methods).

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.