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Working memory management and predicted utility.

Chatham CH, Badre D - Front Behav Neurosci (2013)

Bottom Line: An adapted Q-learning model indicated that these costs scaled with the historical utility of individual items.Finally, model-based neuroimaging demonstrated that frontal cortex tracked the utility of items to be maintained in WM, whereas ventral striatum tracked changes in the utility of items maintained in WM to the degree that these items are no longer useful.Our findings suggest that frontostriatal mechanisms track the utility of information in WM, and that these dynamics may predict delays in the removal of information from WM.

View Article: PubMed Central - PubMed

Affiliation: Cognitive, Linguistic & Psychological Sciences, Brown University, Providence RI, USA.

ABSTRACT
Given the limited capacity of working memory (WM), its resources should be allocated strategically. One strategy is filtering, whereby access to WM is granted preferentially to items with the greatest utility. However, reallocation of WM resources might be required if the utility of maintained information subsequently declines. Here, we present behavioral, computational, and neuroimaging evidence that human participants track changes in the predicted utility of information in WM. First, participants demonstrated behavioral costs when the utility of items already maintained in WM declined and resources should be reallocated. An adapted Q-learning model indicated that these costs scaled with the historical utility of individual items. Finally, model-based neuroimaging demonstrated that frontal cortex tracked the utility of items to be maintained in WM, whereas ventral striatum tracked changes in the utility of items maintained in WM to the degree that these items are no longer useful. Our findings suggest that frontostriatal mechanisms track the utility of information in WM, and that these dynamics may predict delays in the removal of information from WM.

No MeSH data available.


Related in: MedlinePlus

BOLD responses to selective vs. global context conditions (red-yellow) and common areas of recruitment (black outlines). Contrasts of RCI and ICR vs. RCR revealed reliable BOLD response across frontal, parietal, and occipital cortex (voxelwise z > 2.3, corrected to p < 0.05 via GRF). The reverse contrasts of RCR > ICR and RCR > RCI, and the direct contrasts of ICR with RCI, failed to reach significance.
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Figure 5: BOLD responses to selective vs. global context conditions (red-yellow) and common areas of recruitment (black outlines). Contrasts of RCI and ICR vs. RCR revealed reliable BOLD response across frontal, parietal, and occipital cortex (voxelwise z > 2.3, corrected to p < 0.05 via GRF). The reverse contrasts of RCR > ICR and RCR > RCI, and the direct contrasts of ICR with RCI, failed to reach significance.

Mentions: Direct comparisons of the BOLD response across conditions revealed widespread increases in BOLD at the onset of context in both the ICR and RCI conditions relative to the RCR condition (Figure 5, red-yellow regions), including within the IFS, pre-PMd, PMd, intraparietal sulcus, dorsomedial prefrontal cortex, right anterior insula, and left inferior frontal gyrus. Several of these regions were recruited across both the ICR and RCI conditions more strongly than in the RCR condition (Figure 5, black outlines), including right anterior insula, left IFS, pre-PMd, PMd and bilateral dorsomedial prefrontal cortex. Nonetheless, the only significant difference observed in a direct contrast of the ICR and RCI conditions was a small cluster of voxels in primary visual cortex, which showed only a modest increase in recruitment during the RCI than ICR condition. There were no indications of an increased response during the ICR vs. RCI anywhere in frontoparietal or striatal areas, even at a liberal threshold (p < 0.05 uncorrected).


Working memory management and predicted utility.

Chatham CH, Badre D - Front Behav Neurosci (2013)

BOLD responses to selective vs. global context conditions (red-yellow) and common areas of recruitment (black outlines). Contrasts of RCI and ICR vs. RCR revealed reliable BOLD response across frontal, parietal, and occipital cortex (voxelwise z > 2.3, corrected to p < 0.05 via GRF). The reverse contrasts of RCR > ICR and RCR > RCI, and the direct contrasts of ICR with RCI, failed to reach significance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3713341&req=5

Figure 5: BOLD responses to selective vs. global context conditions (red-yellow) and common areas of recruitment (black outlines). Contrasts of RCI and ICR vs. RCR revealed reliable BOLD response across frontal, parietal, and occipital cortex (voxelwise z > 2.3, corrected to p < 0.05 via GRF). The reverse contrasts of RCR > ICR and RCR > RCI, and the direct contrasts of ICR with RCI, failed to reach significance.
Mentions: Direct comparisons of the BOLD response across conditions revealed widespread increases in BOLD at the onset of context in both the ICR and RCI conditions relative to the RCR condition (Figure 5, red-yellow regions), including within the IFS, pre-PMd, PMd, intraparietal sulcus, dorsomedial prefrontal cortex, right anterior insula, and left inferior frontal gyrus. Several of these regions were recruited across both the ICR and RCI conditions more strongly than in the RCR condition (Figure 5, black outlines), including right anterior insula, left IFS, pre-PMd, PMd and bilateral dorsomedial prefrontal cortex. Nonetheless, the only significant difference observed in a direct contrast of the ICR and RCI conditions was a small cluster of voxels in primary visual cortex, which showed only a modest increase in recruitment during the RCI than ICR condition. There were no indications of an increased response during the ICR vs. RCI anywhere in frontoparietal or striatal areas, even at a liberal threshold (p < 0.05 uncorrected).

Bottom Line: An adapted Q-learning model indicated that these costs scaled with the historical utility of individual items.Finally, model-based neuroimaging demonstrated that frontal cortex tracked the utility of items to be maintained in WM, whereas ventral striatum tracked changes in the utility of items maintained in WM to the degree that these items are no longer useful.Our findings suggest that frontostriatal mechanisms track the utility of information in WM, and that these dynamics may predict delays in the removal of information from WM.

View Article: PubMed Central - PubMed

Affiliation: Cognitive, Linguistic & Psychological Sciences, Brown University, Providence RI, USA.

ABSTRACT
Given the limited capacity of working memory (WM), its resources should be allocated strategically. One strategy is filtering, whereby access to WM is granted preferentially to items with the greatest utility. However, reallocation of WM resources might be required if the utility of maintained information subsequently declines. Here, we present behavioral, computational, and neuroimaging evidence that human participants track changes in the predicted utility of information in WM. First, participants demonstrated behavioral costs when the utility of items already maintained in WM declined and resources should be reallocated. An adapted Q-learning model indicated that these costs scaled with the historical utility of individual items. Finally, model-based neuroimaging demonstrated that frontal cortex tracked the utility of items to be maintained in WM, whereas ventral striatum tracked changes in the utility of items maintained in WM to the degree that these items are no longer useful. Our findings suggest that frontostriatal mechanisms track the utility of information in WM, and that these dynamics may predict delays in the removal of information from WM.

No MeSH data available.


Related in: MedlinePlus