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Combining computational modeling and neuroimaging to examine multiple category learning systems in the brain.

Nomura EM, Reber PJ - Brain Sci (2012)

Bottom Line: The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli.Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback.The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity.

View Article: PubMed Central - PubMed

Affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA. eminomura@berkeley.edu.

ABSTRACT
Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the "off system" (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery.

No MeSH data available.


Correct trial activity for the best fitting RB versus best fitting II blocks. In Dataset 1 and Dataset 2, a region in right PFC is more active for best fitting RB blocks (red) and a region in right posterior visual association cortex is more active for best fitting II blocks (blue).
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brainsci-02-00176-f004: Correct trial activity for the best fitting RB versus best fitting II blocks. In Dataset 1 and Dataset 2, a region in right PFC is more active for best fitting RB blocks (red) and a region in right posterior visual association cortex is more active for best fitting II blocks (blue).

Mentions: The fit value for a given block of 80 trials amounts to the sum of the individual trial fits in that block. To identify the best examples of RB and II trial activity irrespective of the group the subject was assigned, we restricted the analysis to the best fitting blocks of data defined as the top third of all blocks. Within these blocks, a contrast of correct RB and II trials identified a number of regions of activity similar to those found with DBT-based modeling [18]. That is, trials in which PINNACLE predicted the RB system was successfully engaged in learning were compared to trials where the II system was successfully learning (Figure 4). A region in the right PFC (Dataset 1) and medial PFC (Dataset 2) was more active during correct RB than II trials. The opposite effect was observed in right posterior visual association cortex where activity was greater during correct II than RB trials (Datasets 1 and 2). The contrast of activity associated with correct RB and II trials should emphasize brain regions associated with effective expression of those strategies. In our previous work, the contrast between successful and unsuccessful trials found differential activity in the MTL and posterior caudate, which was not observed in the current success-only contrast (in best-fitting blocks, there are too few unsuccessful trials to examine this contrast). This may reflect covert activity in the “off system” (e.g., II system activity during RB trials) that weakens this difference and will be examined more directly below.


Combining computational modeling and neuroimaging to examine multiple category learning systems in the brain.

Nomura EM, Reber PJ - Brain Sci (2012)

Correct trial activity for the best fitting RB versus best fitting II blocks. In Dataset 1 and Dataset 2, a region in right PFC is more active for best fitting RB blocks (red) and a region in right posterior visual association cortex is more active for best fitting II blocks (blue).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061791&req=5

brainsci-02-00176-f004: Correct trial activity for the best fitting RB versus best fitting II blocks. In Dataset 1 and Dataset 2, a region in right PFC is more active for best fitting RB blocks (red) and a region in right posterior visual association cortex is more active for best fitting II blocks (blue).
Mentions: The fit value for a given block of 80 trials amounts to the sum of the individual trial fits in that block. To identify the best examples of RB and II trial activity irrespective of the group the subject was assigned, we restricted the analysis to the best fitting blocks of data defined as the top third of all blocks. Within these blocks, a contrast of correct RB and II trials identified a number of regions of activity similar to those found with DBT-based modeling [18]. That is, trials in which PINNACLE predicted the RB system was successfully engaged in learning were compared to trials where the II system was successfully learning (Figure 4). A region in the right PFC (Dataset 1) and medial PFC (Dataset 2) was more active during correct RB than II trials. The opposite effect was observed in right posterior visual association cortex where activity was greater during correct II than RB trials (Datasets 1 and 2). The contrast of activity associated with correct RB and II trials should emphasize brain regions associated with effective expression of those strategies. In our previous work, the contrast between successful and unsuccessful trials found differential activity in the MTL and posterior caudate, which was not observed in the current success-only contrast (in best-fitting blocks, there are too few unsuccessful trials to examine this contrast). This may reflect covert activity in the “off system” (e.g., II system activity during RB trials) that weakens this difference and will be examined more directly below.

Bottom Line: The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli.Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback.The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity.

View Article: PubMed Central - PubMed

Affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA. eminomura@berkeley.edu.

ABSTRACT
Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the "off system" (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery.

No MeSH data available.