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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.


(A) Off system activity in RB subjects. In the RB participant group in Experiment 2, trials were marked as correct or incorrect according to the predictions of the II system when PINNACLE predicted the RB system was active. Activity within the caudate ROI is significantly active in RB participants during II trials when the II system was not selected. These posterior caudate regions are consistent with the hypothesized “II network”, so the observation of activity here suggests that the II system is operating simultaneously with the RB system in these participants; (B) Accuracy of the off-system in RB and II subjects. The correct and incorrect designations are based on the off-system’s predictions, not the overt feedbackto given the subject.
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brainsci-02-00176-f006: (A) Off system activity in RB subjects. In the RB participant group in Experiment 2, trials were marked as correct or incorrect according to the predictions of the II system when PINNACLE predicted the RB system was active. Activity within the caudate ROI is significantly active in RB participants during II trials when the II system was not selected. These posterior caudate regions are consistent with the hypothesized “II network”, so the observation of activity here suggests that the II system is operating simultaneously with the RB system in these participants; (B) Accuracy of the off-system in RB and II subjects. The correct and incorrect designations are based on the off-system’s predictions, not the overt feedbackto given the subject.

Mentions: By analyzing activity in brain regions associated with the off system, we hypothesized that it might be possible to identify neural activity associated with this otherwise covert process (off system learning). In Dataset 1, there was no evidence of off-system activity either in the MTL or the caudate for II or RB subjects, respectively. However, in Dataset 2, perhaps due to the higher variability in the category stimulus space, there was evidence of off-system II activity in the caudate ROI for RB subjects (Figure 6A) based on contrasting successful and unsuccessful category predictions of the II module. Note that there is no external measure of II predictions other than our estimates provided by fitting PINNACLE to the behavioral data. The accuracy of the off-system responses for Dataset 2 is shown in Figure 6B. The off-system was more accurate when it was associated with the appropriate participant group. That is, in RB subjects when the RB system was the off-system, those trials were more accurate than when the II system was the off-system. The converse was true for the II subjects. This pattern suggests that learning in the off-system leads to better performance when the off-system is the eventual optimal system.


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

Nomura EM, Reber PJ - Brain Sci (2012)

(A) Off system activity in RB subjects. In the RB participant group in Experiment 2, trials were marked as correct or incorrect according to the predictions of the II system when PINNACLE predicted the RB system was active. Activity within the caudate ROI is significantly active in RB participants during II trials when the II system was not selected. These posterior caudate regions are consistent with the hypothesized “II network”, so the observation of activity here suggests that the II system is operating simultaneously with the RB system in these participants; (B) Accuracy of the off-system in RB and II subjects. The correct and incorrect designations are based on the off-system’s predictions, not the overt feedbackto given the subject.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

brainsci-02-00176-f006: (A) Off system activity in RB subjects. In the RB participant group in Experiment 2, trials were marked as correct or incorrect according to the predictions of the II system when PINNACLE predicted the RB system was active. Activity within the caudate ROI is significantly active in RB participants during II trials when the II system was not selected. These posterior caudate regions are consistent with the hypothesized “II network”, so the observation of activity here suggests that the II system is operating simultaneously with the RB system in these participants; (B) Accuracy of the off-system in RB and II subjects. The correct and incorrect designations are based on the off-system’s predictions, not the overt feedbackto given the subject.
Mentions: By analyzing activity in brain regions associated with the off system, we hypothesized that it might be possible to identify neural activity associated with this otherwise covert process (off system learning). In Dataset 1, there was no evidence of off-system activity either in the MTL or the caudate for II or RB subjects, respectively. However, in Dataset 2, perhaps due to the higher variability in the category stimulus space, there was evidence of off-system II activity in the caudate ROI for RB subjects (Figure 6A) based on contrasting successful and unsuccessful category predictions of the II module. Note that there is no external measure of II predictions other than our estimates provided by fitting PINNACLE to the behavioral data. The accuracy of the off-system responses for Dataset 2 is shown in Figure 6B. The off-system was more accurate when it was associated with the appropriate participant group. That is, in RB subjects when the RB system was the off-system, those trials were more accurate than when the II system was the off-system. The converse was true for the II subjects. This pattern suggests that learning in the off-system leads to better performance when the off-system is the eventual optimal system.

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.