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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.


fMRI contrast of C vs. NC trial types. (A) For presentation, Dataset 1 was thresholded at t > 3.5 with a minimum cluster size of 300mm3. The peak coordinates of activity are (39, 21, 18) and (43, 1, 33); (B) For presentation, Dataset 2 was thresholded at t > 3 with a minimum cluster size of 800mm3 (DLPFC regions in both datasets are evident at more stringent t > 4.0 thresholds, the lower thresholds were used to show consistency across the replication). The peak coordinates of activity are (48, 3, 33) and (46, 33, 27). The consistent regions of activity across studies occur in the right DLPFC which we hypothesize corresponds to the operation of the Decision Module on these trials.
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brainsci-02-00176-f005: fMRI contrast of C vs. NC trial types. (A) For presentation, Dataset 1 was thresholded at t > 3.5 with a minimum cluster size of 300mm3. The peak coordinates of activity are (39, 21, 18) and (43, 1, 33); (B) For presentation, Dataset 2 was thresholded at t > 3 with a minimum cluster size of 800mm3 (DLPFC regions in both datasets are evident at more stringent t > 4.0 thresholds, the lower thresholds were used to show consistency across the replication). The peak coordinates of activity are (48, 3, 33) and (46, 33, 27). The consistent regions of activity across studies occur in the right DLPFC which we hypothesize corresponds to the operation of the Decision Module on these trials.

Mentions: Neural activity differences between the C and NC trials were assessed in both experiments and shown in Figure 5. Given that the Decision Module is hypothesized to be active on every trial, for each dataset, we restricted the analysis to a functionally defined ROI based on all cross-subject trial-evoked activity. This smaller volume allowed for greater sensitivity than is afforded when searching the entire brain for trial-evoked activity. The grouped functional ROI was used to mask each individual subject’s contrast of competition-related activation. The resulting t-test then used the masked functional dataset to isolate significant clusters of activity (t > 3.5, cluster > 300 mm3). The regions shown reflect brain regions where C trials evoked more neural activity than NC trials. While there were regions of reliable differential activity across experiments (Table 4), the right DLPFC and bilateral motor cortex was found to exhibit greater activity for C trials in both datasets. These brain regions reflect candidate areas for the neural basis of the Decision Module where competition between the two category systems is resolved and the consistent activity observed in DLPFC across datasets indicates this are is likely of particular importance to this process.


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

Nomura EM, Reber PJ - Brain Sci (2012)

fMRI contrast of C vs. NC trial types. (A) For presentation, Dataset 1 was thresholded at t > 3.5 with a minimum cluster size of 300mm3. The peak coordinates of activity are (39, 21, 18) and (43, 1, 33); (B) For presentation, Dataset 2 was thresholded at t > 3 with a minimum cluster size of 800mm3 (DLPFC regions in both datasets are evident at more stringent t > 4.0 thresholds, the lower thresholds were used to show consistency across the replication). The peak coordinates of activity are (48, 3, 33) and (46, 33, 27). The consistent regions of activity across studies occur in the right DLPFC which we hypothesize corresponds to the operation of the Decision Module on these trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

brainsci-02-00176-f005: fMRI contrast of C vs. NC trial types. (A) For presentation, Dataset 1 was thresholded at t > 3.5 with a minimum cluster size of 300mm3. The peak coordinates of activity are (39, 21, 18) and (43, 1, 33); (B) For presentation, Dataset 2 was thresholded at t > 3 with a minimum cluster size of 800mm3 (DLPFC regions in both datasets are evident at more stringent t > 4.0 thresholds, the lower thresholds were used to show consistency across the replication). The peak coordinates of activity are (48, 3, 33) and (46, 33, 27). The consistent regions of activity across studies occur in the right DLPFC which we hypothesize corresponds to the operation of the Decision Module on these trials.
Mentions: Neural activity differences between the C and NC trials were assessed in both experiments and shown in Figure 5. Given that the Decision Module is hypothesized to be active on every trial, for each dataset, we restricted the analysis to a functionally defined ROI based on all cross-subject trial-evoked activity. This smaller volume allowed for greater sensitivity than is afforded when searching the entire brain for trial-evoked activity. The grouped functional ROI was used to mask each individual subject’s contrast of competition-related activation. The resulting t-test then used the masked functional dataset to isolate significant clusters of activity (t > 3.5, cluster > 300 mm3). The regions shown reflect brain regions where C trials evoked more neural activity than NC trials. While there were regions of reliable differential activity across experiments (Table 4), the right DLPFC and bilateral motor cortex was found to exhibit greater activity for C trials in both datasets. These brain regions reflect candidate areas for the neural basis of the Decision Module where competition between the two category systems is resolved and the consistent activity observed in DLPFC across datasets indicates this are is likely of particular importance to this process.

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.