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Dissociation of category-learning systems via brain potentials.

Morrison RG, Reber PJ, Bharani KL, Paller KA - Front Hum Neurosci (2015)

Bottom Line: Categorization accuracy was similar for the two distributions.Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the RB, but not the II condition.These results provide additional evidence for distinct brain mechanisms supporting RB vs. implicit II category learning and use.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Neuroscience Institute, Loyola University Chicago Chicago, IL, USA.

ABSTRACT
Behavioral, neuropsychological, and neuroimaging evidence has suggested that categories can often be learned via either an explicit rule-based (RB) mechanism critically dependent on medial temporal and prefrontal brain regions, or via an implicit information-integration (II) mechanism relying on the basal ganglia. In this study, participants viewed sine-wave gratings (Gabor patches) that varied on two dimensions and learned to categorize them via trial-by-trial feedback. Two different stimulus distributions were used; one was intended to encourage an explicit RB process and the other an implicit II process. We monitored brain activity with scalp electroencephalography (EEG) while each participant: (1) passively observed stimuli represented of both distributions; (2) categorized stimuli from one distribution, and, 1 week later; (3) categorized stimuli from the other distribution. Categorization accuracy was similar for the two distributions. Subtractions of Event-Related Potentials (ERPs) for correct and incorrect trials were used to identify neural differences in RB and II categorization processes. We identified an occipital brain potential that was differentially modulated by categorization condition accuracy at an early latency (150-250 ms), likely reflecting the degree of holistic processing. A stimulus-locked Late Positive Complex (LPC) associated with explicit memory updating was modulated by accuracy in the RB, but not the II task. Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the RB, but not the II condition. These results provide additional evidence for distinct brain mechanisms supporting RB vs. implicit II category learning and use.

No MeSH data available.


Related in: MedlinePlus

Behavioral results for Model-Conforming Group. (A) Accuracy and (B) RTs for participants included based on DBT model fits and included in the analysis of brain potentials. Error bars represent ±1 standard error of the mean.
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Figure 5: Behavioral results for Model-Conforming Group. (A) Accuracy and (B) RTs for participants included based on DBT model fits and included in the analysis of brain potentials. Error bars represent ±1 standard error of the mean.

Mentions: To evaluate potential differences in category-learning accuracy for the RB and II distributions, we ran a 2 (RB vs. II) by 4 (block) repeated-measures ANOVA. Accuracy for RB and II distributions (Figure 5A) did not reliably differ (F(1,11) = 1.6, p = 0.23, ηp2 = 0.13). There was a main effect of block (F(3,33) = 24, p < 0.001, ηp2 = 0.69), and category learning linearly increased over blocks (F(1,11) = 50, p < 0.001, ηp2 = 0.81). However, RB and II distributions did not differ with respect to this pattern (F(1,11) = 0.4, p = 0.5, ηp2 = 0.04). Thus, observed differences in correct/incorrect ERP subtractions (described below) cannot easily be attributed to differences in accuracy between RB and II learning.


Dissociation of category-learning systems via brain potentials.

Morrison RG, Reber PJ, Bharani KL, Paller KA - Front Hum Neurosci (2015)

Behavioral results for Model-Conforming Group. (A) Accuracy and (B) RTs for participants included based on DBT model fits and included in the analysis of brain potentials. Error bars represent ±1 standard error of the mean.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493768&req=5

Figure 5: Behavioral results for Model-Conforming Group. (A) Accuracy and (B) RTs for participants included based on DBT model fits and included in the analysis of brain potentials. Error bars represent ±1 standard error of the mean.
Mentions: To evaluate potential differences in category-learning accuracy for the RB and II distributions, we ran a 2 (RB vs. II) by 4 (block) repeated-measures ANOVA. Accuracy for RB and II distributions (Figure 5A) did not reliably differ (F(1,11) = 1.6, p = 0.23, ηp2 = 0.13). There was a main effect of block (F(3,33) = 24, p < 0.001, ηp2 = 0.69), and category learning linearly increased over blocks (F(1,11) = 50, p < 0.001, ηp2 = 0.81). However, RB and II distributions did not differ with respect to this pattern (F(1,11) = 0.4, p = 0.5, ηp2 = 0.04). Thus, observed differences in correct/incorrect ERP subtractions (described below) cannot easily be attributed to differences in accuracy between RB and II learning.

Bottom Line: Categorization accuracy was similar for the two distributions.Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the RB, but not the II condition.These results provide additional evidence for distinct brain mechanisms supporting RB vs. implicit II category learning and use.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Neuroscience Institute, Loyola University Chicago Chicago, IL, USA.

ABSTRACT
Behavioral, neuropsychological, and neuroimaging evidence has suggested that categories can often be learned via either an explicit rule-based (RB) mechanism critically dependent on medial temporal and prefrontal brain regions, or via an implicit information-integration (II) mechanism relying on the basal ganglia. In this study, participants viewed sine-wave gratings (Gabor patches) that varied on two dimensions and learned to categorize them via trial-by-trial feedback. Two different stimulus distributions were used; one was intended to encourage an explicit RB process and the other an implicit II process. We monitored brain activity with scalp electroencephalography (EEG) while each participant: (1) passively observed stimuli represented of both distributions; (2) categorized stimuli from one distribution, and, 1 week later; (3) categorized stimuli from the other distribution. Categorization accuracy was similar for the two distributions. Subtractions of Event-Related Potentials (ERPs) for correct and incorrect trials were used to identify neural differences in RB and II categorization processes. We identified an occipital brain potential that was differentially modulated by categorization condition accuracy at an early latency (150-250 ms), likely reflecting the degree of holistic processing. A stimulus-locked Late Positive Complex (LPC) associated with explicit memory updating was modulated by accuracy in the RB, but not the II task. Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the RB, but not the II condition. These results provide additional evidence for distinct brain mechanisms supporting RB vs. implicit II category learning and use.

No MeSH data available.


Related in: MedlinePlus