Limits...
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.


Rule-based (RB) and information-integration (II) category distributions used in the study. Sine-wave gratings varied based on spatial frequency and spatial orientation. (A) The RB category was defined based on frequency whereas orientation varied unsystematically. (B) The II category was defined based on both frequency and orientation with a diagonal decision bound.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493768&req=5

Figure 1: Rule-based (RB) and information-integration (II) category distributions used in the study. Sine-wave gratings varied based on spatial frequency and spatial orientation. (A) The RB category was defined based on frequency whereas orientation varied unsystematically. (B) The II category was defined based on both frequency and orientation with a diagonal decision bound.

Mentions: A prominent way to characterize category-learning systems postulates distinct rule-based (RB) and information-integration (II) categorization strategies that engage different neurocognitive networks (see Ashby and Maddox, 2011). Within this framework, Maddox et al. (2003) have developed a feedback category-learning paradigm which parametrically varies the perceptual properties of sine-wave gratings (Gabor patches) to create category distributions that encourage either RB or II category learning strategies (see Figure 1).


Dissociation of category-learning systems via brain potentials.

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

Rule-based (RB) and information-integration (II) category distributions used in the study. Sine-wave gratings varied based on spatial frequency and spatial orientation. (A) The RB category was defined based on frequency whereas orientation varied unsystematically. (B) The II category was defined based on both frequency and orientation with a diagonal decision bound.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Rule-based (RB) and information-integration (II) category distributions used in the study. Sine-wave gratings varied based on spatial frequency and spatial orientation. (A) The RB category was defined based on frequency whereas orientation varied unsystematically. (B) The II category was defined based on both frequency and orientation with a diagonal decision bound.
Mentions: A prominent way to characterize category-learning systems postulates distinct rule-based (RB) and information-integration (II) categorization strategies that engage different neurocognitive networks (see Ashby and Maddox, 2011). Within this framework, Maddox et al. (2003) have developed a feedback category-learning paradigm which parametrically varies the perceptual properties of sine-wave gratings (Gabor patches) to create category distributions that encourage either RB or II category learning strategies (see Figure 1).

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.