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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.


(A) Placement of 52 scalp electrodes (of 58 total) used in this study with respect to 10–20 landmarks. (B) Frontal (F), Central (C), Parietal (P), and Occipital (O) electrode clusters used for the analysis of the N1. (C) Parietal electrode cluster surrounding location Pz used in the analysis of the Late positive complex (LPC). (D) Central electrode cluster surrounding and including electrode Cz used in the analysis of the feedback P300.
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Figure 3: (A) Placement of 52 scalp electrodes (of 58 total) used in this study with respect to 10–20 landmarks. (B) Frontal (F), Central (C), Parietal (P), and Occipital (O) electrode clusters used for the analysis of the N1. (C) Parietal electrode cluster surrounding location Pz used in the analysis of the Late positive complex (LPC). (D) Central electrode cluster surrounding and including electrode Cz used in the analysis of the feedback P300.

Mentions: Continuous EEG recordings were made during prelearning and category-learning blocks from 59 evenly distributed scalp sites using tin electrodes embedded in an elastic cap (Figure 3). Four additional electrodes were used for monitoring horizontal and vertical eye movements, and two electrodes were placed over the left and right mastoid bones. Participants were instructed to attempt to refrain from blinking or moving their eye position from fixation during the categorization and feedback portions of each trial. Electrode impedance was ≤5 kΩ. EEG signals were amplified with a band pass of 0.05–200 Hz and sampled at a rate of 1000 Hz. The online reference (left mastoid) was changed to average mastoids offline and a 59 to 60 Hz band-stop filter was applied. EMSE Software Suite (Source Signal Imaging, San Diego, CA, USA) was used to process raw EEG files and to compute ERPs. Electrooculograph (EOG) artifacts were corrected by using a blink-correction algorithm based on independent component analysis. Averaging epochs for stimulus and feedback lasted 1200 ms, including a 200 ms pre-stimulus baseline. Trials showing a greater than 100 μV deflection during the epoch were discarded. Fewer than 15% of trials were excluded for any given condition for any given participant.


Dissociation of category-learning systems via brain potentials.

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

(A) Placement of 52 scalp electrodes (of 58 total) used in this study with respect to 10–20 landmarks. (B) Frontal (F), Central (C), Parietal (P), and Occipital (O) electrode clusters used for the analysis of the N1. (C) Parietal electrode cluster surrounding location Pz used in the analysis of the Late positive complex (LPC). (D) Central electrode cluster surrounding and including electrode Cz used in the analysis of the feedback P300.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: (A) Placement of 52 scalp electrodes (of 58 total) used in this study with respect to 10–20 landmarks. (B) Frontal (F), Central (C), Parietal (P), and Occipital (O) electrode clusters used for the analysis of the N1. (C) Parietal electrode cluster surrounding location Pz used in the analysis of the Late positive complex (LPC). (D) Central electrode cluster surrounding and including electrode Cz used in the analysis of the feedback P300.
Mentions: Continuous EEG recordings were made during prelearning and category-learning blocks from 59 evenly distributed scalp sites using tin electrodes embedded in an elastic cap (Figure 3). Four additional electrodes were used for monitoring horizontal and vertical eye movements, and two electrodes were placed over the left and right mastoid bones. Participants were instructed to attempt to refrain from blinking or moving their eye position from fixation during the categorization and feedback portions of each trial. Electrode impedance was ≤5 kΩ. EEG signals were amplified with a band pass of 0.05–200 Hz and sampled at a rate of 1000 Hz. The online reference (left mastoid) was changed to average mastoids offline and a 59 to 60 Hz band-stop filter was applied. EMSE Software Suite (Source Signal Imaging, San Diego, CA, USA) was used to process raw EEG files and to compute ERPs. Electrooculograph (EOG) artifacts were corrected by using a blink-correction algorithm based on independent component analysis. Averaging epochs for stimulus and feedback lasted 1200 ms, including a 200 ms pre-stimulus baseline. Trials showing a greater than 100 μV deflection during the epoch were discarded. Fewer than 15% of trials were excluded for any given condition for any given participant.

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.