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This pilot study explores the links between cognitive activity modeled in ACT-R buffers and EEG activity decomposed inindependent components.Recent efforts to predict fMRI activation using cognitive modelshelped to spatially localize modeled brain functionality. EEGexcels in temporal resolution and may provide a more detailedpicture of cognitive activity at the millisecond scale. ACT-R is an architecture for cognitive models that give atemporally precise description of activity in buffers that havepreviously been localized using fMRI.For an attentional blink model, preliminary results support arelation between imaginal buffer activity and the P300.
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Linking cognitive models and Event Related Potentials
AbstractThis pilot study explores the links between cognitive activity modeled in ACT-R buffers and EEG activity decomposed in independent components.
Recent efforts to predict fMRI activation using cognitive models helped to spatially localize modeled brain functionality. EEG excels in temporal resolution and may provide a more detailed picture of cognitive activity at the millisecond scale.
ACT-R is an architecture for cognitive models that give a temporally precise description of activity in buffers that have previously been localized using fMRI.
For an attentional blink model, preliminary results support a relation between imaginal buffer activity and the P300.
Imaginal buffer and the P300● P300 and imaginal buffer activity are both target related
● Activity in the imaginal buffer is therefore previously linked to memory consolidation (Taatgen, 2009), as is the P300 (Martens, 2006)
● ERP results support a link between imaginal buffer activation and the P300
● ICA results indicate decomposition of the P300 into an early and a late component
● Latency of peaks in these components corresponds better with modeled activity then P300 deflections in ERP do
Fit on Behavioral Data
● Modified model to fit the presentation rate and the other experimental conditions (Wierda, 2010) in Martens (2006) accurately reproduces the blink effect for Lag 3
1Department of Artificial Intelligence, University of Groningen; 2Neuroimaging Center, University of Groningen
Virtual ERP's from a procedural model
● Buffer use in ACT-R takes a predefined amount of time. Activity is defined 1 when active (between event on- and offset) and 0 when not (left)
● Virtual ERP's are constructed by simply averaging activity in buffers over model runs for a specific condition (right)
ERP Results (non-blink trials) ICA Results (non-blink trials)
Comparison between components and virtual ERP
Blink Non-Blink
Independent Component Analysis (ICA)● EEG (Martens, 2006) is decomposed in components using
ICA for one of the non-blinking (NB) participants
● Four of the components that contribute most to the ERP under all four conditions (B, NB, Lag 3, Lag 8) were compared with the constructed virtual ERP's
The Attentional Blink Task● Activity from an existing model (Taatgen, 2009) is compared
with EEG activity from an existing study (Martens, 2006).
● Model and participants performed the same Rapid Serial Visual Presentation (RSPV) task testing the Attentional Blink (AB) paradigm
● During an AB task one or two targets are presented with varying inter-target delays (Lag). For small lags, blinking participants (B) often miss the second one, causing a dip in accuracy, while non-blinkers (NB) do not.
IC 5 IC 14
IC 7 IC 1
Weights
RESULTS
DISCUSSION
METHODS
MODEL AND EXPERIMENT
ReferencesMartens, S., Munneke, J., Smid, H. & Johnson, A. Quick Minds Don't Blink: Electrophysiological Correlates of Individual Differences in Attentional Selection. Journal of Cognitive Neuroscience 18, 1423-1438 (2006).
Taatgen, N.A., Juvina, I., Schipper, M., Borst, J.P. & Martens, S. Too much control can hurt: A threaded cognition model of the attentional blink. Cognitive Psychology 59, 1-29 (2009)
CONTACT [email protected]
Future work● Clustering of independent components (IC) is required for
direct comparison of modeled groups of participants
● Increasing accuracy of virtual ERP's by transforming activation on- and offsets rather then directly comparing them
● Better informed match between ERP components and virtual ERP's by guiding search, clustering or ICA, using features of modeled virtual ERP's
Cognitive Models and ERP● Results indicate that ICA can narrow the gap between model
predictions and ERP
● Construction of virtual ERP's from ACT-R buffer activations seems to be a promising tool for model evaluation
Lag
3La
g 8
Lag
3La
g 8
Human ERP measured at the Pz electrode (gray lines) and virtual ERP based on imaginal buffer activity (red lines) for the blink (dashed) and non-blink (solid) groups.
Contribution of selected components (shaded blue) to the overall ERP (dashed gray) for one of the non-blinking participants. Contribution of the two components compared to imaginal buffer activity are highlighted (red and blue).
Top Projection weights of the components to the scalp electrodes and approximate location of the ACT-R buffers that are compared with the components on the left ().
Left Average over all trials for the selected components (gray lines) compared with their best matching virtual ERP (red lines).
H. Prins1, J. Wierda1, S. Martens2, N. Taatgen1, R.A.J. van Elburg1
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