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. 2008 Sep;2(3):229-55.
doi: 10.1007/s11571-008-9041-5. Epub 2008 Apr 29.

Towards dynamical system models of language-related brain potentials

Peter Beim Graben  1 , Sabrina Gerth, Shravan Vasishth
Affiliations

Towards dynamical system models of language-related brain potentials

Peter Beim Graben et al. Cogn Neurodyn. 2008 Sep.

Abstract

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

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Figures

Fig. 1
Fig. 1
GB phrase structure trees. (a) For the formula image sentence (33). (b) For the formula image sentence (34)
Fig. 2
Fig. 2
Event-related brain potentials for the formula image (blue) and formula image (red) sentences ((33 and 34), respectively) at the parieto-central electrode Pz. (a) ERP voltage averages. (b) Event-related cylinder entropies obtained from a half-wave symbolic encoding with parameters T1 = 70ms, T2 = 10ms, and l = 8ms. Waveforms are digitally low-pass filtered with a cut-off frequency of 10Hz for better visibility. The disambiguating words der versus den appeared at t0 = 0s. Language-related ERPs, N400 and P600, are indicated by arrows
Fig. 3
Fig. 3
Return plot of the first principal component, PC#1, of the tensor product top-down recognizer processing the formula image (blue) and formula image (red) sentences (33 and 34), respectively
Fig. 4
Fig. 4
Time series of the first principal component of the tensor product top-down recognizer in phase space processing the formula image (blue) and formula image (red) sentences (33 and 34), respectively. The model “P600” is indicated by the arrow
Fig. 5
Fig. 5
Phase space dynamics of input-driven nonlinear automaton processing the formula image sentence (33). Each layer displays one iteration of the NDA top-down recognizer. The initial condition is indicated by “*”; non-autonomous formula image operations are indicated by “°”
Fig. 6
Fig. 6
Phase space dynamics of input-driven nonlinear automaton processing the formula image sentence (34). Here, only one non-autonomous formula image operation is indicated by “°”. The diagnosis and repair transitions are indicated by “*”. (a) Global view. (b) Enlargement
Fig. 7
Fig. 7
Measurement partition of the NDA phase space. Grid density is shown logarithmically
Fig. 8
Fig. 8
Time series of the cylinder entropy of the NDA top-down recognizer processing the formula image (blue) and formula image (red) sentences (33 and 34), respectively. The model “N400’s and the “P600” are indicated by arrows
See this image and copyright information in PMC

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