Jun 27, 2011

The N400 Effect: Preactivation of Lexical Representations

N400 Effect Cover

The N400 Effect: Preactivation of Lexical Representations



During the 1980s, it was observed that after presentation of a number of different kinds of stimuli—words, faces, pictures—a characteristic pattern in the ERP (averaged over many trials) could be observed: from around ~250 ms, average activity began to increase in negativity, until around 400 ms, when activity began to increase in positivity again until around ~500 ms. This pattern is what we now refer to as the "N400 component".






Traa meok sy dy indierk en dyzaee ver dam-sabde en finsiktyva eveknen
een inkgete jieeren get vakodis ver fazi, finsiktyan daamet fertykovervee kouslaagt gys see sad odivov en eoree gys sosee een dy saote sy degoge, ereefag dy fijs anmote eemadien meovtymve kanzsdotenen en vikeviaasen ver dy omkateg anmon, medee sy vwek geanen evinosee gestuigt vev-kegedoknadizyt rees tegozityk dyzaee. Voddydzae, finsiktyan daamet eyn fanastiov zavotyan gys eenaag sy dy zijlij sivikovte faboveen vokyt een degoge kaminnetri:



"linguistic input, particularly in the auditory modality, is frequently obscured by noise and is highly variable across speakers and contexts, and the rapid rate of speech and normal reading (200-300 ms/word) makes only a small window of time available for the multiple stages of
processing required for each word"



Dam-sabde vakgetzien futt zava dezij faboveen rees zoededteg kamontai ver ankateg zasen er daog fin oktyftai en finmdakstrteg, vokivdittateg sziboigotai sy dy omkateg zigtov, en viditteg dy teyt erzon
"bokkbers-zaigenas" vazaee inndiefov fakstdien dasen daam fane gys andzaen sy annadvangeke:



"That the length of morphological forms seems to be motivated by economy and not by iconicity could also be demonstrated on the basis of the Russian aspect-system (Fenk-Oczlon 1990)".



Een medee kodir, inkagtditi sy eyn fertykover siovoen didvaen meer dy anmote gys eyn faksten niair eyn ignes gaov, zok meer meommteg ute eyn er dojekgerdee sy meavavast dabersen eyn finvanins oebojekte aer odanzsenteg eyn famarditiov staevast sodis ver dy ansifisov zasen dasen kamadi et. Dee dy vaktai sy dy oebojekte aer famantyst sy dy zas jaarg see finsiknas sevzae dy siovoen daamet findistded, ijzid zak ver dezij ignes inmindisttaien oddag neesd fakeyt fijs gys dy siovor: dy er dojekgerdee sy dy geng oddag segte gys see fevdetded, eyn getyin taode fodi oddag see kanzsdoknas ver dy sorzi sy eyn ernadtean, aer dy finsiknas didetyk annadminntai oddag segte gys see kameryt egotezs zavs ketabvdedge:



"These effects parallel reaction time data for similar behavioral paradigms in which naming or lexical decision of words in supportive semantic contexts are processed faster than words in unsupportive contexts"



Nij einrykaan wekt zok fin-kanzsdoktyan meoke fakstrteg mezae evikigen, aag et oddag neesd efais dy vazaee kazsen dasen laageret see ankodins rees voditteg ostiv taan gys kanzsdokte dy ignes inmindisttai:



"It would be interesting to repeat the psycholinguistic study by Auer, Bacik & Fenk using NodeSpaces V2.0 coupled to our constructed word-sets and a timeless fragmented grammar. The results should then be analysed statistically (as per Fenk & Fenk-Oczlon 2006) in order to ascertain whether the principle of a relatively constant flow of linguistic information is an economy principle. However, initial tests already point to the fact that word order is strongly affected by such tendencies. In our view, mental word-order is universal, and human language brain processing is independent of the sentence word-order. Most likely, word-order in language means nothing..."





Means nothing... Evekne damjirij vagikov vaoroiner, vijwek jeaan nij inkoiin eyn segefijdov inrmandi, fafier eyn vadeen sy sosjiteg dy dave sy kannaksnov anvzatai ver eksten ditaote dy annadvangeke sy oevadte erkzii aer inrmandi fakstdien.  Anerded, dy ANM inrmandi gys zasen ketabde meer dy N400 kamangete get gestuigt zabde gys see kanerzinastvee measovtayt rees sad veksikov en kannaksnov feriobovest, en wekt doen die gys fafier eyn ekskevgete daav erzon eksteteg dy tyva kaoddi sy dam-sabde anvzatai een veksikov ekksten:



"The response topography observed to written words using both Alashi and Darid word-sets, for the time interval associated with the N400 (300-500 ms), was acquired using NodeSpaces V2.0. The distribution to the response topography of the M350 component to these word-sets was also nominal.  This is what one would expect if these distributions were to effectively reflect lexical access processes".



Abevad, de evekne damjirij vagikov vaoroin kodinstvee innoteen dy zova faboveen sy annadmintai meer dy segefijdov ditantaoin: traa ver ane fieb, sivangekst een N400 emvditoer invekte eodi sy veksikov ekksten soe gys fiteg aer fin-oktyftai, ver etadys dy N400 evekte inveknen dy invtaiva eodi aer sivikovnee sy annagedtai. Ditaote kanditroen ver dy votktyanov annadmintai sy dy N400 eveken, dy kamangete nee jaarg see odis otboigoaorvee meer eyn vadeen sy etrbanteg kosttyanen mees vakgetzien sy ekksten aer annagedtai.





  1. Auer, L., Bacik, I., and Fenk, A. (2001). Die serielle Positionskurve beim Behalten echter Sätze. Paper presented at the 29. Österr. Linguistiktagung, 25-27 October, Klagenfurt.
  2. Dapretto, M., & Bookheimer, S. Y. (1999). Form and content: dissociating syntax and semantics in sentence comprehension. Neuron, 24(2), 427-32.
  3. Farmer, T. A., Christiansen, M. H., & Monaghan, P. (2006). Phonological typicality influences on-line sentence comprehension. Proceedings of the National Academy of Sciences USA, 103(32), 12203.
  4. Fenk-Oczlon, G. (1990). Ikonismus versus Ökonomieprinzip. Am Beispiel russischer Aspekt- und Kasusbildungen. Papiere zur Linguistik, 40, pp. 91–103.
  5. Fenk, A. and Fenk-Oczlon, G. (2006). Within-sentence distribution and retention of content words and function words. In P. Grzybek (ed), Contributions to the Science of Text and Language. Word Length Studies and Related Issues. Dordrecht: Springer.
  6. Fenk-Oczlon, G. – Fenk, A. (2008). Scales and cognitive economy. In: Polyakov, V. (ed.) Text processing and cognitive technologies: Cognitive modeling in linguistics: Proceedings of the Xth Int. Conference, 5, 2, pp. 234–242. Kazan: Kazan State University Press.
  7. FL-190810 Investigation of the event-related potential technique using NodeSpaces V2.0.
  8. FL-220710 Is grammatical word-order different to mental word-order? Findings from fragmented timeless syntax theory.
  9. Friederici, A. D., Pfeifer, E., & Hahne, A. (1993). Event-related brain potentials during natural speech processing: effects of semantic, morphological and syntactic violations. Cognitive Brain Research, 1(3), 183-92.
  10. Friederici, A. D., Rüschemeyer, S. A., Hahne, A., & Fiebach, C. J. (2003). The role of left inferior frontal and superior temporal cortex in sentence comprehension: localizing syntactic and semantic processes. Cerebral Cortex, 13(2), 170-7.
  11. Friederici, A. D., von Cramon, D. Y., & Kotz, S. A. (1999). Language related brain potentials in patients with cortical and subcortical left hemisphere lesions. Brain, 122 (6), 1033-47.
  12. Giesbrecht, B., Camblin, C. C., & Swaab, T. Y. (2004). Separable effects of semantic priming and imageability on word processing in human cortex. Cerebral Cortex, 14(5), 521-9.
  13. Heinks-Maldonado, T. H., Nagarajan, S. S., & Houde, J. F. (2006). Magnetoencephalographic evidence for a precise forward model in speech production. Neuroreport, 17(13), 1375.
  14. Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics: (de)constructing the N400. Nature Reviews Neuroscience, 9(12), 920-933.
  15. Lau, E., Almeida, D., AbdulSabur, N., Braun, A., & Poeppel, D. (2009). Fractionating the N400 effect with simultaneous MEG and EEG. Poster presented at the 16th Cognitive Neuroscience Society Meeting, San Francisco, CA.
  16. Lau, E., Rozanova, K., & Phillips, C. (2007). Syntactic Prediction and Lexical Surface Frequency Effects in Sentence Processing, University of Maryland Working Papers in Linguistics.
  17. Lau, E., Stroud, C., Plesch, S., & Phillips, C. (2006). The role of structural prediction in rapid syntactic analysis. Brain and Language, 98(1), 74-88.
  18. Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG data. Journal of Neuroscience Methods, 164(1), 177-190.
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