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Modeling ERPs

Combining electrophysiology and cognitive computational modeling in research on meaning in language

Total Cost €

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EC-Contrib. €

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Partnership

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Project "Modeling ERPs" data sheet

The following table provides information about the project.

Coordinator
FREIE UNIVERSITAET BERLIN 

Organization address
address: KAISERSWERTHER STRASSE 16-18
city: BERLIN
postcode: 14195
website: www.fu-berlin.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website https://www.researchgate.net/profile/Milena_Rabovsky
 Total cost 159˙795 €
 EC max contribution 159˙795 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-GF
 Starting year 2015
 Duration (year-month-day) from 2015-08-01   to  2017-07-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FREIE UNIVERSITAET BERLIN DE (BERLIN) coordinator 159˙795.00
2    BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY US (STANFORD) partner 0.00

Map

 Project objective

Language and meaning processing have been investigated with event-related brain potentials (ERPs), providing direct time-resolved measures of electrical brain activity, and with connectionist network models, providing mechanistic implementations of the assumed processes. However, there has been very little contact between these fields, even though a combination of both methods could be highly beneficial. Based on initial evidence from the applicant, the present project therefore aims to integrate ERPs and computational models in research on language and meaning. Specifically, the N400 ERP component is widely used in research on language and meaning. As the computational mechanisms underlying this component are still unclear, we recently related the N400 to a model of word meaning and observed a close correspondence between N400 amplitudes and semantic network error. As network error is often conceptualized as implicit prediction error, we took these results to indicate that N400 amplitudes may reflect implicit prediction error in the semantic system. However, because the most typical N400 effects are observed during sentence processing, I propose to extend connectionist N400 simulations to sentence processing (Objective 1). Furthermore, the development of syntactic and semantic knowledge in the model should be related to the development of syntactic and semantic ERP components, both in developmental time and when processing words in sentences over time (Objective 2). Next, we aim to test behavioral predictions derived from this model-based account of N400 amplitudes, namely that larger N400 amplitudes should enhance implicit memory formation (Objective 3). Finally, the model-based account of N400 amplitudes as reflecting implicit prediction error should be tested in a conceptually similar theoretical framework, namely the Bayesian brain hypothesis. Thus, we will model N400 amplitudes as Bayesian surprise in the semantic system (Objective 4).

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