TY  - JOUR
A1  - Rabovsky, Milena
T1  - Change in a probabilistic representation of meaning can account for N400 effects on articles
BT  - a neural network model
JF  - Neuropsychologia : an international journal in behavioural and cognitive neuroscience
N2  - Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.
KW  - N400
KW  - ERPs
KW  - prediction
KW  - neural networks
KW  - cue validity
KW  - meaning
Y1  - 2020
U6  - https://doi.org/10.1016/j.neuropsychologia.2020.107466
SN  - 0028-3932
SN  - 1873-3514
VL  - 143
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Rabovsky, Milena
T1  - Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model
JF  - Neuropsychologia
N2  - Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.
KW  - N400
KW  - ERPs
KW  - prediction
KW  - neural networks
KW  - cue validity
KW  - meaning
Y1  - 2019
VL  - 143
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - GEN
A1  - Rabovsky, Milena
T1  - Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model
T2  - Postprints der Universität Potsdam : Humanwissenschaftliche Reihe
N2  - Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.
T3  - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 731 
KW  - N400
KW  - ERPs
KW  - prediction
KW  - neural networks
KW  - cue validity
KW  - meaning
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-526988
SN  - 1866-8364
ER  -