TY - GEN A1 - Dambacher, Michael A1 - Kliegl, Reinhold T1 - Synchronizing timelines: Relations between fixation durations and N400 amplitudes during sentence reading N2 - We examined relations between eye movements (single-fixation durations) and RSVP-based event-related potentials (ERPs; N400’s) recorded during reading the same sentences in two independent experiments. Longer fixation durations correlated with larger N400 amplitudes. Word frequency and predictability of the fixated word as well as the predictability of the upcoming word accounted for this covariance in a path-analytic model. Moreover, larger N400 amplitudes entailed longer fixation durations on the next word, a relation accounted for by word frequency. This pattern offers a neurophysiological correlate for the lag-word frequency effect on fixation durations: Word processing is reliably expressed not only in fixation durations on currently fixated words, but also in those on subsequently fixated words. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - paper 262 KW - sentence reading KW - eye-movements KW - fixation durations KW - rapid serial visual presentation (RSVP) KW - event-related potentials (ERP) KW - N400 KW - path analysis Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-57212 ER - TY - JOUR A1 - Lago, Sol A1 - Namyst, Anna A1 - Jäger, Lena Ann A1 - Lau, Ellen T1 - Antecedent access mechanisms in pronoun processing BT - evidence from the N400 JF - Language, cognition and neuroscience N2 - Previous cross-modal priming studies showed that lexical decisions to words after a pronoun were facilitated when these words were semantically related to the pronoun's antecedent. These studies suggested that semantic priming effectively measured antecedent retrieval during coreference. We examined whether these effects extended to implicit reading comprehension using the N400 response. The results of three experiments did not yield strong evidence of semantic facilitation due to coreference. Further, the comparison with two additional experiments showed that N400 facilitation effects were reduced in sentences (vs. word pair paradigms) and were modulated by the case morphology of the prime word. We propose that priming effects in cross-modal experiments may have resulted from task-related strategies. More generally, the impact of sentence context and morphological information on priming effects suggests that they may depend on the extent to which the upcoming input is predicted, rather than automatic spreading activation between semantically related words. KW - Coreference KW - semantic priming KW - event-related potentials KW - sentence comprehension KW - N400 Y1 - 2019 U6 - https://doi.org/10.1080/23273798.2019.1566561 SN - 2327-3798 SN - 2327-3801 VL - 34 IS - 5 SP - 641 EP - 661 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - 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 - TY - JOUR A1 - Rabovsky, Milena A1 - McClelland, James L. T1 - Quasi-compositional mapping from form to meaning BT - a neural network-based approach to capturing neural responses during human language comprehension JF - Philosophical transactions of the Royal Society of London : B, Biological sciences N2 - We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of human language processing should explain both the outcome of the comprehension process and the continuous internal processes underlying this performance. These points motivate our discussion of a neural network model of sentence comprehension, the Sentence Gestalt model, which we have used to account for the N400 component of the event-related brain potential (ERP), which tracks meaning processing as it happens in real time. The model, which shares features with recent deep learning-based language models, simulates N400 amplitude as the automatic update of a probabilistic representation of the situation or event described by the sentence, corresponding to a temporal difference learning signal at the level of meaning. We suggest that this process happens relatively automatically, and that sometimes a more-controlled attention-dependent process is necessary for successful comprehension, which may be reflected in the subsequent P600 ERP component. We relate this account to current deep learning models as well as classic linguistic theory, and use it to illustrate a domain general perspective on some specific linguistic operations postulated based on compositional analyses of natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'. KW - language KW - meaning KW - event-related brain potentials KW - neural networks KW - N400 KW - P600 Y1 - 2019 U6 - https://doi.org/10.1098/rstb.2019.0313 SN - 0962-8436 SN - 1471-2970 SN - 0080-4622 VL - 375 IS - 1791 PB - Royal Society CY - London ER - TY - GEN A1 - Stone, Kate A1 - Nicenboim, Bruno A1 - Vasishth, Shravan A1 - Rösler, Frank T1 - Understanding the effects of constraint and predictability in ERP T2 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe N2 - Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 829 KW - N400 KW - anterior PNP KW - posterior P600 KW - probabilistic processing KW - constraint KW - predictability KW - entropy Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-587594 SN - 1866-8364 IS - 829 ER - TY - JOUR A1 - Stone, Kate A1 - Nicenboim, Bruno A1 - Vasishth, Shravan A1 - Rösler, Frank T1 - Understanding the effects of constraint and predictability in ERP JF - Neurobiology of language N2 - Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size. KW - N400 KW - anterior PNP KW - posterior P600 KW - probabilistic processing KW - constraint KW - predictability KW - entropy Y1 - 2022 U6 - https://doi.org/10.1162/nol_a_00094 SN - 2641-4368 VL - 4 IS - 2 SP - 221 EP - 256 PB - MIT Press CY - Cambridge, MA, USA ER -