TY - JOUR
A1 - Vasishth, Shravan
A1 - Nicenboim, Bruno
A1 - Engelmann, Felix
A1 - Burchert, Frank
T1 - Computational Models of Retrieval Processes in Sentence Processing
JF - Trends in Cognitive Sciences
N2 - Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.
Y1 - 2019
U6 - https://doi.org/10.1016/j.tics.2019.09.003
SN - 1364-6613
SN - 1879-307X
VL - 23
IS - 11
SP - 968
EP - 982
PB - Elsevier
CY - London
ER -
TY - JOUR
A1 - Nicenboim, Bruno
A1 - Vasishth, Shravan
A1 - Rösler, Frank
T1 - Are words pre-activated probabilistically during sentence comprehension?
BT - evidence from new data and a Bayesian random-effects meta-analysis using publicly available data
JF - Neuropsychologia : an international journal in behavioural and cognitive neuroscience
N2 - Several studies (e.g., Wicha et al., 2003b; DeLong et al., 2005) have shown that readers use information from the sentential context to predict nouns (or some of their features), and that predictability effects can be inferred from the EEG signal in determiners or adjectives appearing before the predicted noun. While these findings provide evidence for the pre-activation proposal, recent replication attempts together with inconsistencies in the results from the literature cast doubt on the robustness of this phenomenon. Our study presents the first attempt to use the effect of gender on predictability in German to study the pre-activation hypothesis, capitalizing on the fact that all German nouns have a gender and that their preceding determiners can show an unambiguous gender marking when the noun phrase has accusative case. Despite having a relatively large sample size (of 120 subjects), both our preregistered and exploratory analyses failed to yield conclusive evidence for or against an effect of pre-activation. The sign of the effect is, however, in the expected direction: the more unexpected the gender of the determiner, the larger the negativity. The recent, inconclusive replication attempts by Nieuwland et al. (2018) and others also show effects with signs in the expected direction. We conducted a Bayesian random-ef-fects meta-analysis using our data and the publicly available data from these recent replication attempts. Our meta-analysis shows a relatively clear but very small effect that is consistent with the pre-activation account and demonstrates a very important advantage of the Bayesian data analysis methodology: we can incrementally accumulate evidence to obtain increasingly precise estimates of the effect of interest.
KW - ERP
KW - pre-activation
KW - predictions
KW - grammatical gender
KW - Bayesian meta-analysis
Y1 - 2020
U6 - https://doi.org/10.1016/j.neuropsychologia.2020.107427
SN - 0028-3932
SN - 1873-3514
VL - 142
PB - Elsevier Science
CY - Oxford
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 - Albert, Aviad
A1 - Nicenboim, Bruno
T1 - Modeling sonority in terms of pitch intelligibility with the nucleus attraction principle
JF - Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society
N2 - Sonority is a fundamental notion in phonetics and phonology, central to many descriptions of the syllable and various useful predictions in phonotactics. Although widely accepted, sonority lacks a clear basis in speech articulation or perception, given that traditional formal principles in linguistic theory are often exclusively based on discrete units in symbolic representation and are typically not designed to be compatible with auditory perception, sensorimotor control, or general cognitive capacities. In addition, traditional sonority principles also exhibit systematic gaps in empirical coverage. Against this backdrop, we propose the incorporation of symbol-based and signal-based models to adequately account for sonority in a complementary manner. We claim that sonority is primarily a perceptual phenomenon related to pitch, driving the optimization of syllables as pitch-bearing units in all language systems. We suggest a measurable acoustic correlate for sonority in terms of periodic energy, and we provide a novel principle that can account for syllabic well-formedness, the nucleus attraction principle (NAP). We present perception experiments that test our two NAP-based models against four traditional sonority models, and we use a Bayesian data analysis approach to test and compare them. Our symbolic NAP model outperforms all the other models we test, while our continuous bottom-up NAP model is at second place, along with the best performing traditional models. We interpret the results as providing strong support for our proposals: (i) the designation of periodic energy as the acoustic correlate of sonority; (ii) the incorporation of continuous entities in phonological models of perception; and (iii) the dual-model strategy that separately analyzes symbol-based top-down processes and signal-based bottom-up processes in speech perception.
KW - Sonority
KW - Pitch intelligibility
KW - Periodic energy
KW - Bayesian data
KW - analysis
KW - Speech perception
KW - Phonetics and phonology
Y1 - 2022
U6 - https://doi.org/10.1111/cogs.13161
SN - 0364-0213
SN - 1551-6709
VL - 46
IS - 7
PB - Wiley
CY - Hoboken
ER -
TY - JOUR
A1 - Schad, Daniel
A1 - Nicenboim, Bruno
A1 - Bürkner, Paul-Christian
A1 - Betancourt, Michael
A1 - Vasishth, Shravan
T1 - Workflow techniques for the robust use of bayes factors
JF - Psychological methods
N2 - Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Hem, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. Reproducible code is available from haps://osf.io/y354c/.
Translational Abstract
In psychology and related areas, scientific hypotheses are commonly tested by asking questions like "is [some] effect present or absent." Such hypothesis testing is most often carried out using frequentist null hypothesis significance testing (NIIST). The NHST procedure is very simple: It usually returns a p-value, which is then used to make binary decisions like "the effect is present/abscnt." For example, it is common to see studies in the media that draw simplistic conclusions like "coffee causes cancer," or "coffee reduces the chances of geuing cancer." However, a powerful and more nuanced alternative approach exists: Bayes factors. Bayes factors have many advantages over NHST. However, for the complex statistical models that arc commonly used for data analysis today, computing Bayes factors is not at all a simple matter. In this article, we discuss the main complexities associated with computing Bayes factors. This is the first article to provide a detailed workflow for understanding and computing Bayes factors in complex statistical models. The article provides a statistically more nuanced way to think about hypothesis testing than the overly simplistic tendency to declare effects as being "present" or "absent".
KW - Bayes factors
KW - Bayesian model comparison
KW - prior
KW - posterior
KW - simulation-based calibration
Y1 - 2022
U6 - https://doi.org/10.1037/met0000472
SN - 1082-989X
SN - 1939-1463
VL - 28
IS - 6
SP - 1404
EP - 1426
PB - American Psychological Association
CY - Washington
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 -