Individual differences in cue-weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for processing speed and cue weighting using 13 published datasets; hierarchical Approximate Bayesian Computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the higher weighting for structural cues, suggesting that reading proficiency might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.

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Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing

MethodsX ◽

10.1016/j.mex.2020.100850 ◽

2020 ◽

Vol 7 ◽

pp. 100850

Author(s):

Shravan Vasishth

Keyword(s):

Sentence Processing ◽

Approximate Bayesian Computation ◽

Bayesian Computation ◽

Retrieval Model ◽

Approximate Bayesian

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Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis

Journal of Computational Physics ◽

10.1016/j.jcp.2020.109999 ◽

2020 ◽

pp. 109999

Author(s):

Agnieszka Borowska ◽

Diana Giurghita ◽

Dirk Husmeier

Keyword(s):

Differential Equation ◽

Stochastic Differential Equation ◽

Gaussian Process ◽

Approximate Bayesian Computation ◽

Equation System ◽

Bayesian Computation ◽

Differential Equation System ◽

Parameter Inference ◽

Approximate Bayesian

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Weighted approximate Bayesian computation via Sanov’s theorem

Computational Statistics ◽

10.1007/s00180-021-01093-4 ◽

2021 ◽

Author(s):

Cecilia Viscardi ◽

Michele Boreale ◽

Fabio Corradi

Keyword(s):

Large Deviations ◽

Posterior Distribution ◽

Approximate Bayesian Computation ◽

Bayesian Computation ◽

Information Theoretic ◽

Discrete Random Variables ◽

Positive Weights ◽

Approximate Bayesian ◽

Information Theoretic Method ◽

Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

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Inference of Brain Networks with Approximate Bayesian Computation – assessing face validity with an example application in Parkinsonism

NeuroImage ◽

10.1016/j.neuroimage.2021.118020 ◽

2021 ◽

pp. 118020

Author(s):

Timothy O. West ◽

Luc Berthouze ◽

Simon F. Farmer ◽

Hayriye Cagnan ◽

Vladimir Litvak

Keyword(s):

Approximate Bayesian Computation ◽

Brain Networks ◽

Face Validity ◽

Bayesian Computation ◽

Approximate Bayesian

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Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

Experiments in Fluids ◽

10.1007/s00348-020-03122-2 ◽

2021 ◽

Vol 62 (2) ◽

Author(s):

Jason D. Christopher ◽

Olga A. Doronina ◽

Dan Petrykowski ◽

Torrey R. S. Hayden ◽

Caelan Lapointe ◽

...

Keyword(s):

Parameter Estimation ◽

Absorption Spectroscopy ◽

Approximate Bayesian Computation ◽

Flow Parameter ◽

Bayesian Computation ◽

Laser Absorption Spectroscopy ◽

Laser Absorption ◽

Approximate Bayesian

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Approximate Bayesian Computation for Discrete Spaces

Entropy ◽

10.3390/e23030312 ◽

2021 ◽

Vol 23 (3) ◽

pp. 312

Author(s):

Ilze A. Auzina ◽

Jakub M. Tomczak

Keyword(s):

Approximate Bayesian Computation ◽

Likelihood Function ◽

Real Life ◽

Random Variables ◽

Bayesian Computation ◽

Inference Problem ◽

Markov Kernel ◽

Inference Problems ◽

Binary Neural Network ◽

Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

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