Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing
A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it...
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| Published in: | MethodsX Vol. 7; p. 100850 |
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| Abstract | A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified.
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| AbstractList | A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified. A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified. [Display omitted] A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified. Image, graphical abstract A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified.A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified. |
| ArticleNumber | 100850 |
| Author | Vasishth, Shravan |
| AuthorAffiliation | University of Potsdam , Germany |
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| References | Dillon, Mishler, Sloggett, Phillips (bib0003) 2013; 69 Lewis, Vasishth (bib0004) 2005; 29 Engelmann, Jäger, Vasishth (bib0002) 2019; 43 Vasishth, Nicenboim, Engelmann, Burchert (bib0008) 2019; 23 Sisson, Fan, Beaumont (bib0005) 2018 Jäger, Mertzen, Van Dyke, Vasishth (bib0001) 2020; 111 Palestro, Sederberg, Osth, Van Zandt, Turner (bib0006) 2018 Kangasrääsiö, Jokinen, Oulasvirta, Howes, Kaski (bib0007) 2019; 43 Sisson (10.1016/j.mex.2020.100850_bib0005) 2018 Engelmann (10.1016/j.mex.2020.100850_bib0002) 2019; 43 Palestro (10.1016/j.mex.2020.100850_bib0006) 2018 Kangasrääsiö (10.1016/j.mex.2020.100850_bib0007) 2019; 43 Jäger (10.1016/j.mex.2020.100850_bib0001) 2020; 111 Lewis (10.1016/j.mex.2020.100850_bib0004) 2005; 29 Dillon (10.1016/j.mex.2020.100850_bib0003) 2013; 69 Vasishth (10.1016/j.mex.2020.100850_bib0008) 2019; 23 |
| References_xml | – volume: 69 start-page: 85 year: 2013 end-page: 103 ident: bib0003 article-title: Contrasting intrusion profiles for agreement and anaphora: experimental and modeling evidence publication-title: J. Mem. Lang. – volume: 29 start-page: 375 year: 2005 end-page: 419 ident: bib0004 article-title: An activation‐based model of sentence processing as skilled memory retrieval publication-title: Cogn. Sci. – year: 2018 ident: bib0006 article-title: Likelihood-Free Methods For Cognitive Science – volume: 43 year: 2019 ident: bib0002 article-title: The effect of prominence and cue association in retrieval processes: a computational account publication-title: Cogn. Sci. – volume: 111 year: 2020 ident: bib0001 article-title: Interference patterns in subject-verb agreement and reflexives revisited: a large-sample study publication-title: J. Mem. Lang. – volume: 23 start-page: 968 year: 2019 end-page: 982 ident: bib0008 article-title: Computational models of retrieval processes in sentence processing publication-title: Trends Cogn. Sci. (Regul. Ed.) – year: 2018 ident: bib0005 article-title: Handbook of Approximate Bayesian Computation – volume: 43 start-page: e12738 year: 2019 ident: bib0007 article-title: Parameter inference for computational cognitive models with approximate bayesian computation publication-title: Cogn. Sci. – volume: 111 year: 2020 ident: 10.1016/j.mex.2020.100850_bib0001 article-title: Interference patterns in subject-verb agreement and reflexives revisited: a large-sample study publication-title: J. Mem. Lang. doi: 10.1016/j.jml.2019.104063 – volume: 69 start-page: 85 issue: 2 year: 2013 ident: 10.1016/j.mex.2020.100850_bib0003 article-title: Contrasting intrusion profiles for agreement and anaphora: experimental and modeling evidence publication-title: J. Mem. Lang. doi: 10.1016/j.jml.2013.04.003 – year: 2018 ident: 10.1016/j.mex.2020.100850_bib0006 – year: 2018 ident: 10.1016/j.mex.2020.100850_bib0005 – volume: 29 start-page: 375 issue: 3 year: 2005 ident: 10.1016/j.mex.2020.100850_bib0004 article-title: An activation‐based model of sentence processing as skilled memory retrieval publication-title: Cogn. Sci. doi: 10.1207/s15516709cog0000_25 – volume: 43 start-page: e12738 issue: 6 year: 2019 ident: 10.1016/j.mex.2020.100850_bib0007 article-title: Parameter inference for computational cognitive models with approximate bayesian computation publication-title: Cogn. Sci. doi: 10.1111/cogs.12738 – volume: 23 start-page: 968 year: 2019 ident: 10.1016/j.mex.2020.100850_bib0008 article-title: Computational models of retrieval processes in sentence processing publication-title: Trends Cogn. Sci. (Regul. Ed.) doi: 10.1016/j.tics.2019.09.003 – volume: 43 issue: 12 year: 2019 ident: 10.1016/j.mex.2020.100850_bib0002 article-title: The effect of prominence and cue association in retrieval processes: a computational account publication-title: Cogn. Sci. doi: 10.1111/cogs.12800 |
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| Title | Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing |
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