Factor analysis linking functions for simultaneously modeling neural and behavioral data

Turner, B. E.; Wang, Ting; Merkle, Edgar C.

doi:10.1016/j.neuroimage.2017.03.044

Cited by 52 publications

(85 citation statements)

References 66 publications

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“…For instance, structural equation models (e.g., Hoyle, 2012;Cole & Preacher, 2014; for Bayesian solutions, see Kaplan & Depaoli, 2012; S.-Y. Lee, 2007;Song & Lee, 2012), factor models (e.g., Lopes & West, 2004;Ghosh & Dunson, 2009;Turner, Wang, & Merkle, 2017), and cognitive latent variable models (Vandekerckhove, 2014) provide more sophisticated avenues for the estimation and testing of covariance structures in the presence of noise in the data. Moreover, as discussed earlier, within the Bayesian framework we are not limited to the two-step procedure illustrated here.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, estimating the pair-wise correlations between model parameters using the simultaneous hierarchical approach has computational consequences; the simultaneous approach can result in highly complex models with undesirable estimation properties (e.g., Turner et al, 2017). We view our two-step method as an easy-to-use and flexible complement to more principled approaches, one that is especially valuable in exploratory analyses of the correlation between model parameters and external observations (see also Ly, Boehm, et al, in press).…”

Section: Discussionmentioning

confidence: 99%

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Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

Matzke

Selker

et al. 2017

Collabra: Psychology

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Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Bayesian hierarchical model proposed by Behseta, Berdyyeva, Olson, and Kass (2009) that allows researchers to infer the underlying correlation between error-contaminated observations. We show that this approach may be also applied to obtain the underlying correlation between uncertain parameter estimates as well as the correlation between uncertain parameter estimates and noisy observations. We illustrate the Bayesian modeling of correlations with two empirical data sets; in each data set, we first infer the posterior distribution of the underlying correlation and then compute Bayes factors to quantify the evidence that the data provide for the presence of an association.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

Matzke

Selker

et al. 2017

Collabra: Psychology

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“…We believe that Warp-III may be especially useful for so-called sloppy models with highly correlated parameters (Brown & Sethna, 2003), including but not limited to race models of response times, which often yield skewed posterior distributions (e.g., Brown & Heathcote, 2008;Matzke, Love, & Heathcote, 2017). The Warp-III methodology also lends itself to model comparison in extensions of hierarchical cognitive models that impose on the model parameters a statistical structure such as a linear regression, factor analysis, or analysis of variance (e.g., Boehm, Steingroever, & Wagenmakers, 2017;Heck et al, 2018a;Turner, Wang, & Merkle, 2017;Vandekerckhove, 2014). The application of Warp-III to complex experimental designs is ongoing work in our laboratory.…”

Section: Discussionmentioning

confidence: 99%

A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models using Warp-III Bridge Sampling

Gronau¹,

Wagenmakers²,

Heck³

et al. 2017

Preprint

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Multinomial processing trees (MPTs) are a popular class of cognitive models for categorical data. Typically, researchers compare several MPTs, each equipped with many parameters, especially when the models are implemented in a hierarchical framework. A Bayesian solution is to compute posterior model probabilities and Bayes factors. Both quantities, however, rely on the marginal likelihood, a highdimensional integral that cannot be evaluated analytically. In this case study, we show how Warp-III bridge sampling can be used to compute the marginal likelihood for hierarchical MPTs. We illustrate the procedure with two published data sets and demonstrate how Warp-III facilitates Bayesian model averaging.

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“…Within hierarchical models, it is also possible to investigate relationships between model parameters and neurophysiological measures from techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI) and eyetracking (e.g., Cassey et al, 2014;Cassey et al, 2016;Turner et al, 2013;Turner et al, 2015;Turner et al, 2017). The inclusion of such measures in the model helps to inform the neural correlates of parameters and bridge brain and behavioural data (Ratcliff & McKoon, 2008;Turner et al, 2016).…”

Section: Figure 1 Representation Of the Decision-making Process In Tmentioning

confidence: 99%

Perceptual decision-making in children: Age-related differences and EEG correlates

Manning¹,

Wagenmakers²,

Norcia³

et al. 2020

Preprint

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Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.

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Factor analysis linking functions for simultaneously modeling neural and behavioral data

Cited by 52 publications

References 66 publications

Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models using Warp-III Bridge Sampling

Perceptual decision-making in children: Age-related differences and EEG correlates

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