Variational Bayesian methods for cognitive science.

Galdo, Matthew; Bahg, Giwon; Turner, B. E.

doi:10.1037/met0000242

Cited by 19 publications

(18 citation statements)

References 92 publications

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“…For example, hierarchical extensions for voxel-wise or group-level modeling are obvious next steps, but they are currently computationally infeasible. Alternatively, two promising directions are sparse GP approximations and variational inference (e.g., [24,37,38]), but the tradeoffs of these approximations have yet to be well studied. For now, we propose the GPJM for its theoretical advantages, and save computational innovations for future investigations.…”

Section: Discussionmentioning

confidence: 99%

Gaussian Process Linking Functions for Mind, Brain and Behavior

Bahg¹,

Evans²,

Galdo³

et al. 2019

Preprint

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The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., EEG, fMRI) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models (Turner, Forstmann, & Steyvers, 2019). Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proven prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, non-parametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fit to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data, and reveals interesting latent cognitive dynamics. Finally, we provide a test of the model’s generalizability by assessing its predictive accuracy in a cross-validation test.

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Section: Discussionmentioning

confidence: 99%

Gaussian Process Linking Functions for Mind, Brain and Behavior

Bahg¹,

Evans²,

Galdo³

et al. 2019

Preprint

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“…Despite their strengths, VB methods are still not widely used in psychological research (see, however, Galdo et al, 2020). One reason is that VB methods have certain limitations which make drawing model-based inferences difficult.…”

Section: Variational Bayesmentioning

confidence: 99%

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Dao¹,

Gunawan²,

Tran³

et al. 2024

Psychological Methods

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Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants.

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“…Such misconceptions might also lead to unnecessarily limited views of each. Bayesian posterior distributions may be estimated by a variety of other methods, such as analytically (e.g., Gelman et al, 2013), via Taylor series approximations (e.g., Tierney & Kadane, 1986), via optimizing an approximating distribution as in variational Bayes approaches (Galdo et al, 2020), or via optimization techniques to approximate features of the posterior (e.g., the mode & curvature, Mislevy, 1986). Furthermore, MCMC techniques may be used to approximate and maximize likelihood functions, quite aside from their use in Bayesian contexts (e.g., Geyer & Thompson, 1992).…”

Section: Brief Review Of Bayes’ Theoremmentioning

confidence: 99%

Perspectives on Bayesian inference and their implications for data analysis.

Levy¹,

McNeish²

2023

Psychological Methods

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Use of Bayesian methods has proliferated in recent years as technological and software developments have made Bayesian methods more approachable for researchers working with empirical data. Connected with the increased usage of Bayesian methods in empirical studies is a corresponding increase in recommendations and best practices for Bayesian methods. However, given the extensive scope of Bayes, theorem, there are various compelling perspectives one could adopt for its application. This paper first describes five different perspectives, including examples of different methodologies that are aligned within these perspectives. We then discuss how the different perspectives can have implications for modeling and reporting practices, such that approaches and recommendations that are perfectly reasonable under one perspective might be unreasonable when viewed from another perspective. The ultimate goal is to show the heterogeneity of defensible practices in Bayesian methods and to foster a greater appreciation for the variety of orientations that exist.

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Variational Bayesian methods for cognitive science.

Cited by 19 publications

References 92 publications

Gaussian Process Linking Functions for Mind, Brain and Behavior

Gaussian Process Linking Functions for Mind, Brain and Behavior

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Perspectives on Bayesian inference and their implications for data analysis.

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