A deep learning method for comparing Bayesian hierarchical models.

Elsemüller, Lasse; Schnuerch, Martin; Bürkner, Paul-Christian; Radev, Stefan T.

doi:10.1037/met0000645

Psychological Methods

2024

DOI: 10.1037/met0000645

|View full text |Cite

A deep learning method for comparing Bayesian hierarchical models.

Lasse Elsemüller,

Martin Schnuerch,

Paul-Christian Bürkner

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 97 publications

(183 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

Schumacher,

Schnuerch,

Voss

et al. 2024

Comput Brain Behav

View full text Add to dashboard Cite

Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

show abstract

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

Schumacher,

Schnuerch,

Voss

et al. 2024

Comput Brain Behav

View full text Add to dashboard Cite

show abstract

The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models

Kvam

2024

Psychon Bull Rev

View full text Add to dashboard Cite

Theories of dynamic decision-making are typically built on evidence accumulation, which is modeled using racing accumulators or diffusion models that track a shifting balance of support over time. However, these two types of models are only two special cases of a more general evidence accumulation process where options correspond to directions in an accumulation space. Using this generalized evidence accumulation approach as a starting point, I identify four ways to discriminate between absolute-evidence and relative-evidence models. First, an experimenter can look at the information that decision-makers considered to identify whether there is a filtering of near-zero evidence samples, which is characteristic of a relative-evidence decision rule (e.g., diffusion decision model). Second, an experimenter can disentangle different components of drift rates by manipulating the discriminability of the two response options relative to the stimulus to delineate the balance of evidence from the total amount of evidence. Third, a modeler can use machine learning to classify a set of data according to its generative model. Finally, machine learning can also be used to directly estimate the geometric relationships between choice options. I illustrate these different approaches by applying them to data from an orientation-discrimination task, showing converging conclusions across all four methods in favor of accumulator-based representations of evidence during choice. These tools can clearly delineate absolute-evidence and relative-evidence models, and should be useful for comparing many other types of decision theories.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A deep learning method for comparing Bayesian hierarchical models.

Cited by 2 publications

References 97 publications

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models

Contact Info

Product

Resources

About