Neuroimage April 2021 PMID 33524573

Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults.

Joram Soch, Anni Richter, Hartmut Schütze, Jasmin M Kizilirmak, Anne Assmann, Lea Knopf, Matthias Raschick, Annika Schult, Anne Maass, Gabriel Ziegler, Alan Richardson-Klavehn, Emrah Düzel, Björn H Schott

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Mentions

Peak: 2 in Feb 2021

across 2 sources since Jan 2021

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3,427

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Jan 2021 – Mar 2025

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AI synopsis

What people are saying

Discussions highlight the importance of Bayesian model selection and its preference for parametric models, with phrases like 'optimal models for memory encoding' and 'overcome limitations of categorical models' indicating both interest and perceived scientific impact. The tone reflects curiosity about methodological advancements without overly emotional language, contributing to an overall sense of cautious optimism about the research's significance.

AgreementModerate agreement

The posts generally support the findings, emphasizing the preference for parametric models in recent research on memory encoding.

InterestModerate interest

Discussions show a moderate level of curiosity, focusing on methodological advances and implications for future studies.

EngagementModerate engagement

Posts analyze the research methodology and applications, indicating a reflective but not deeply exploratory engagement.

ImpactModerate impact

The posts suggest the findings could influence current modeling approaches in neuroimaging studies, hinting at meaningful scientific progress.

Abstract in brief

Bayesian model selection shows that parametric models, especially those with nonlinear transformations of memory confidence, better explain fMRI data during memory encoding than traditional categorical models in both young and older adults.
Using crossvalidated Bayesian model selection, the study found that parametric approaches overcome limitations of categorical models, which struggle with participants who have lower memory performance, particularly in older or memory-impaired individuals.
This research offers a new framework for accurately modeling brain activity related to memory encoding, making it more applicable to diverse populations, including those with memory impairments.]

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All mentions

Bayesian Model Selection Finds Parametric fMRI Memory Models Superior

Bayesian model selection shows that parametric models, especially those with nonlinear transformations of memory confidence, better explain fMRI data during memory encoding than traditional categorical models in both…

Joram Soch · Mar 15, 2025 44 views

Joram Soch

@JoramSoch · X

FYI: The proof-read journal PDF is now online (open access): https://t.co/66UpQeCX0H.

Feb 5, 2021 · 1 like · 486 followers View post →

Joram Soch

@JoramSoch · X

Feb 2, 2021 · 4 likes · 486 followers View post →

MRI Papers

@mripapers · X

NI: Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults https://t.co/z7xTAL1xFf

Jan 31, 2021 · 1 like · 2,412 followers View post →