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Related Experiment Video

Updated: May 12, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Likelihood-free Bayesian analysis of memory models.

Brandon M Turner1, Simon Dennis2, Trisha Van Zandt2

  • 1Department of Psychology.

Psychological Review
|April 17, 2013
PubMed
Summary
This summary is machine-generated.

Approximate Bayesian computation (ABC) enables fitting complex computational memory models to data. This method overcomes limitations of traditional analyses, allowing for deeper insights into memory processes.

More Related Videos

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Related Experiment Videos

Last Updated: May 12, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Many computational memory models lack a computable likelihood function, hindering direct data fitting.
  • Traditional statistical and Bayesian analyses are not directly applicable to these simulation-based models.
  • This limitation restricts the ability to rigorously test and compare different memory theories.

Purpose of the Study:

  • To demonstrate the application of Approximate Bayesian Computation (ABC) for fitting computational memory models.
  • To analyze the Bind Cue Decide (BCD) and Retrieving Effectively from Memory (REM) models using ABC.
  • To explore parameter relationships and evaluate model fits that were previously intractable.

Main Methods:

  • Utilized Approximate Bayesian Computation (ABC), a likelihood-free Bayesian inference technique.
  • Applied ABC to hierarchical versions of the BCD and REM models.
  • Fitted models to empirical data from Dennis, Lee, and Kinnell (2008) and Kinnell and Dennis (2012).

Main Results:

  • Successfully fitted hierarchical BCD and REM models to episodic memory data using ABC.
  • ABC analysis allowed for the exploration of parameter interdependencies within each model.
  • Relative model fits to the data were evaluated, providing comparative insights.

Conclusions:

  • Approximate Bayesian Computation (ABC) provides a viable framework for analyzing complex computational memory models.
  • This methodology enables previously impossible analyses, such as direct parameter exploration and model comparison.
  • The findings facilitate a more rigorous quantitative evaluation of episodic memory theories.