Granger causality in cardiovascular variability series: Comparison between model-based and model-free approaches

2012 ◽  
Author(s):  
A. Porta ◽  
T. Bassani ◽  
V. Bari ◽  
S. Guzzetti
Keyword(s):  
Granger Causality ◽  
Cardiovascular Variability ◽  
Model Based ◽  
Model Free

scholarly journals Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ying Liu ◽  
Selin Aviyente
Keyword(s):  
Granger Causality ◽  
Linear Models ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Detection Methods ◽  
Information Theoretic ◽  
Model Based ◽  
Model Free ◽  
Directed Information ◽  
The Brain

Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Model-Based and Model-Free Control of DC-DC Converters with High-Order Dynamics and Limited Measurements

2020 ◽  
pp. 1-1
Author(s):  
Javier Loranca ◽  
Jonathan Carlos Mayo Maldonado ◽  
Gerardo Escobar ◽  
Carlos Villarreal-Hernandez ◽  
Thabiso Maupong ◽  
...  
Keyword(s):  
High Order ◽  
Model Based ◽  
Model Free ◽  
Model Free Control

scholarly journals Combining Model-Based Design and Model-Free Policy Optimization to Learn Safe, Stabilizing Controllers

2021 ◽  
Vol 54 (5) ◽  
pp. 19-24
Author(s):  
Tyler Westenbroek ◽  
Ayush Agrawal ◽  
Fernando Castañeda ◽  
S Shankar Sastry ◽  
Koushil Sreenath
Keyword(s):  
Stabilizing Controllers ◽  
Model Based ◽  
Model Free ◽  
Policy Optimization

Model based processing of cardiovascular variability applied to bed-rest case studies

2008 ◽  
Author(s):  
F. Vallais ◽  
F. Aletti ◽  
G. Baselli ◽  
E. Tam ◽  
M. Cautero ◽  
...  
Keyword(s):  
Case Studies ◽  
Bed Rest ◽  
Cardiovascular Variability ◽  
Model Based
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