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Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Expected Value

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Updated: Nov 22, 2025

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

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Learning in Volatile Environments With the Bayes Factor Surprise.

Vasiliki Liakoni¹, Alireza Modirshanechi², Wulfram Gerstner³

¹École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and School of Life Sciences, 1015 Lausanne, Switzerland vasiliki.liakoni@epfl.ch.

Neural Computation

|January 5, 2021

Summary

Surprise-based learning enables agents to adapt quickly to changing environments. New algorithms using Bayes Factor Surprise improve adaptation and parameter estimation, outperforming existing methods.

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Area of Science:

Computational neuroscience
Machine learning
Reinforcement learning

Background:

Agents need to adapt to nonstationary environments.
Surprise-based learning offers a mechanism for rapid adaptation.
Existing methods face challenges in balancing memory and new information.

Purpose of the Study:

To investigate surprise-modulated learning in hierarchical Bayesian models.
To introduce the Bayes Factor Surprise as a key modulator.
To develop novel, efficient surprise-based algorithms.

Main Methods:

Exact Bayesian inference in a hierarchical model.
Derivation of three novel algorithms: particle filter, variational learning, and message passing.
Analysis of Bayes Factor Surprise in existing approximate algorithms.

Main Results:

Bayes Factor Surprise modulates adaptation rates in existing algorithms.
Novel algorithms demonstrate constant scaling and simple updates for exponential family distributions.
Surprise-based algorithms achieve superior parameter estimation and performance.

Conclusions:

Surprise-based learning provides a unified framework for adaptation.
The proposed algorithms offer efficient and effective solutions for nonstationary environments.
Findings have implications for understanding behavior and improving reinforcement learning.