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Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

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Related Experiment Video

Updated: Jul 30, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Cell Decision Making through the Lens of Bayesian Learning.

Arnab Barua^1,2, Haralampos Hatzikirou^3,4

¹Departement de Biochimie, Université de Montréal, Montréal, QC H3T 1C5, Canada.

Entropy (Basel, Switzerland)

|May 16, 2023

Summary

Cell decision-making relies on Bayesian learning, where microenvironmental entropy changes dictate cell state probability. This framework explains cell sensing impacts without detailed biochemical data.

Keywords:

Bayesian learning cell decision making cell sensing dynamics hierarchical Fokker–Planck equation least microenvironmental uncertainty principle (LEUP)multiscale

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Area of Science:

Cell biology
Theoretical biology
Statistical mechanics

Background:

Cell decision-making involves sensing the microenvironment to regulate internal states.
Microenvironmental sensing is crucial for cellular responses.
The underlying regulatory mechanisms are not fully understood.

Purpose of the Study:

To hypothesize and explore Bayesian learning as the regulatory principle for cell decision-making.
To investigate the temporal evolution of internal cell states under this hypothesis.
To develop a formalism for understanding cell sensing impacts on cell decision-making.

Main Methods:

Derivation of a hierarchical Fokker-Planck equation for cell-microenvironment dynamics using timescale separation.
Integration of the Bayesian learning hypothesis with the derived dynamics.
Analysis of microenvironmental entropy's role in cell state probability distribution.

Main Results:

Microenvironmental entropy changes were found to dominate the cell state probability distribution.
A theoretical framework was established to link cell sensing to decision-making dynamics.
The model allows analysis of cell state dynamics using key parameters, independent of specific biochemical details.

Conclusions:

Cell decision-making regulation can be effectively described by Bayesian learning principles.
Microenvironmental factors, particularly entropy, are key drivers of cellular responses.
The developed formalism offers a generalized approach to studying cell sensing and decision-making.