New model to predict belief change


Summary: A new predictive model is able to determine who will change their minds about controversial scientific information when presented with evidence-based research.

Source: Santa Fe Institute

A new type of predictive network model could help determine which people will change their minds about controversial scientific issues when presented with evidence-based information.

A study in Scientists progress presents a framework for accurately predicting whether a person will change their mind on a certain topic. The approach estimates the amount of dissonance, or mental discomfort, a person has from having conflicting beliefs about a topic.

Santa Fe Institute postdoctoral fellows Jonas Dalege and Tamara van der Does built on previous efforts to model belief change by integrating both moral and social beliefs into a statistical physics framework of 20 interacting beliefs.

They then used this cognitive network model to predict how the beliefs of a group of nearly 1,000 people, who were at least somewhat skeptical about the effectiveness of GM foods and childhood vaccines, would change as a result. of an educational intervention.

Study participants received a message about the scientific consensus on genetic modification and vaccines. Those who entered the study with a lot of dissonance in their interwoven web of beliefs were more likely to change their beliefs after seeing the message, but not necessarily agree with the message. In contrast, people with little dissonance showed little change following the intervention.

Belief networks and the development of interdependence on measures. Credit: Jonas Dalege and Tamara van der Does

“For example, if you think scientists are inherently trustworthy, but your family and friends tell you that vaccines aren’t safe, that’s going to create some dissonance in your mind,” van der Does says.

“We found that if you were already some sort of GMO food or vaccine to begin with, you would just go more in that direction when you were presented with new information, even if that was not the intention of intervention.”

Although still at an early stage, the research could ultimately have important implications for communicating evidence-based scientific information to the public.

“On the one hand, you might want to target people who have some dissonance in their beliefs, but at the same time, it also creates a risk that they will reduce their dissonance in a way that you didn’t want them to. they do,” says Dalege. .

“Moving forward, we want to expand this research to see if we can learn more about why people take certain paths to reduce their dissonance.”

About this psychology research news

Author: Amy Akmal
Source: Santa Fe Institute
Contact: Amy Akmal – Santa Fe Institute
Image: The image is attributed to Jonas Dalege and Tamara van der Does

Original research: Free access.
“Using a cognitive network model of moral and social beliefs to explain belief change” by Jonas Dalege et al. Scientists progress

See also

It shows a brain


Using a cognitive network model of moral and social beliefs to explain belief change

Skepticism about childhood vaccines and genetically modified foods has grown despite scientific evidence of their safety. Beliefs about scientific matters are difficult to change because they are rooted in many moral concerns and interrelated beliefs about what other people think.

We propose a cognitive network model that estimates the network links between all interdependent beliefs to calculate global dissonance and interdependence.

Using a nationally representative probabilistic longitudinal study, we test whether our model can be used to predict belief change and find support for our model’s predictions: high network dissonance predicts belief change later, and people are pushed into weaker network dissonance.

We show the advantages of measuring dissonance using belief network structure over traditional measures.

This study is the first to combine a unifying predictive model with an experimental intervention and shed light on the dynamics of dissonance reduction leading to belief change.


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