Theory‐led confirmation bias and experimental persona

Allen, Michael

doi:10.1080/02635143.2010.539973

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…These two heuristics-namely, confirmation bias and conformity-mutually reinforce each other. Specifically, to save energy, confirmation bias leads to agents' being drawn to groups that validate their opinion, and thus increases the probability of behavioural and epistemic conformity [26]. Importantly, these two heuristics form the basis for information spread.…”

Section: The Spread Of Ideasmentioning

confidence: 99%

Epistemic Communities under Active Inference

Albarracin

Daphne

Ramstead

et al. 2022

Entropy

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The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

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Section: The Spread Of Ideasmentioning

confidence: 99%

Epistemic Communities under Active Inference

Albarracin

Daphne

Ramstead

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…These two heuristics, confirmation bias and conformity, mutually reinforce each other. Specifically, to save energy, confirmation bias leads to agents' being drawn to groups that validate their opinion, and thus increases the probability of behavioural and epistemic conformity [27]. Importantly, these two heuristics form the basis for information spread.…”

Section: The Spread Of Ideasmentioning

confidence: 99%

Epistemic Communities under Active Inference

Albarracin¹,

Daphne²,

Ramstead³

et al. 2022

Preprint

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The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

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“…This bias has been shown to influence data collection and decision-making in many disciplines, including animal behavior research, 5,6 psychiatric diagnoses, 7 criminal investigation, 8,9 and middle-school level science education. 10 The phenomenon of confirmation bias has been seen in psychology literature. 11,12 Peer reviewers were more likely to accept research that aligned with their beliefs than that which differed, even when the methodology was exactly the same.…”

Section: Introductionmentioning

confidence: 99%

Confirmation Bias as a Factor in Pharmacy Student Assessment of Research Study Quality

Miller

Jangula

2019

American Journal of Pharmaceutical Education

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Objective. To determine whether pharmacy students' prior beliefs and attitudes about drug products and dietary supplements affected their ability to analyze the quality of research study abstracts and use them in making drug recommendations to patients. Methods. Fifty-nine Doctor of Pharmacy (PharmD) students in a drug literature course were randomly assigned to receive one of two forms to evaluate four drug literature abstracts of varying quality and study design. On each form, there were two abstracts that had been taken directly from published research studies and two abstracts in which a different product had been substituted for the actual product studied. Pharmacy students completed a questionnaire about the studies to determine whether their evaluation of quality was affected by their prior opinions about the products. Results. Students correctly recognized the relative quality of the studies. However, after reading abstracts of research articles that were identical except for the product named, students were still more likely to recommend drugs approved by the Food and Drug Administration than dietary supplements. Conclusion. Pharmacy students' evaluation of clinical research studies was mildly influenced by confirmation bias but more so by the quality of the research.

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Theory‐led confirmation bias and experimental persona

Cited by 10 publications

References 26 publications

Epistemic Communities under Active Inference

Epistemic Communities under Active Inference

Epistemic Communities under Active Inference

Confirmation Bias as a Factor in Pharmacy Student Assessment of Research Study Quality

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