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15th World Congress on Computational Mechanics (WCCM-XV)

8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII)

31 July – 5 August 2022, Yokohama, Japan

Modelling Cognitive Bias in Safety using Bayesian Inference

Hideyoshi Yanagisawa1*

1 The University of Tokyo

7-3-1 Hongo, Bunkyo, Tokyo, JAPAN, hide@mech.t.u-tokyo.ac.jp

Key Words: Safety, Perception, Cognitive Bias, Bayes, Covid-19

In a human centred society, sense of safety perceived by people is important to consider as well

as actual safety. The gap between expectations and reality in safety causes social problems.

Overestimation of safety provokes dangerous behaviours, while underestimation of safety

causes excessive behavioural restraint. For example, in COVID 19 pandemic, the

overestimation may lead to the spread of infection due to dangerous behaviours, and the

underestimation may lead to slow consumption due to excessive self-control.

In this talk, I propose a mathematical framework to model sense of safety using Bayesian

inference. Recent neuroscience studies suggest that human brain activities can be explained as

a Bayes’ model(Knill & Pouget, 2004). Here, based on Helmholtz’s epistemology, I assume

that safety x is inferred as the cause of observation (data: y) and define the sense of safety as

Bayesian posterior p(x|y) that is proportional to a product of a prior p(x) and likelihood p(y|x).

The prior and the likelihood represent the expectation of safety and the safety based solely on

the data (observed safety), respectively. In this model, the prior (safety belief) is updated to the

posterior (perception of safety).

The difference between the posterior mean and the peak of likelihood represents the gap

between perceived and observed safety. This gap is regarded as a cognitive bias termed

expectation effect (Yanagisawa, 2016). In our previous study, we modelled the expectation

effect as a function of three parameters: a prediction error (difference between prior mean and

peak of likelihood), prior precision (inverse variance of the prior), and observation precision

(inverse variance of the likelihood). We found that there are two types: assimilation and contrast.

Assimilation diminishes the prediction error, and contrast exaggerates the prediction error. The

expectation effect explains several psychological biases that lead to abnormal behaviours, such

as normalcy bias, excessive anxiety, and overconfidence. Based on the expectation effect model,

I discuss the condition of each psychological bias in safety. In addition, I discuss how to model

emotions such as anxious and fear in a mathematical manner by applying information theoretic

quantities such as surprisal and free energy (Yanagisawa, Kawamata, & Ueda, 2019).

REFERENCES

Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and

computation. Trends in Neurosciences, 27(12), 712-719. doi:10.1016/j.tins.2004.10.007

Yanagisawa, H. (2016). A computational model of perceptual expectation effect based on neural coding

principles. Journal of Sensory Studies, 31(5), 430-439. doi:10.1111/joss.12233

Yanagisawa, H., Kawamata, O., & Ueda, K. (2019). Modeling Emotions Associated With Novelty at

Variable Uncertainty Levels: A Bayesian Approach. Frontiers in Computational Neuroscience,

13(2). doi:10.3389/fncom.2019.00002