< Rationality

How do our cognitive biases influence us during COVID-19 ?

Text updated on 2020-07-19

Faced with the amount of information available in their environment, human beings often resort to what are called intuitive mental processes, known as "heuristics", which enable information to be processed and decisions to be made quickly and without further cost. These heuristics can be very effective or, on the contrary, can lead to systematic errors known as "cognitive bias". Let's understand them better in order to free ourselves from them in our judgements!

Some cognitive biases may have influenced our behaviour towards minimizing COVID-19. Today, we do not have the necessary hindsight to assess the impact of cognitive biases in the perception and management of the crisis and it would be very difficult from a methodological point of view to attribute a specific act (for example, not closing schools) to a given bias (e.g., a bias of optimism on the part of a leader). However, it has been shown through many experiments in experimental psychology and neuroscience that certain biases are particularly relevant to explain human behaviour. Here is a selection, arbitrary and non-exhaustive, of cognitive biases that can influence our perception and the decisions made during the crisis.

  1. Optimism bias: We tend to be irrationally optimistic and this has several implications:

Moreover, when we learn new information, we integrate it more easily if it is in our favour rather than against us. For example, if an individual initially estimates his or her probability of developing cancer at 40% and is told that this probability is "only" 30%, the individual will tend to integrate this new information more easily than if he or she had been told that the probability was ultimately 50%.

This bias may explain in part why countries in Europe and North America, among others, did not immediately feel concerned about the epidemic when it was spreading to China at a time when there was considerable human flux worldwide.

2. Confirmation bias: this bias leads to an increased sensitivity to those who confirm our beliefs or assumptions, and a reduced sensitivity to those who disprove them.

For example, if a person thinks that wearing a mask is not helpful in the fight against the spread of the SARS-CoV-2 coronavirus, he or she will tend to be preferentially sensitive to facts that point in that direction and not to consider facts that prove otherwise. Consequently, s/he may focus on the fact that there have been as many deaths in one country that has made the wearing of masks mandatory as in another that has not, without taking into account other potential explanatory factors such as population density, cultural habits, age of the population, or co-morbidity factors.

3. Availability bias: Our estimates of the probability of an event are biased by the ease with which its occurrence comes to mind. If it is easy, we estimate the event as probable. This phenomenon can lead to an overestimation of the frequency of mediatized delinquency, and thus create exaggerated fear. Alternatively, an underestimation of the prevalence of a disease may occur if we do not know of any sick people in our community which may lead to a non-compliance with security rules.

The underestimation of the risk for people who have not experienced severe illness is particularly relevant today for COVID-19. Low-risk individuals, i.e., the "young and invincible", frequently underestimate their contribution to the risk of disease transmission and its impact on the population because there are no severe cases in their surroundings.

Another specificity in the event of an epidemic is the underestimation of the exponential growth in the number of people affected by COVID-19 when the reproduction rate is higher than 1. When a contagious individual infects several people, who may themselves infect several people, the number of cases increases exponentially. Whether the available information is numerical or visual, we tend to underestimate exponential growth rates.

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Experts in law, psychology, and economics explore the power of "fast and frugal" heuristics in the creation and implementation of law.

Gigerenzer, G. In G. Gigerenzer & С. Engel (Eds.), Heuristics and the law (pp. 17-44). Cambridge, MA: MIT Press© 2006.

Prejudices in judgments reveal certain heuristics of thinking in the face of uncertainty.

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124-1131. Retrieved June 22, 2020.

Introduction to the optimism bias.

Jefferson, A., Bortolotti, L., & Kuzmanovic, B. (2017). What is unrealistic optimism?. Consciousness and Cognition, 50, 3-11.

This study reflects that we may overestimate our winnings at a random game based on our past history.

Langer, E. J., & Roth, J. (1975). Heads I win, tails it's chance: The illusion of control as a function of the sequence of outcomes in a purely chance task. Journal of Personality and Social Psychology, 32(6), 951-955.

People evaluate themselves more positively than most other people: this is the "I am above average" effect.

Brown, J. D. (2012). Understanding the better than average effect: Motives (still) matter. Personality and Social Psychology Bulletin, 38(2), 209-219.

Description of the optimism bias.

Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of personality and social psychology, 39(5), 806.

This study shows that we update our beliefs more in response to positive information than in response to information that is negative for us. The most optimistic people show a lack of selective updating and a decrease in neural coding of undesirable information about the future.

Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained in the face of reality. Nature neuroscience, 14(11), 1475-1479.

In one study, participants were asked to estimate how often a given letter (e.g., "K") is found in the first or third position of a word in the English language. Subjects tended to rate the first position as more frequent for the majority of the letters presented, whereas all of them were more frequent in the third position. This is due to the fact that the types of words that come to mind more easily are those that begin with a "K": they are therefore considered more frequent.

Tversky, A., & Kahneman, D. (1973). A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232.

The duckweed experiment in a pond reveals our underestimation of exponential growth.

Wagenaar, W. A., & Timmers, H. (1979). The pond-and-duckweed problem: three experiments on the misperception of exponential growth. Acta psychologica, 43(3), 239-251.

This study shows that we tend to be preferentially sensitive to facts that are consistent with our beliefs and not to consider facts that prove the opposite.

Beattie, J., & Baron, J. (1988). Confirmation and matching biases in hypothesis testing. The Quarterly Journal of Experimental Psychology, 40(2), 269-297.

Further reading

How to untangle the truth from the false about the COVID-19 pandemic in the media?