Sociodemographic data for all four groups (N = 123).

Experimental design.

(a) Timeline of testing. Four groups were tested before the COVID-19 outbreak in October 2019, during the first complete lockdown of social and economic life in March and April 2020, after a partial lockdown in Mai 2021, and after the pandemic in June 2022. (b) Belief updating task. Panels show subsequent events within a good news trial (left panels) and a bad news trial (right panel). Responses were self-paced. The task goal was to estimate the risk of experiencing different adverse future life events (e.g., tooth decay) for oneself and for somebody else before (E1) and after (E2) being presented with information about the event’s prevalence in the general population (i.e., base rate).

Behavioral results.

(a) Boxplots display the belief-updating bias (i.e., the difference between the belief update for good news minus belief update for bad news) in each of the four participant groups tested before the pandemic in October 2019 (n=30), during the first lockdown from March to April 2020 (n=34), with less restrictive measures in Mai 2021 (n=31), and at the end of the pandemic in June 2022 (n=28). (b) Belief updating for good and bad news during (n=65) and outside the pandemic (n=58). (c) Confidence ratings, and (d) estimation errors for bad and good news during and outside the pandemic. Boxplots in all panels display 95% confidence intervals, with boxes indicating the interquartile range from Q1 25th to Q3 75th percentile. The horizontal black lines indicate medians and whiskers range from minimum to maximum values and span 1.5 times the interquartile range. The dots correspond to individual participants. The source data file provides exact p-values. *p < 0.05 two-sampled, two-tailed t-tests, * p < 0.05 two-sampled, one-tailed t-tests.

Computational model comparisons.

Twelve alternative models from RL-like (blue) and Bayesian (orange) updating model families were fitted to observed belief updates for participants tested during the COVID-19 pandemic (left panel columns) and outside the pandemic (right column panels). (a) Protected exceedance probabilities for each of the 12 alternative models. (b) Posterior model attributions. Colored cells display the probability that individual participants (y-axis) will be best explained by a model version (x-axis). (c) Estimated model frequencies correspond to how many participants are expected to be best described by a model version, with error bars corresponding to standard deviations. The red line indicates the null hypothesis that all model versions are equally likely in the cohort (chance level). Labels on the x-axis of the histogram and bar graphs indicate the model versions with non-silenced parameters (S— scaling, A— Asymmetry) and PR — personal relevance of events.

Parameter comparisons between participants tested during (n=65) and outside (n=58) the COVID-19 pandemic.

(a) Learning rates. Boxplots display 95% confidence intervals for learning rates from the RL-like updating model that assumed updating is proportional to the estimation error with an asymmetry and a scaling learning rate component. (b) Parameter recovery for learning rate components. Pearson’s correlation between generating and recovered parameters for scaling (left panel) and asymmetry (right panel) learning rate component. r—Pearson’s correlation coefficient against zero. Source data and exact p-values are provided as a Source Data file. (c) Group comparisons scaling and asymmetry components. Boxplots display 95% confidence intervals for the learning rate’s scaling (left panel) and the asymmetry (right panel) component. Boxes in all boxplots correspond to the interquartile range from Q1 25th percentile to Q3 75th percentile. The horizontal black lines indicate medians and whiskers range from minimum to maximum values and span 1.5 times the interquartile range. The dots correspond to individual participants. *p <0.05. P-values were obtained with two-sampled, two-tailed t-tests between groups, and exact p-values are provided in the source data file.

Belief updating within the same group of participants tested before and during the COVID-19 pandemic (n=28).

Boxplots display 95% confidence intervals for belief updating after bad and good news and during and outside the pandemic. Boxes indicate the interquartile range from Q1 25th to Q3 75th percentile. The horizontal black lines indicate medians and whiskers range from minimum to maximum values and span 1.5 times the interquartile range. The dots correspond to individual participants. The source data file provides exact p-values. *p < 0.05 two-sampled, two-tailed t-tests.

Estimated model frequencies for participants tested both before and during the COVID-19 pandemic.

The histograms display average posterior model frequencies that reflect how many participants are expected to be best described by a model version, with error bars corresponding to standard deviations. The red line indicates the null hypothesis that all model versions are equally likely in the cohort (chance level). Labels on the x-axis of the histograms indicate the model versions with non-silenced parameters (S – scaling, A–asymmetry), and PR – personal relevance factor.

Survey responses in n=40 participants tested during the pandemic.

Linear Mixed-Effects Model results fitting the average absolute Belief Updates (UPD) in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average absolute Belief Updates (UPD) in participants tested before the COVID-19 outbreak in France (October 2019, n=30, baseline), and comparing them to participants tested during the first lockdown in March/April 2020 (n=34, context 1), one year later in Mai 2021 during a less strict lockdown (n=31, context 2), and at the lift of the sanitary state of emergency in June 2022 (n=28, context 3).

Linear Mixed-Effects Model results fitting the average absolute Belief Updates (UPD) in participants tested both before and during the pandemic (n = 28)

Linear Mixed-Effects Model results fitting the average confidence ratings in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average absolute Estimation Error (EE) in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average Learning Rates in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average Learning Rates for RL-like model in participants tested both before and during the pandemic (n = 28)

Linear Mixed-Effects Model results fitting the average asymmetry in the RL-like model in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average scaling in the RL-like model in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average asymmetry in the RL-like model in participants tested both before and during the pandemic (n = 28)

Linear Mixed-Effects Model results fitting the average scaling in the RL-like model in participants tested both before and during the pandemic (n = 28)

Linear Mixed-Effects Model results fitting the average number of trials in participants tested outside (n=58) and during (n=65) the pandemic

Linear Mixed-Effects Model results fitting the average number of under- and overshooting in participants tested outside (n=58) and during (n=65) the pandemic

Sociodemographical data (N = 123)