1. Neuroscience

Effects of experiencing the COVID-19 pandemic on optimistically biased belief updating

  1. Iraj Khalid
  2. Orphee Morlaas
  3. Hugo Bottemanne
  4. Lisa Thonon
  5. Thomas Da Costa
  6. Philippe Fossati
  7. Liane Schmidt  Is a corresponding author
  1. Control-interception-attention team, Paris Brain Institute (ICM), UMR 7225, U1127, Institut National de la Santé et de la Recherche Médicale/Centre National de la Recherche Scientifique/Sorbonne Universités, Hôpital Pitié-Salpêtrière, France
  2. Département de Psychiatrie Adulte, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris (APHP), France
https://doi.org/10.7554/eLife.101157.3
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Cite this article
  1. Iraj Khalid
  2. Orphee Morlaas
  3. Hugo Bottemanne
  4. Lisa Thonon
  5. Thomas Da Costa
  6. Philippe Fossati
  7. Liane Schmidt
(2025)
Effects of experiencing the COVID-19 pandemic on optimistically biased belief updating
eLife 13:RP101157.
https://doi.org/10.7554/eLife.101157.3
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4 figures, 19 tables and 1 additional file

Figures

Figure 1 with 2 supplements
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Behavioral results.

(a) Boxplots display the belief-updating bias (i.e. the difference between the belief update for good news and 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 May 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 squares in the boxplots in (b) correspond to mean observed updates (purple) and mean modelled updates (blue; averaged across 1000 estimations) from the best-fitting models in each context, which were the optimistically biased RL-like model of belief updating outside and the rational Bayesian model of belief updating during the Covid-19 pandemic. The source data file provides exact values. *<0.05 two-sampled, two-tailed t-tests, * p<0.05 two-sampled, one-tailed t-tests.

Figure 1—source data 1

Average values across trials for each participant on behavioral outcome measures.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig1-data1-v1.xlsx
Figure 1—figure supplement 1
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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 (left panel) and good (right panel) 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.

Figure 1—figure supplement 1—source data 1

Average belief update across trials for each participant tested both before and during the pandemic (within-subjects).

https://cdn.elifesciences.org/articles/101157/elife-101157-fig1-figsupp1-data1-v1.xlsx
Figure 1—figure supplement 2
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Optimism bias in initial beliefs about adverse future life events.

First estimates of the likelihood of and adverse life event happening to oneself (left) or someone else (right) and before (n=58) and during (n=65) the COVID-19 pandemic. Boxplots 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 individual dot and vertical line in the middle correspond to the means and standard errors. The contiguous dots correspond to individual participants.

Figure 1—figure supplement 2—source data 1

Average first estimate across trials for each participant.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig1-figsupp2-data1-v1.xlsx
Figure 2 with 3 supplements
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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, which is the probability that the model predominates in the population above and beyond chance. (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. The source data file provides exact values.

Figure 2—source data 1

Model comparison metrics.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig2-data1-v1.xlsx
Figure 2—figure supplement 1
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Estimated model frequencies for participants tested both before and during the COVID-19 pandemic.

(a) Posterior model attributions. Colored cells display the probability that individual participants (y-axis) will be best explained by a model version (x-axis). (b) Estimated model frequencies. 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.

Figure 2—figure supplement 1—source data 1

Model comparison metrics for the within-subjects analysis.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig2-figsupp1-data1-v1.xlsx
Figure 2—figure supplement 2
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Model recovery confusion matrix.

The matrix displays the estimated model frequencies from the model recovery analysis. Each column represents the generative model used to simulate behavioral data, while each row indicates the model used to recover data during the fitting procedure. Higher values along the diagonal (blue) indicate successful recovery, confirming that each model can be reliably distinguished from the others. Off-diagonal values (gray) reflect potential misattributions.

Figure 2—figure supplement 2—source data 1

Estimated model frequencies from the model recovery analysis.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig2-figsupp2-data1-v1.xlsx
Figure 2—figure supplement 3
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Observed and modelled belief updating for the whole participant sample (n=123).

This figure illustrates the percentage of belief update for each participant (blue line) and the estimated belief update (black line) from the overall best fitting optimistically biased RL-like model of belief updating. The shaded blue area reflects the variance in observed data. The colored background highlights the four groups of participants tested in different contexts – before the COVID-19 pandemic (gray), during the 1st lockdown (red), at time of last lockdown release (beige), and one year later (green).

Figure 2—figure supplement 3—source data 1

Average observed and modelled belief update across trials for each participant.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig2-figsupp3-data1-v1.xlsx
Figure 3 with 1 supplement
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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 of the overall best fitting Model 1 (n=123). 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 for 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 values are provided in the source data file.

Figure 3—source data 1

Computational model parameters per participant.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig3-data1-v1.xlsx
Figure 3—figure supplement 1
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Parameter recovery for the wining model family according to context.

Pearson’s correlation between generating and recovered parameters for scaling (upper panel) and asymmetry (lower panel) learning rate component in participants tested outside (n = 58; left panel) and during (n = 65; right panel). The blue doted lines correspond to 95% confidence intervals. r – Pearson’s correlation coefficient against zero.

Figure 3—figure supplement 1—source data 1

Pearson correlation coefficients per participant for parameter recovery.

https://cdn.elifesciences.org/articles/101157/elife-101157-fig3-figsupp1-data1-v1.xlsx
Figure 4
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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 May 2021, and after the lift of the pandemic-related state of emergency in June 2022. (b) Belief updating task. Panels show subsequent appearances on the screen 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 (E1) and for somebody else (eBR) before and after (E2) being presented with information about the event’s prevalence in the general population (i.e. base rate (BR)).

Tables

Table 1
Sociodemographic data for all four groups (N=123).♀: Female; ♂: Male; Note: education is the number of years completed in higher education after a high school diploma.
October2019(N=30)March – April 2020(N=34)May2021(N=31)June2022(N=28)
Age (years)34±242±342±335±3
Gender18 ♀, 12 ♂25 ♀, 9 ♂20 ♀, 11 ♂14 ♀, 14 ♂
Education (years)5±0.44±0.35±0.24±0.4
Appendix 7—table 1
Survey responses in n=40 participants tested during the pandemic.
CategorySpecific questionMeansem
Risk perceptionCOVID-19 risk2.90.2
General risk perception1.80.1
COVID-19 mortality risk3.10.2
Adoption of protective measuresMask wearing3.70.2
Social distancing outside home (shops, work)4.40.1
Hand washing4.20.2
Social distancing at home4.00.1
Gloves wearing1.40.1
Shaking hands, hugging2.20.2
Leave home for work, errands3.70.1
Hand sanitizer use4.00.2
Need for social interactionSocial craving3.80.2
Feeling isolated2.60.2
Calling friends, parents, family, acquaintances4.50.1
Losing contact with friends, acquaintances2.30.2
Social media use3.60.2
Feeling of isolation from loved ones2.90.2
Quality of social interactions3.90.1
MoodSadness and anxiety2.50.2
AnxietyLevel of anxiety2.60.2
Living1 – alone, 2 – alone with pet, 3 – couple w/o children, 4 – couple w/ children, 5 – Family3.30.2
Housing1 – apartment, 2 – apartment w/ outdoor space, 3 – house, 4 – house w/ outdoor space2.30.2
Occupation1 – no occupation. 2 – remote work. 3 – work2.20.1
Displacement1 – no displacement, 2 – public transportation, 3 – car2.40.1

  1. 5–point Likert scale from 1 to 5, 1 – minimal, 3 – medium, 5 – maximum; sem – standard error of the mean.

Appendix 7—table 2
Linear Mixed-Effects Model results fitting the average Belief Updates (UPD) in participants tested outside (n=58) and during (n=65) the pandemic.
UPD ~1 + context +valence + EE+confidence + age+gender + education +design + valence*context + (1 | subject) + (1+valence | subject) + (1+EE | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1851.11913.9–907.541815.1
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept–2.19755.033–0.43662320.6628–12.1147.7188
valence3.24181.22972.63632320.008950.818985.6646
context–0.763281.9987–0.38192320.7029–4.70133.1747
EE0.41870.114343.6622320.000310.193430.64397
confidence0.022340.038590.57892320.56323–0.05370.09836
age–0.006900.03181–0.21692320.82845–0.06960.05577
gender–0.621681.0909–0.56982320.56932–2.77111.5277
education–0.299370.29786–1.0052320.31592–0.88620.2875
design1.7971.96420.91492320.36121–2.07295.6669
valence:context–5.53951.6879–3.28192320.00119–8.8652–2.2139
Random effects covariance parameters (95% CIs):
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.0778NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd3.1204NaNNaN
valenceInterceptcorr–0.38448NaNNaN
valencevalencestd8.3125NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd12.1176.452722.755
EEInterceptcorr–0.99998NaNNaN
EEEEstd0.534860.314950.90834
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std5.0881NaNNaN
Appendix 7—table 3
Linear Mixed-Effects Model results fitting the average Belief Updates (UPD) in participants tested outside (n=58) and during (n=65) the pandemic, corrected for distance defined by the difference between the estimate for oneself (E1) and for others (eBR).
UPD ~1 + context +valence + EE+confidence + distance +age + gender +education + design +valence*context + (1 | subject) + (1+valence | subject) + (1+EE | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1852.81919.1–907.421814.8
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept–1.61075.1877–0.31052310.7565–11.8328.6105
valence3.25391.23072.64392310.00880.829085.6788
context–0.74791.9932–0.37522310.7078–4.67513.1793
EE0.43240.117453.68172310.00030.2010.66382
confidence0.01910.03910.48922310.6252–0.05780.0961
distance–0.06190.1262–0.5002310.6239–0.31050.1866
age–0.00750.0317–0.23712310.8128–0.07000.0550
gender–0.65041.0889–0.59732310.5509–2.79591.4951
education–0.32890.3053–1.07752310.2824–0.93040.2725
design1.88871.96540.96102310.3376–1.98375.7611
valence:context–5.57671.6903–3.29922310.0011–8.9071–2.2463
Random effects covariance parameters (95% CIs):
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd3.3422NaNNaN
valenceInterceptcorr–0.3646NaNNaN
valencevalencestd8.3923NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd12.344NaNNaN
EEInterceptcorr–0.9994NaNNaN
EEEEstd0.54430.32380.9149
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std4.8289NaNNaN
Appendix 7—table 4
Linear Mixed-Effects Model results fitting the average 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), 1 year later in May 2021 with less strict measures in place (n=31, context 2), and at the lift of the sanitary state of emergency in June 2022 (n=28, context 3).
UPD ~1 + context +valence + EE+confidence + age+gender + education +design + valence*context + (1 | subject) + (1+valence | subject) + (1+EE | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
18571933.8–906.51813
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept–5.0416.1791–0.81582280.41546–17.2167.1345
valence4.23711.67322.53242280.0120.940337.534
EE0.424320.115723.66682280.000310.19630.65233
confidence0.022250.03860.576872280.5646–0.05380.098252
age–0.00620.0317–0.19552280.8452–0.06860.0562
gender–0.47861.1083–0.43192280.6663–2.66251.7052
education–0.26860.2992–0.89782280.3703–0.85820.3210
design2.72782.26431.20472280.2296–1.7347.1894
Context 10.95162.69610.35292280.7245–4.3616.2641
Context 20.52822.71120.19482280.8457–4.8145.8703
Context 31.71661.81270.94702280.3446–1.8555.2883
Valence by context 1–7.38532.2942–3.21912280.0015–11.906–2.8647
Valence by context 2–5.58762.3627–2.3652280.0189–10.243–0.9321
Valence by context 3–2.10962.456–0.85902280.3913–6.94892.7297
Random effects covariance parameters (95% CIs):
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd1.8256NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.7322NaNNaN
valenceInterceptcorr–0.4591NaNNaN
valencevalencestd8.0804NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd12.4296.809722.687
EEInterceptcorr–0.99996NaNNaN
EEEEstd0.545490.327280.90917
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std5.6331NaNNaN
Appendix 7—table 5
Linear Mixed-Effects Model results fitting the average Belief Updates (UPD) in participants tested both before and during the pandemic (n=28).
UPD ~1 + context +valence + EE+confidence + age+gender + education +valence*context + (1 | subject) + (1+valence | subject) + (1+EE | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
830.29876.5–398.14796.29
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept10.3337.55181.36831030.1742–4.644325.31
valence4.04971.22193.31421030.001271.62636.4732
context–4.41011.4926–2.95461030.00388–7.3704–1.4499
EE0.088690.146640.60461030.54678–0.20220.37948
confidence0.001410.047680.029481030.97654–0.09320.09597
age–0.050150.08563–0.58561030.55941–0.21990.11968
gender3.52991.64242.14921030.033960.272536.7873
education–0.56490.42109–1.34151030.1827–1.40.27023
valence:context–7.66011.4923–5.13321031.35e-06–10.62–4.7005
Random effects covariance parameters (95% CIs):
Group: subject (28 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.2282e-07NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd1.42910.809972.5215
valenceInterceptcorr-1NaNNaN
valencevalencestd3.32491.95545.6537
Group: subject (28 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd8.274e-07NaNNaN
EEInterceptcorr–0.99994NaNNaN
EEEEstd3.0154e-08NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std7.83666.79149.0426
Appendix 7—table 6
Linear Mixed-Effects Model results fitting the average belief updates (UPD) in participants tested outside (n=58) and during (n=65) the pandemic, corrected for distance, and with estimation errors (EE) calculated based on the estimate for someone else (eBR).
UPD ~1 + context +valence + EE+confidence + distance +age + gender +education + design +valence*context + (1 | subject) + (1+valence | subject) + (1+EE | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1822.91889.2–892.441784.9
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept–8.28815.0873–1.62922310.1046–18.3111.7353
valence2.44091.29131.89022310.0600–0.10344.9852
context–0.78601.7408–0.45152310.6520–4.21592.6438
EE0.50570.12204.14592314.8e-050.26540.7460
confidence0.09480.03492.72022310.00700.02610.1635
distance–0.15820.1054–1.50132310.1347–0.36590.0494
age–0.00250.0274–0.09162310.9271–0.05660.0515
gender0.60550.95040.63712310.5247–1.26712.4781
education–0.24190.2640–0.91602310.3606–0.76210.2784
design0.91421.73010.52842310.5977–2.49454.3229
valence:context–5.09681.7627–2.89142310.0042–8.5698–1.6237
Random effects covariance parameters (95% CIs):
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd1.5868NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.9714NaNNaN
valenceInterceptcorr0.2994NaNNaN
valencevalencestd9.2418NaNNaN
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd1.1447NaNNaN
EEInterceptcorr–0.9956NaNNaN
EEEEstd0.1645NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std3.9480NaNNaN
Appendix 7—table 7
Linear Mixed-Effects Model results fitting the average confidence ratings in participants tested outside (n=58) and during (n=65) the pandemic.
confidence ~1 + context +valence + EE+age + gender +education + design +valence*context + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1908.61946.9–943.281886.6
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept61.0519.15526.66852331.86e-1043.01379.088
valence–0.47950.7288–0.65802330.5112–1.91540.9563
context14.1054.5243.11772330.00215.191523.018
EE–0.23980.1177–2.03762330.0427–0.4717–0.0079
age0.04510.07470.60372330.5466–0.10210.1923
gender–2.4152.5148–0.96032330.3379–7.36972.5397
education0.05630.68980.08162330.935–1.30271.4153
design3.85684.65030.82942330.4077–5.305213.019
valence:context0.09691.02560.09442330.9248–1.92372.1174
Random effects covariance parameters
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd11.89210.18813.88
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std7.69356.78288.7265
Appendix 7—table 8
Linear Mixed-Effects Model results fitting the average absolute Estimation Error (EE) in participants tested outside (n=58) and during (n=65) the pandemic.
EE ~1 + context +valence + confidence +age + gender +education + design +valence*context + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
15691607.3–773.481547
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept24.6023.58876.85532336.32e-1117.53131.672
valence–0.45500.4902–0.92822330.3543–1.42090.5108
context3.6861.68932.18192330.03010.35777.0143
confidence–0.04960.0289–1.71232330.0882–0.10670.0075
age0.04030.02721.48012330.1402–0.01330.0939
gender–1.0730.9181–1.16882330.2437–2.88180.7358
education–0.32960.2511–1.31282330.1906–0.82430.1651
design1.70371.69711.00392330.3165–1.645.0473
valence:context2.19420.66883.28082330.00120.87653.5119
Random effects covariance parameters
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd3.0462.12554.3652
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std5.18674.57175.8845
Appendix 7—table 9
Linear Mixed-Effects Model results fitting the average Learning Rates from the RL-like model in participants tested outside (n=58) and during (n=65) the pandemic.
LR ~1 + context +valence + age+gender + education +design + valence*context + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
–54.291–19.31937.146–74.291
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.51890.15463.35662360.00090.21440.8235
valence0.08560.01207.13512361.18e-110.06200.1092
context–0.04920.0797–0.61722360.5377–0.20620.1078
age–0.00050.0014–0.34042360.7339–0.00310.0022
gender–0.02870.0457–0.62672360.5315–0.11870.0614
education–0.02270.0126–1.80912360.0717–0.04740.0020
design0.02690.08220.32722360.7438–0.13510.1889
valence:context–0.03470.0164–2.11262360.0357–0.0671–0.0023
Random effects covariance parameters
Group: subject (122 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd0.220530.190160.25575
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.128080.112970.1452
Appendix 7—table 10
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).
LR ~1 + context +valence + age+gender + education +valence*context + (1 | subject)
 
Model fit statistics:
AICBICLogLikelihoodDeviance
–15.9548.512516.977–33.954
 
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.35290.18691.88871050.0617–0.01760.7234
valence0.07520.02343.21751050.00170.02880.1215
context–0.10240.0330–3.10111050.0025–0.1679–0.0369
age0.00100.00350.28761050.7742–0.00600.0080
gender0.18580.07042.63851050.00960.04620.3254
education–0.02160.0178–1.21301050.2279–0.05700.0137
valence:context–0.06160.0330–1.86621050.0648–0.12710.0039
 
Random effects covariance parameters
Group: subject (28 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd0.151670.106620.21576
 
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.174790.150260.20332
Appendix 7—table 11
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.
asymmetry ~1 + context +age + gender +education + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
–204.79–185.16109.39–218.79
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.02640.03230.8181170.415–0.03750.0903
context–0.040.0172–2.32021170.0221–0.0741–0.0058
age0.00060.00051.17521170.2423–0.00040.0016
gender0.00890.00452.00241170.04750.00010.0178
education–0.00480.0172–0.28151170.7788–0.03880.0291
Random effects covariance parameters
Group: subject (122 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd0.063415NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.063415NaNNaN
Appendix 7—table 12
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.
scaling ~1 + context +age + gender +education + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
10.51930.1471.7406–3.4813
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.5610.08586.53751171.71e-090.39110.731
context–0.07050.0458–1.54061170.1261–0.16120.0201
age–0.00040.0014–0.32631170.7448–0.00310.0022
gender–0.02140.0119–1.79931170.0745–0.04490.0022
education–0.02970.0456–0.65171170.5159–0.12010.0606
Random effects covariance parameters
Group: subject (122 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd0.16865NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.16865NaNNaN
Appendix 7—table 13
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).
asymmetry ~1 + context +age + gender +education + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
–138.71–124.5376.354–152.71
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.04710.04730.9969510.3235–0.04780.1420
context–0.06150.0165–3.7185510.0005–0.0947–0.0283
age0.00050.00090.6121510.5432–0.00120.0023
gender–0.00230.0176–0.1285510.8983–0.03760.0331
education0.00190.00450.4158510.6793–0.00710.0108
Random effects covariance parameters
Group: subject (28 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd9.1905e-09NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.061890.0514270.074482
Appendix 7—table 14
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).
scaling ~1 + context +age + gender +education + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
3.480917.6585.2595–10.519
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept0.35290.18801.8776510.0662–0.02440.7302
context–0.10240.0524–1.9554510.0560–0.20760.0027
age0.00100.00350.2876510.7748–0.00610.0081
gender0.18580.07042.6385510.01100.04440.3272
education–0.02160.0178–1.2130510.2307–0.05750.0142
Random effects covariance parameters
Group: subject (28 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd0.106920.0466890.24485
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std0.196010.150840.2547
Appendix 7—table 15
Sociodemographical data (N=123).
Ngenderageeducation level
Outside pandemic5832 females33.84±1.684.54±0.29
During pandemic6545 females42.32±2.354.59±0.19
Group tested before and during2818 females34.14±2.085.00±0.41

  1. Note: education is reported as years of higher education (university level).

Appendix 7—table 16
Linear Mixed-Effects Model results fitting the average number of paradoxical trials in participants tested outside (n=58) and during (n=65) the pandemic.
Nb of trials ~1 + valence +context + age+gender + education +design + context*valence + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1623.51644.6–805.761611.5
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept19.4273.0596.35082341.10e-0913.425.454
valence–1.97320.6083–3.24362340.0014–3.1717–0.7747
context–0.19041.57–0.12132340.9036–3.28362.9028
age0.00100.02600.04022340.9680–0.05010.0522
gender–0.14030.8746–0.16042340.8727–1.86331.5828
education0.00220.23970.00922340.9926–0.47010.4745
design0.12151.61770.07512340.9402–3.06563.3085
context:valence–2.35760.8300–2.84042340.0049–3.9928–0.7223
Random effects covariance parameters
Group: subject (123 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.8591e-15NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std6.43815.88937.038
Appendix 7—table 17
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.
Nb of shoot ~1 + type+context + EE+age + gender +education + design +context*type + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1623.51644.6–805.761611.5
Fixed effects coefficients
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept3.52292.01861.74522330.0823–0.45427.5001
type–1.07510.3611–2.97692330.0032–1.7866–0.3636
context0.21410.93900.22802330.8199–1.63592.064
EE–0.02880.0406–0.71022330.4783–0.10880.0511
age0.00790.01550.50992330.6106–0.02260.0384
gender–0.40390.5200–0.77662330.4382–1.42840.6207
education–0.41820.1428–2.92862330.0037–0.6995–0.1369
design1.40820.96121.46512330.1443–0.48553.3019
context:type1.66480.50033.32772330.00100.67922.6505
Random effects covariance parameters
Group: subject (123 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd2.2037e-06NaNNaN
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std3.81743.4924.1731
Appendix 7—table 18
Linear Mixed-Effects Model results fitting initial beliefs about the likelihood of adverse future life events for oneself (E1) and for others (eBR) in participants tested outside (n=58) and during (n=65) the pandemic.

Note the perspective regressor (coded 0 for E1 and 1 for eBR) tested if and how beliefs differed when assessed for oneself than for others.

Estimate ~1 + context +perspective + EE+confidence + age+gender + education +design + context*perspective + (1 | subject)
Model fit statistics:
AICBICLogLikelihoodDeviance
1630.41672.3–803.221606.4
Fixed effects coefficients (95% CIs):
NameEstimateSEtStatDFpValue95% CIs
LowerUpper
Intercept31.4726.68094.71082324.25e-0618.30944.635
perspective3.01940.85643.52572320.00051.33214.7067
context–1.91272.9993–0.63772320.5243–7.82213.9966
EE0.31460.12612.49552320.01330.06620.5629
confidence0.05980.03501.70692320.0892–0.00920.1287
age–0.08080.0481–1.67932320.09444–0.17560.0140
gender0.10481.62850.06432320.9488–3.10373.3133
education–0.68570.4443–1.54332320.1241–1.56120.1897
design0.27922.99990.09312320.9259–5.63136.1897
context: perspective0.06780.98110.06912320.9450–1.86522.0007
Random effects covariance parameters (95% CIs):
Group: subject (121 Levels)
Name1Name2TypeEstimate95% CIs
LowerUpper
InterceptInterceptstd8.00256.94439.2219
Group: Error
NameEstimate95% CIs
LowerUpper
Res Std3.75063.30334.2584

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  1. Iraj Khalid
  2. Orphee Morlaas
  3. Hugo Bottemanne
  4. Lisa Thonon
  5. Thomas Da Costa
  6. Philippe Fossati
  7. Liane Schmidt
(2025)
Effects of experiencing the COVID-19 pandemic on optimistically biased belief updating
eLife 13:RP101157.
https://doi.org/10.7554/eLife.101157.3