Article Figures & Data
Figures
- Figure 1.
Outline and examples of experimental trials. Each experimental trial consisted of four succeeding events. With respect to a specific adverse life event (e.g., suffering from cancer), subjects had to estimate the BR (eBR) and their own risk (E1). They were then presented with the actual BR and had the opportunity to estimate their own risk again (E2). After identical eBR and E1, the upper progression of the hypothetical trial example shows a BR lower than expected indicating good news, whereas the lower progression shows a BR higher than expected indicating bad news. EEs corresponded to the difference between the eBR and the actual BR and the update corresponded to the difference between the first and the second self-risk estimate. Note that, in both trial examples, the EE is 10 and the update is 8. For eBR, E1, and E2, subjects were instructed to use response buttons to adjust the displayed number to match their estimate as soon as the number font changed to green (after 2 s). Interstimulus intervals between eBR, E1, and E2, as well as intertrial intervals after E2, were jittered and consisted of a fixation cross (not shown here).
- Figure 2.
Task performance and computational modeling. A, Bars show subjects' updates, that were significantly larger after good news (GOOD) than after bad news (BAD). White dots represent simulations of updates by the “biased” computational model that assumes asymmetric learning rates for good and bad news (αA, two free parameters, α and A). Gray dots indicate simulated updates resulting from the “unbiased” model that assumes identical learning rates for good and bad news (α, one free parameter, α). The simulated unbiased updates provide a normative benchmark for rational updating with learning rates estimated for each subject under consideration of her or his exact trial history. Error bars indicate SEs. B, Bayesian model comparison confirmed that the biased model αA best predicted subjects' updates. Model frequencies show that the majority of subjects were best described by the αA model above and beyond chance (red dashed line). Error bars indicate the posterior variance. C, Learning rates extracted from the winning model αA were significantly higher after good than bad news. Error bars indicate SEs. D, Optimism bias (updateGOOD − updateBAD) and A (estimated for each subject by the model αA) were significantly correlated (dots represent single subjects). E, F, Correlations between task variables separately for trials with good news (E) and those with bad news (F). *p < 0.05, **p < 0.01.
- Figure 3.
Brain regions encoding errors and the valence of belief updating. A, When being confronted with the actual BR, errors in BR estimation (weighted by the PR) were tracked by the ACC, the IFG, the anterior insula, the middle orbital gyrus and the dlPFC. The line chart shows that the activity in the dlPFC (representative of all clusters) increased with decreasing error size (parametric modulation by error, negative correlation). Of all the involved regions, only in the dlPFC did the magnitude of the error tracking correlate with the general learning rate component α (see scatter plot). Therefore, subjects with a stronger error tracking in the dlPFC also more strongly adjusted their initial beliefs in response to errors. B, During the second risk estimation, the activity in the vmPFC encoded the valence of updating, adjusted for EE and PR. The gray box schematically illustrates the opposed valences of increasing updates after good and bad news (in this example, eBR = E1). After good news, large updates are favorable because they ultimately change beliefs toward lower risk estimates and small updates are unfavorable because they let the opportunity to improve risk estimates pass by. In contrast, after bad news, large updates are unfavorable because they ultimately change beliefs toward higher risk estimates and small updates are favorable because they prevent worsening of risk estimates. Resulting valences are summarized in the table below: unfavorable (U), mid (M), and favorable (F) updates. The line chart shows that the activity in the vmPFC tracked the positive valence because it increased with increasing update sizes after good news but decreased with increasing update sizes after bad news. The scatter plot shows that subjects with a stronger optimism bias also demonstrated a greater tracking of favorable updating in the vmPFC. In A and B, the line charts and the scatter plots were not used for statistical inference (which was performed in parametric modulation and covariate analyses within the SPM framework); they are shown solely for illustrative purposes. C, After demonstrating the valence effect with the more precise parametric modulation analysis presented in B, a simplified analysis of updating was conducted as a basis for DCM. Here, all trials were assigned to three valence categories: those with unfavorable (U), mid (M), and favorable (F) updates (adjusted for EE). Conjunction across these three categories revealed a distributed network involved in general updating, overlapping with the error tracking effect in the dlPFC. Comparing trials with favorable and unfavorable updates revealed the differential recruitment of the vmPFC and the dmPFC during updating. The line charts show contrast estimates in the dlPFC, vmPFC, and dmPFC, respectively.
- Figure 4.
Neurocircuitry mechanisms underlying optimistic belief updating. A, Ten different dynamic causal models varying in intrinsic connectivity and contextual modulation (unfavorable and favorable updating, U and F) were specified. The model space encompassed three brain regions involved in updating: dlPFC, vmPFC, and dmPFC. B, Bayesian model selection revealed that the model m1 best explained subjects' BOLD signal above and beyond chance (red dashed line). In this model, the coupling between dlPFC and vmPFC was differentially modulated by unfavorable and favorable updating. Therefore, the vmPFC filtered the incoming information in a valence-dependent manner and furthermore influenced the dmPFC. C, Connectivity parameters derived from m1 show that the coupling between dlPFC and vmPFC tended to be weaker in the context of unfavorable relative to favorable updating. D, Optimism bias correlated with two parameters of m1 (highlighted in red): differential modulation of the dlPFC-vmPFC connection by favorable versus unfavorable updating (F-U) and the strength of the vmPFC-dmPFC connection (vmPFC::dmPFC). Therefore, subjects with a stronger optimism bias also demonstrated a greater valence-dependent filtering of incoming information by vmPFC and a greater transmission of this differential signal further to dmPFC.
Tables
Parameter M (SD) p Source Good news Bad news Number of trials 39.96 (1.45) 38.21 (2.38) 0.024 Estimated base rate (eBR) 49.74 (12.67) 45.92 (12.30) 0.000 Participants' response First estimate (E1) 42.64 (11.10) 37.85 (10.49) 0.000 Participants' response Presented base rate (BR) 36.35 (12.57) 59.76 (12.22) 0.000 Base rate algorithm Estimation error (EE) 13.39 (0.95) 13.84 (0.57) 0.001 EE = |eBR − BR| Second estimate (E2) 35.12 (10.86) 44.55 (11.26) 0.000 Participants' response Update 7.51 (2.58) 6.70 (2.20) 0.045 UpdateGOOD = E1 − E2, UpdateBAD = E2 − E1 Personal relevance (PR) 0.70 (0.12) 0.69 (0.13) 0.287 for E1 < eBR: PR = 1 − ((eBR − E1)/(eBR − 1)) for E1 > eBR: PR = 1 − ((E1 − eBR)/(99 − eBR)) for E1 = eBR: PR = 1 RT eBR (s) 5.19 (0.84) 5.11 (0.80) 0.192 Participants' response RT E1 (s) 3.25 (0.91) 3.30 (0.90) 0.461 Participants' response RT E2 (s) 2.70 (0.62) 2.56 (0.61) 0.018 Participants' response All measures (except for number of trials) were recorded or computed for each trial and were then averaged separately for the conditions GOOD and BAD and separately for each participant. Positive update values indicated updates toward the BR and negative values updates away from the BR (<3% of the trials). PR: 1 indicates equal risk perception for the average and oneself and 0 indicates maximally different risk perception for the average and oneself; note that PR corresponds to “relative personal knowledge” in Kuzmanovic and Rigoux, 2017. RT, Reaction time. p-values refer to paired two-tailed paired t test with n = 24.
- Table 2.
Error coding during base rate presentation and its relation to the learning rate component alpha
Cluster size Peak pFWE-corr T x y z (1) Parametric modulation of BRGOOD and BRBAD by error (a) Conjunction: PM_errorGOOD and PM_errorBAD, positive correlation No significant results (b) Conjunction: PM_errorGOOD and PM_errorBAD, negative correlation Anterior cingulate cortex 63 0.000 5.18 10 34 20 Inferior frontal gyrus (p. triangularis) 45 0.001 4.09 46 44 0 Anterior insula 37 0.001 4.05 28 22 6 Middle orbital gyrus 30 0.002 4.00 16 50 −2 dlPFCCOV_Alpha 32 0.004 3.81 40 40 28 (c) PM_errorGOOD > PM_errorBAD No significant results (d) PM_errorBAD > PM_errorGOOD Cerebellum 48 0.001 6.90 −18 −76 −46 Middle occipital gyrus 83 0.005 6.17 −34 −84 28 Superior parietal lobule 47 0.006 6.15 24 −56 48 Conjunction: PM_errorBAD, positive correlation and PM_errorGOOD, negative correlation Inferior occipital gyrus 22 0.001 4.16 40 −84 −10 (2) Covariate analysis of error coding with alpha (masked with contrast 1b) dlPFC 12 0.008 4.46 40 38 30 Error = EE * PR, based on the computational modeling of task performance. For significant differences between PM_errorGOOD and PM_errorBAD, we report global conjunction results to clarify whether the difference relates to different magnitudes of the same modulation effect (e.g., the positive correlation between BOLD and error was stronger in BAD than in GOOD) or to modulation effects of opposite direction (e.g., the correlation between BOLD and error was positive in BAD, but negative in GOD). COV_Alpha indicates that in this cluster the magnitude of the error tracking correlated with the learning rate component alpha across subjects (covariate analysis). Peak coordinates refer to the MNI space.
Cluster size Peak pFWE-corr T x y z (1) Parametric modulation of E2GOOD and E2BAD by update (a) PM_updateGOOD > PM_updateBAD vmPFC 49 0.000 5.63 −12 44 −16 Conjunction: PM_updateGOOD, positive correlation & PM_updateBAD, negative correlation 0.010 5.40 −6 44 −20 vmPFC 44 0.000 3.67 −8 46 −18 0.020 3.35 −10 54 −12 (b) PM_updateBAD > PM_updateGOOD No significant results (c) Conjunction: PM_updateGOOD and PM_updateBAD, positive correlation No significant results (d) Conjunction: PM_updateGOOD and PM_updateBAD, negative correlation Fusiform gyrus (V4) 816 0.000 7.33 28 −72 −8 Lingual gyrus (V1) 0.000 4.59 6 −72 2 Lingual gyrus (V3) 736 0.000 6.70 −10 −86 −6 Superior occipital gyrus (V3) 0.000 4.11 −16 −88 18 Superior occipital gyrus 231 0.000 5.17 22 −80 20 Precentral gyrus 494 0.000 4.54 −32 −18 64 Postcentral gyrus 0.000 4.34 −42 −26 54 Fusiform gyrus 25 0.010 3.53 24 −46 −14 (2) Covariate analysis of valence coding with optimism bias (a) Masked with contrast 1a, conjunction vmPFC 39 0.000 15.59 −6 50 −18 (b) Whole brain vmPFC 14 0.011 7.28 −2 48 −18 (3) Three categories of E2: unfavorable, mid, favorable (a) E2favorable > E2unfavorable vmPFCDCM 27 0.000 5.84 −2 46 −22 Dorsomedial prefrontal cortex 30 0.020 5.39 −16 44 40 (b) E2unfavorable > E2favorable No significant results (c) Conjunction: all 3 categories of E2 Lingual gyrus (V3) 57571 0.000 24.00 24 −86 −12 Fusiform gyrus 0.000 18.90 −30 −58 −14 Lingual gyrus (V4) 0.000 18.70 −24 −86 −14 Fusiform gyrus 0.000 16.40 32 −50 −18 Inferior parietal lobule 0.000 14.80 −44 −40 48 Inferior parietal lobule 0.000 12.60 50 −34 48 Thalamus 0.000 8.38 20 −30 −2 Middle frontal gyrus 6959 0.000 10.50 42 2 60 Inferior frontal gyrus (p. opercularis) 0.000 9.62 46 10 36 dlPFCDCM 0.000 8.99 44 42 26 dlPFC 1414 0.000 8.46 −28 52 28 dlPFC 0.000 8.21 −40 30 32 Inferior frontal gyrus (p.triangularis) 0.000 7.44 −34 34 24 Thalamus 136 0.000 6.58 −8 −22 8 Posterior cingulate cortex 125 0.000 6.13 −2 −24 28 Precentral gyrus 23 0.010 5.65 34 −28 72 For significant differences between PM_updateGOOD and PM_updateBAD, we report global conjunction results to clarify whether the difference relates to different magnitudes of the same modulation effect (e.g., the positive correlation between BOLD and update was stronger in GOOD than in BAD), or to modulation effects of opposite direction (e.g., the correlation between BOLD and update was positive in GOOD, but negative in BAD). Peak coordinates refer to the MNI space.
- Table 4.
DCM parameter estimates of the model m1 and correlations with measures of optimism bias
M (SD) p, t test r, optimism bias p r, asymmetry p Matrix A dl::dl −0.01 (0.09) 0.000* −0.19 0.361 −0.21 0.323 dl::vm −0.06 (0.12) 0.030 −0.23 0.277 −0.05 0.819 vm::dl 0.10 (0.15) 0.000* −0.09 0.679 −0.15 0.485 vm::vm −0.07 (0.09) 0.000* −0.20 0.348 −0.20 0.342 vm::dm 0.15 (0.14) 0.000* 0.47 0.020* 0.49 0.015 dm::vm 0.08 (0.124) 0.004* 0.27 0.207 0.32 0.127 dm::dm −0.02 (0.03) 0.001* −0.49 0.015 −0.56 0.005 Matrix B U on dl::vm −0.35 (1.06) 0.123 −0.23 0.275 −0.31 0.147 F on dl::vm 0.05 (1.00) 0.822 0.49 0.015 0.36 0.088 F - U 0.39 (1.42) 0.188 0.52 0.009* 0.48 0.018 Matrix C U M F to dl 0.12 (0.07) 0.000 0.20 0.340 0.13 0.541 Parameter estimates are shown in Hertz, self-connections were log-transformed.
dl, Dorsolateral prefrontal cortex; vm, ventromedial prefrontal cortex; dm, dorsomedial prefrontal cortex; “::,” endogenous connection; U, unfavorable updating; M, mid-updating; F, favorable updating.
↵*Equivalent to p < 0.05, Bonferroni-corrected for multiple comparisons (Matrix A, t test, p < 0.007 corrected for 7 comparisons; r, optimism bias, p < 0.025 corrected for 2 comparisons with a priori hypotheses).
