Updating Beliefs under Perceived Threat

Abstract

Humans are better at integrating desirable information into their beliefs than undesirable information. This asymmetry poses an evolutionary puzzle, as it can lead to an underestimation of risk and thus failure to take precautionary action. Here, we suggest a mechanism that can speak to this conundrum. In particular, we show that the bias vanishes in response to perceived threat in the environment. We report that an improvement in participants' tendency to incorporate bad news into their beliefs is associated with physiological arousal in response to threat indexed by galvanic skin response and self-reported anxiety. This pattern of results was observed in a controlled laboratory setting (Experiment I), where perceived threat was manipulated, and in firefighters on duty (Experiment II), where it naturally varied. Such flexibility in how individuals integrate information may enhance the likelihood of responding to warnings with caution in environments rife with threat, while maintaining a positivity bias otherwise, a strategy that can increase well-being.SIGNIFICANCE STATEMENT The human tendency to be overly optimistic has mystified scholars and lay people for decades: How could biased beliefs have been selected over unbiased beliefs? Scholars have suggested that although the optimism bias can lead to negative outcomes, including financial collapse and war, it can also facilitate health and productivity. Here, we demonstrate that a mechanism generating the optimism bias, namely asymmetric information integration, evaporates under threat. Such flexibility could result in enhanced caution in dangerous environments while supporting an optimism bias otherwise, potentially increasing well-being.

Keywords: anxiety; information processing; optimism; risk; stress; threat.

Figure 1.

Behavioral task. On each trial, participants were presented with a short description of an adverse event and asked to estimate how likely this event was to occur to them in the future. They were then presented with the probability of that event occurring to someone from the same age, location, and socioeconomic background as them. The second session was the same as the first except that the average probability of the event to occur was not presented. Shown are examples of trials for which the participant's estimate was higher or lower than the statistical information provided leading to receipt of good news (left) and bad news (right), respectively. Note that the blue and red boxes are just for illustration and did not appear in the actual experiment.

Figure 2.

Manipulation check. Measures of self-reported state anxiety (a), skin conductance (b), and cortisol levels (c) were greater after manipulation relative to before in the threat manipulation group compared with the control group. Time points for cortisol measurements are as follows: t0, before threat/control manipulation procedure; t1, immediately after threat/control manipulation procedure, before undertaking the task (+10 min from t0); t2, halfway through the task (+30 min from t0); t3, after completion of task and postexperiment questionnaires (+1 h from t0). **p < 0.01 independent/paired sample ttest as appropriate; error bars represent SEM.

Figure 3.

Bias in information integration parameters vanishes under threat manipulation. Whereas the control group showed asymmetrical information integration parameters (α) in response to good and bad news, this bias vanished in the threat manipulation group because of an increase in α_B (information integration parameter for bad news). The group * valence interaction was significant, controlling for all covariates identified in Table 1 (see Materials and Methods). *p < 0.05 independent/paired sample t test as appropriate; **p < 0.01 independent/paired-sample t test as appropriate; error bars represent SEM.

Figure 4.

Greater integration of bad news related to self-reported anxiety and SCL. After the manipulation, an increase in both self-reported anxiety (a; b_i = 0.049, p = 0.003, η_p² = 0.25) and SCL (b; b_i = 0.076, p = .042, η_p² = 0.13) was related to larger information integration from bad news, correcting for possible confounds. Plotted are the partial regression plots from two linear models (one for self-report and one for SCL) that control for additional covariates.

Figure 5.

State anxiety in firefighters differentially relates to integration of good and bad news. Subjective state anxiety scores of firefighters on shift were related to larger information integration from bad news (b_i = 0.03, p = 0.008, η_p² = 0.26) and lower information integration from good news (b_i = −0.045, p = 0.038, η_p² = 0.17), correcting for possible confounds. Plotted are the partial regression plots for bad news (a; partial α_B) and good news (b; partial α_G) from two separate linear models (one for bad news and one for good news) that control for additional covariates.