In this paper, I develop a model of confirmation bias, which arises from a cognitive mechanism conceptually linked to the Bayesian brain paradigm and the efficient coding hypothesis in neuroscience. Specifically, I hypothesize that cognitive effort - quantified as the statistical divergence between prior and posterior beliefs -influences the precision of the sampling process, thereby allowing the probability of misperception to emerge endogenously. Consequently, disconfirming evidence, which requires greater cognitive effort to process, attenuates the impact of new information during Bayesian updating, resulting in posterior mean beliefs that are disproportionately influenced by prior mean beliefs. To test the model's predictions, I conduct an experiment, and I find empirical support for the hypothesis: noise in perceived probabilities increases proportionally with the divergence between the decision-maker's initial expectations and the sample.

We develop a novel method that allows us to econometrically recover belief distributions from binary choices between bets on different events. We delpoy the method in a strategic context by studying the predictive power of the recovered beliefs about reciprocity on trust. We econometrically recover two measures: a measure of the mean belief of the decision-maker; and a measure of the subjective uncertainty surrounding that mean belief. We show that mean beliefs are a significant predictor of trusting behaviour. Belief uncertainty, however, plays an even more important role in explaining behaviour. We illustrate this by estimating the generalized Arrow-Pratt approximation of our `trust equivalents' under model uncertainty proposed by Maccheroni, Marinacci, and Ruffino (2014). We find aversion to model uncertainty (i.e., uncertainty characterizing the trustor's own belief distribution) to be the single most important driver of trust. This showcases the role of the dispersion of the belief distribution when it comes to explaining behaviour in strategic interactions, and supports multiple-prior models of decision-making under ambiguity.

I experimentally investigate the interplay between confirmation bias and base-rate neglect at the individual level. I hypothesize that both how subjects utilize information and their reliance on base-rates vary depending on whether the information confirms or contradicts the base-rate during belief updating. I test this hypothesis in a setup which enables the elicitation of subjects' posterior beliefs for both confirming vs disconfirming signals at each trial. The evidence supports the hypothesis: while a majority of subjects exhibits confirmation bias, they also show less severe base-rate neglect when the signal confirms the base-rate. I further evaluate the predictive accuracy and the explanatory power of the model that accounts for the dependence of both information and base-rate utilization on the signal type, comparing it with several alternative specifications. I find that, at the individual level, the former outperforms all other specifications, including the most commonly used model in the literature on asymmetric belief updating, which only considers signal-type dependence in how information is utilized. These findings provide empirical evidence of the coexistence and interaction between these two widespread biases and underscore the importance of correctly accounting for the base-rate utilization in belief updating.