The present study aimed to adapt the actively open-minded thinking about evidence (AOT-E) scale to Brazilian Portuguese and assess its psychometric properties and nomological network in a Brazilian sample. It begins by investigating the underlying content structure of the AOT-E in its original form. Results from an exploratory factor analysis (EFA) of secondary data serve as the basis for the proposed adaptation, which is subjected to both EFA and confirmatory factor analysis (CFA). A total of 718 participants from various regions of Brazil completed an online survey that included the AOT-E, along with other instruments that allowed for an assessment of the scale’s nomological network, including measures of science literacy, attitude toward science, conspiracy beliefs, and religiosity. The EFA and CFA of both the original and Brazilian samples suggested a unidimensional solution. Despite the differences found in a multigroup CFA, polychoric correlations provided evidence of expected nomological relationships that replicate international findings. Overall, this study contributes to expanding the availability of adapted and validated research instruments in non-WEIRD samples.

First-order stochastic dominance is a core principle in rational decision-making. If lottery A has a higher or equal chance of winning an amount $x $ or more compared to lottery B for all x, and a strictly higher chance for at least one $x $, then A should be preferred over B. Previous research suggests that violations of this principle may result from failures in recognizing coalescing equivalence. In Expected Utility Theory (EUT) and Cumulative Prospect Theory (CPT), gambles are represented as probability distributions, where probabilities of equivalent events can be combined, ensuring stochastic dominance. In contrast, the Transfer of Attention Exchange (TAX) model represents gambles as trees with branches for each probability and outcome, making it possible for coalescing and stochastic dominance violations to occur. We conducted two experiments designed to train participants in identifying dominance by splitting coalesced gambles. By toggling between displays of coalesced and split forms of the same choice problem, participants were instructed to recognize stochastic dominance. Despite this training, violations of stochastic dominance were only minimally reduced, as if people find it difficult—or even resist—shifting from a trees-with-branches representation (as in the TAX model) to a cognitive recognition of the equivalence among different representations of the same choice problem.