Connecting the sample size of mental sampling with working memory capacity

The sampling framework has been proposed to provide an integrative perspective of how people make probability judgments. It posits that people approximate probabilities by drawing mental samples from memory or mental simulation. Sampling-based models have successfully reproduced a wide range of observed effects in probability judgments. Yet, they have also been criticized for lacking a robust coupling of model terms and psychological processes (Coenen, Nelson, & Gureckis, 2018). We addressed this critique by testing the positive association between an important model term – the sample size of mental sampling – and individual differences in working memory capacity (WMC). Such a relation has been widely assumed in the sampling framework (e,g., see Lloyd et al., 2019). Nevertheless, as far as we know, the validity of the assumption has yet to be investigated. Here we use the coherence of people’s probability judgments as a proxy of sample size, as larger samples are less vulnerable to sampling variability. Therefore, an empirical examination of the association between WMC and coherence would provide evidence for the assumed positive relation between WMC and sample size. To measure coherence in probability judgments, we adopted the novel event-ranking task proposed by Liu et al. (in prep). In such a task, participants are asked to provide rankings for different sets of events, each consisting of two pairs of complementary events, {A, not-A, B, and not-B}. A logically correct ranking follows the complement rule, such that when A is ranked above B, not-A is ranked below not-B. The probabilities of participants providing logically correct (versus incorrect) ranking would manifest the level of coherence in probability judgments. The present study critically examined the assumed link between the sample size of mental sampling and WMC, thereby contributing to the theory-testing of the sampling framework.