This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recent evidence suggests that grammatical aspect can bias how individuals perceive criminal intentionality during discourse comprehension. Given that criminal intentionality is a common criterion for legal definitions (e.g., first-degree murder), the present study explored whether grammatical aspect may also impact legal judgments. In a series of four experiments participants were provided with a legal definition and a description of a crime in which the grammatical aspect of provocation and murder events were manipulated. Participants were asked to make a decision (first- vs. second-degree murder) and then indicate factors that impacted their decision. Findings suggest that legal judgments can be affected by grammatical aspect but the most robust effects were limited to temporal dynamics (i.e., imperfective aspect results in more murder actions than perfective aspect), which may in turn influence other representational systems (i.e., number of murder actions positively predicts perceived intentionality). In addition, findings demonstrate that the influence of grammatical aspect on situation model construction and evaluation is dependent upon the larger linguistic and semantic context. Together, the results suggest grammatical aspect has indirect influences on legal judgments to the extent that variability in aspect changes the features of the situation model that align with criteria for making legal judgments.

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.