Causal inference: relating language to event representations and events in the world

Events are not isolated but rather linked to one another in various dimensions. In language processing, various sources of information—including real-world knowledge, (representations of) current linguistic input and non-linguistic visual context—help establish causal connections between events. In this review, we discuss causal inference in relation to events and event knowledge as one aspect of world knowledge, and their representations in language comprehension. To evaluate the mechanism and time course of causal inference, we gather insights from studies on (1) implicit causality/consequentiality as a specific form of causal inference regarding the protagonists of cause/consequence events, and (2) the processing of causal relations. We highlight the importance of methodology in measuring causal inference, compare the results from different research methods, and emphasize the contribution of the visual-world paradigm to achieve a better understanding of causal inference. We recommend that further investigations of causal inference consider temporally sensitive measures and more detailed contexts.