Talk

Event schemas are knowledge structures pertaining to event types whose roles in our cognitive lives include guiding behavior, facilitating the encoding of new information, scaffolding memory retrieval processes (Ghosh & Gilboa 2014), and supporting the construction of episodic future thought representations (Addis 2020). They achieve these functions by associatively storing information about the typical ways in which events of a given type unfold, including the kinds of agents, objects, and subevents they typically involve, etc. Given that event schemas are, by definition, understood to result from experiences of multiple similar events that capture their general commonalities (Robin & Moscovitch 2017), they are naturally taken, in semantic terms, to have sets of events as their extensions. This, in turn, raises the question of how these extensions are determined. Given the aforementioned involvement of event schemas in the construction of episodic memory and episodic future thought representations, this question becomes especially pressing, as it may bear directly on the recently emerging debate over how reference is fixed in such representations (Openshaw & Michaelian 2024; Langland-Hassan 2024). The main aim of my talk is to offer an account of the semantics of event schemas. On my view, a given event schema refers to the set of events that it characterizes better than any other schema possessed by the organism. This notion of best characterization can be operationalized in terms of prediction error: a schema best characterizes an event if its application would yield the smallest prediction error among the organism’s available schemas. Additional support for this proposal comes from the leading model of event cognition, “Event Segmentation Theory” (Radvansky & Zacks 2011), according to which it is precisely prediction error exceeding a certain threshold that triggers the updating of the event model currently maintained in working memory.

Zoom Link https://ruhr-uni-bochum.zoom-x.de/j/65197432064?pwd=vGaaTaJxehoTyVc1Yopa6q14xAbv9Y.1