Network Representations of Causal Relations in Memory for Narrative Texts: Evidence from Primed Recognition

This study investigated how adult readers represent causal relations among events in a narrative. Existing models of text comprehension describe the representation either as a linear chain of text components that are adjacent in the surface structure of the text or as a network that includes relations between nonadjacent as well as adjacent text components. These models were tested in three priming experiments. In each experiment subjects read brief narratives and received a speeded-recognition test of their memories for story events. Each story could be represented either by a linear chain or by a network. On each trial in the recognition procedure, subjects read a priming sentence that reminded them of either a story (general prime) or a specific event in a story (specific prime). The specific primes were either causally related or unrelated to the subsequent target event and they were either adjacent or nonadjacent to the target in the surface structure of the text. Across the three experiments, positive responses were faster when the target followed a specific prime that was causally related than when it followed either a specific but unrelated prime or a general prime. Importantly, this was the case both when the specific prime and target were adjacent and when they were nonadjacent in the surface structure of the story. These results support a network model of the representation of causal relations in narratives; they are inconsistent with a linear chain model.