How susceptible a memory is to later modification might depend on how stable the episode has been encoded. This stability was proposed to increase when retrieving information more (vs. less) often and in a spaced (vs. massed) practice. Using fMRI, we examined the effects of these different pre-fMRI retrieval protocols on the subsequent propensity to learn from episodic prediction errors. After encoding a set of different action stories, participants came back for two pre-fMRI retrieval sessions in which they encountered original episodes either two or eight times in either a spaced or a massed retrieval protocol. One week later, we cued episodic retrieval during the fMRI session by using original or modified videos of encoded action stories. Recurrent experience of modified episodes was associated with increasing activity in the episodic memory network including hippocampal and cortical areas, when leading to false memories in a post-fMRI memory test. While this observation clearly demonstrated learning from episodic prediction errors, we found no evidence for a modulatory effect of the different retrieval protocols. As expected, the benefit of retrieving an episode more often was reflected in better memory for originally encoded episodes. In addition, frontal activity increased for episodic prediction errors when episodes had been less frequently retrieved pre-fMRI. A history of spaced versus massed retrieval was associated with increased activation throughout the episodic memory network, with no significant effect on behavioral performance. Our findings show that episodic prediction errors led to false memories. The history of different retrieval protocols was reflected in memory performance and brain responses to episodic prediction errors, but did not interact with the brain's episodic learning response.

Episodic memories are not static but can change on the basis of new experiences, potentially allowing us to make valid predictions in the face of an ever-changing environment. Recent research has identified prediction errors during memory retrieval as a possible trigger for such changes. In this study, we used modified episodic cues to investigate whether different types of mnemonic prediction errors modulate brain activity and subsequent memory performance. Participants encoded episodes that consisted of short toy stories. During a subsequent fMRI session, participants were presented videos showing the original episodes, or slightly modified versions thereof. In modified videos, either the order of two subsequent action steps was changed or an object was exchanged for another. Content modifications recruited parietal, temporo-occipital, and parahippocampal areas reflecting the processing of the new object information. In contrast, structure modifications elicited activation in right dorsal premotor, posterior temporal, and parietal areas, reflecting the processing of new sequence information. In a post-fMRI memory test, the participants' tendency to accept modified episodes as originally encoded increased significantly when they had been presented modified versions already during the fMRI session. After experiencing modifications, especially those of the episodes' structure, the recognition of originally encoded episodes was impaired as well. Our study sheds light onto the neural processing of different types of episodic prediction errors and their influence on subsequent memory recall.

Receiver operating characteristic (ROC) analysis is the standard tool for studying recognition memory. In particular, the curvilinearity and the y -offset of recognition ROC curves have been interpreted as indicative of either memory strength (single-process models) or different memory processes (dual-process model). The distinction between familiarity and recollection has been widely studied in cognitive neuroscience in a variety of conditions, including lesions of different brain regions. We develop a computational model that explicitly shows how performance in recognition memory is affected by a complex and, as yet, underappreciated interplay of various factors, such as stimulus statistics, memory processing, and decision-making. We demonstrate that (1) the factors in the model affect recognition ROC curves in unexpected ways, (2) fitting R and F parameters according to the dual-process model is not particularly useful for understanding the underlying processes, and (3) the variability of recognition ROC curves and the controversies they have caused might be due to the uncontrolled variability in the contributing factors. Although our model is abstract, its functional components can be mapped onto brain regions, which are involved in corresponding functions. This enables us to reproduce and interpret in a coherent framework the diverse effects on recognition memory that have been reported in patients with frontal and hippocampal lesions. To conclude, our work highlights the importance of the rich interplay of a variety of factors in driving recognition memory performance, which has to be taken into account when interpreting recognition ROC curves.