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Abstract
Although our ability to remember future simulations conveys an adaptive advantage, enabling us to better prepare for upcoming events, the factors influencing the memorability of future simulations are not clear. In this study, participants generated future simulations that combined specific people, places and objects from memory, and for each trial, made a series of phenomenological ratings about the event components and the simulation as a whole. Memory for simulations was later assessed using a cued-recall test. We used multilevel modelling to determine whether the phenomenological qualities of event components (familiarity, emotionality and significance) and simulations (detail, plausibility) were predictive of whether the simulation was successfully encoded and later accessible. Our results demonstrate that person familiarity, detail and plausibility were significant predictors of whether a given future simulation was encoded into memory and later accessible. These findings suggest that scaffolding future simulations with pre-existing episodic memories is the path to a memorable future.
This research was supported by a Rutherford Discovery Fellowship [grant number RDF-10-UOA-024] awarded to D.R.A. and a New Zealand International Doctoral Research Scholarship to V.C.M. D.L.S. was supported by a National Institute of Mental Health grant [grant number MH060941]. We would like to thank Chris Sibley for helpful advice regarding the HLM analyses.
This research was supported by a Rutherford Discovery Fellowship [grant number RDF-10-UOA-024] awarded to D.R.A. and a New Zealand International Doctoral Research Scholarship to V.C.M. D.L.S. was supported by a National Institute of Mental Health grant [grant number MH060941]. We would like to thank Chris Sibley for helpful advice regarding the HLM analyses.
Notes
1 Mean component familiarity ratings also significantly predicted the amount of detail in the simulation (β = .237; SE = .054; t(20) = 4.36, p < .001), replicating the effect demonstrated by D'Argembeau and Van der Linden (Citation2012), while mean emotionality (β = .092; SE = .093; t(20) = 0.99, p = .334) and mean significance (β = .019; SE = .068; t(20) = 0.28, p = .78) did not significantly predict simulation detail.
2 Model coefficients and statistics for the significant model are provided in ; for completeness, the coefficient and statistics for this nonsignificant model are as follows: intercept (SE = .177; t(20) = 1.289, p = .212; OR = 1.256); detail (β = .738; SE = .102; t(20) = 7.256, p < .001; OR = 2.091); plausibility (β = .297; SE = .076; t(20) = 3.924, p < .001; OR = 1.345); person familiarity (β = .295; SE = .078; t(20) = 3.768, p = .001; OR = 1.343) and location familiarity (β = .143; SE = .067; t(20) = 2.312, p = .046; OR = 1.154).
