United we fall: All-or-none forgetting of complex episodic events

Bardur Hofgaard Joensen, Mark Gareth Gaskell, Aidan James Horner

Research output: Contribution to journalArticlepeer-review


Do complex event representations fragment over time, or are they instead forgotten in an all-or-none manner? For example, if we met a friend in a café and they gave us a present, do we forget the constituent elements of this event (location, person, and object) independently, or would the whole event be forgotten? Research suggests that item-based memories are forgotten in a fragmented manner. However, we do not know how more complex episodic, event-based memories are forgotten. We assessed both retrieval accuracy and dependency-the statistical association between the retrieval successes of different elements from the same event-for complex events. Across 4 experiments, we show that retrieval dependency is found both immediately after learning and following a 12-hr and 1-week delay. Further, the amount of retrieval dependency after a delay is greater than that predicted by a model of independent forgetting. This dependency was only seen for coherent "closed-loops," where all pairwise associations between locations, people, and objects were encoded. When "open-loops" were learned, where only 2 out of the 3 possible associations were encoded, no dependency was seen immediately after learning or after a delay. Finally, we also provide evidence for higher retention rates for closed-loops than for open-loops. Therefore, closed-loops do not fragment as a function of forgetting and are retained for longer than are open-loops. Our findings suggest that coherent episodic events are not only retrieved, but also forgotten, in an all-or-none manner.

Original languageEnglish
Pages (from-to)230-248
Number of pages19
JournalJournal of Experimental Psychology: General
Issue number2
Early online date15 Jul 2019
Publication statusPublished - 1 Feb 2020

Bibliographical note

© 2019, The Author(s).


  • Episodic memory
  • Forgetting
  • Hippocampus
  • Statistical modeling

Cite this