Sources and Destinations of Misattributions in Recall of Instances of Repeated Events

Eva Rubinova, Feni Kontogianni

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Abstract

Repeated experience of events promotes schema formation. Later activation of the schema facilitates recall of the general structure of the events, whereas attribution of details to instances requires systematic decision-making based on detail characteristics. For repeated events, source monitoring may be less effective due to the similarity and interference of details across instances and consequently result in source attribution errors. To date, researchers have examined aggregated misattributions across instances and have found that misattributions are more frequent in the middle than in the boundary instances. In this study, we investigated the trajectories of misattributions using data from six studies (N = 633), where participants recalled repeated interactive marketing-themed events (Study 1), mock-crime filmed events (Study 2), stories (Study 3), and categorized word lists (Studies 4–6). The patterns confirmed the expected primacy and recency effects, showing fewer misattributions from and to the boundary instances relative to the middle instances. In addition, the patterns indicated proximity effects: Confusions more frequently occurred across adjacent instances and gradually decreased for instances that were further apart from the source. Our findings suggest that detail characteristics that form the basis of source attribution decisions provide information about the relative position of instances in repeated events, where the boundary instances serve as anchors, and where confusion relatively easily occurs across neighbouring instances. In line with context-based models of memory, our findings indicate that a higher-level organization of repeated events that emerges at encoding guides retrieval and source monitoring decisions.

Original languageEnglish
JournalMemory and Cognition
DOIs
Publication statusPublished - 7 Apr 2022

Keywords

  • Misattribution
  • Repeated events
  • Source monitoring

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