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Contributions of Arousal, Attention, Distinctiveness, and Semantic Relatedness to Enhanced Emotional Memory: An Event-Related Potential and Electrocardiogram Study.

Vanessa C ZarubinTimothy K PhillipsEileen RobertsonPaige G Bolton SwaffordTaylor BungeDavid AguillardCarolyn MartsbergerKatherine R Mickley Steinmetz
Published in: Affective science (2020)
Enhanced emotional memory (EEM) describes memory benefits for emotional items, traditionally attributed to impacts of arousal at encoding; however, attention, semantic relatedness, and distinctiveness likely also contribute in various ways. The current study manipulated arousal, semantic relatedness, and distinctiveness while recording changes in event-related potentials and heart rate during memory encoding. Trials were classified as remembered or forgotten by immediate recall performance. Negative images were remembered significantly better than neutral, and related neutral images were remembered significantly better than unrelated neutral images. Higher P300 and late positive potential (LPP) amplitudes were associated with memory for negative images as compared with related neutral images, suggesting that negative images received additional attentional processing at encoding, and that this cannot be accounted for only by the inherent relatedness of negative stimuli. No encoding benefits were found for related neutral images though they were better remembered than unrelated neutral images, indicating retrieval dynamics impacted memory. When image types were intermixed, greater heart rate changes occurred, and negative and unrelated neutral images received increased elaborative processing as compared with related neutral images, perhaps due to the prioritization of encoding resources. These results suggest encoding and retrieval processes contribute to EEM, with emotional items benefiting additively.
Keyphrases
  • deep learning
  • convolutional neural network
  • heart rate
  • working memory
  • optical coherence tomography
  • heart rate variability
  • blood pressure
  • machine learning
  • climate change