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Lateralized processing of emotional images: A left hemisphere memory deficit.

Ella K MoeckNicole A ThomasMelanie K T Takarangi
Published in: Emotion (Washington, D.C.) (2018)
The right hemisphere plays a critical role in visuospatial attention and emotional perception, particularly for negative emotions. Therefore, preferential processing of emotional stimuli by the right, compared to the left, hemisphere could enhance our memory for emotional stimuli. We examined whether recognition memory for negative versus neutral IAPS images (Experiment 1) and negative versus positive IAPS and NAPS images (Experiment 2) differed depending on initial right or left hemisphere processing-manipulated by presenting images in the left (i.e., right hemisphere) or the right (i.e., left hemisphere) visual field. We tested recognition memory for valence-matched image pairs encoded for 500 ms. We manipulated image valence and visual field of presentation within-participants. In Experiment 1, valence and visual field interacted to influence recognition memory: people recognized negative images encoded from the left visual field (right hemisphere) more accurately than negative images encoded from the right visual field (left hemisphere). There were no visual field differences for neutral images. In Experiment 2, recognition memory for positive and negative images was equally enhanced when these images appeared in the left compared to the right visual field. Our findings suggest preferential right hemisphere processing of visuospatial and emotional information enhances recognition memory for emotional images. We interpret these findings as a left hemisphere memory deficit for emotional images, because right hemisphere processing led to similar recognition memory of emotional as neutral images, whereas left hemisphere processing led to worse recognition memory of emotional than neutral images. These findings aid our understanding of how lateralized functions contribute to emotional processing. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Keyphrases
  • deep learning
  • convolutional neural network
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