In today's digital age, we are bombarded with images from the internet, social media, and online magazines. It is fascinating how we can remember so many of these images and their details. However, not every image is equally memorable; some stay with us more than others. Scientists have explored why this is the case. In our research, we are particularly interested in how images that showcase Iranian life and culture stick in the memories of Iranian adults. To investigate this, we created a new collection called the SemMem dataset, which is full of culturally relevant images. We adapted a memory game from earlier studies to test how memorable these images are. To analyze memorability, we used two deep learning architectures, ResNet 50 and ResNet 101. These architectures helped us estimate which images are likely to be remembered. Our findings confirmed that images connected to Iranian culture are indeed more memorable to Iranians, highlighting the impact of familiar cultural elements on memory retention.
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Shokri, A., & Yaghmaee, F. (2023). Understanding Image Memorability through Localized Stimuli. Modeling and Simulation in Electrical and Electronics Engineering, 3(2), 1-6. doi: 10.22075/mseee.2024.33159.1143
MLA
Amir Shokri; Farzin Yaghmaee. "Understanding Image Memorability through Localized Stimuli", Modeling and Simulation in Electrical and Electronics Engineering, 3, 2, 2023, 1-6. doi: 10.22075/mseee.2024.33159.1143
HARVARD
Shokri, A., Yaghmaee, F. (2023). 'Understanding Image Memorability through Localized Stimuli', Modeling and Simulation in Electrical and Electronics Engineering, 3(2), pp. 1-6. doi: 10.22075/mseee.2024.33159.1143
VANCOUVER
Shokri, A., Yaghmaee, F. Understanding Image Memorability through Localized Stimuli. Modeling and Simulation in Electrical and Electronics Engineering, 2023; 3(2): 1-6. doi: 10.22075/mseee.2024.33159.1143
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