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Deep neural network decodes aspects of stimulus-intrinsic memorability inaccessible to humans.

Some stimuli are more memorable than others. Humans have demonstrated partial access to the properties that make a given stimulus more or less memorable. Recently, a deep neural network named ResMem was shown to successfully decode the memorability of visual stimuli as well. However, it remains unknown whether ResMem's predictions of memorability reflect the influence of stimulus-intrinsic properties or other stimulus-extrinsic factors that are known to induce interindividual consistency in memory performance (e.g., interstimulus similarity). Additionally, it is not clear whether ResMem and humans share access to overlapping properties of memorability. Here, in three experiments, we show that ResMem predicts stimulus-intrinsic memorability independent of stimulus-extrinsic factors, and that it captures aspects of memorability that are inaccessible to human observers. Taken together, our results confirm the multifaceted nature of memorability and establish a method for isolating its aspects that are largely inaccessible to humans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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