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Prediction of image memorability has attracted interest in various fields.Consequently, the prediction accuracy of convolutional neural network (CNN)models has been approaching the empirical upper bound estimated based on humanconsistency. However, identifying which feature representations embedded in CNNmodels are responsible for the high memorability prediction accuracy remains anopen question. To tackle this problem, we sought to identifymemorability-related feature representations in CNN models using brainsimilarity. Specifically, memorability prediction accuracy and brain similaritywere examined across 16,860 layers in 64 CNN models pretrained for objectrecognition. A clear tendency was observed in this comprehensive analysis thatlayers with high memorability prediction accuracy had higher brain similaritywith the inferior temporal (IT) cortex, which is the highest stage in theventral visual pathway. Furthermore, fine-tuning of the 64 CNN models formemorability prediction revealed that brain similarity with the IT cortex atthe penultimate layer positively correlated with the memorability predictionaccuracy of the models. This analysis also showed that the best fine-tunedmodel provided accuracy comparable to state-of-the-art CNN models developed formemorability prediction. Overall, the results of this study indicated that theCNN models' great success in predicting memorability relies on featurerepresentation acquisition, similar to the IT cortex. This study advances ourunderstanding of feature representations and their use in predicting imagememorability.

Feature representations useful for predicting image memorability