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bioRxiv · 10.1101/2022.04.06.487259

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

Sörensen, L. K. A.·Bohte, S. M.·De Jong, D.·Slagter, H. A.·Scholte, H. S.
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Abstract

Humans can rapidly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed deep learning models for dynamic recognition and compared different computational mechanisms, contrasting feedforward and recurrent, single-image and sequential processing as well as different forms of adaptation. We found that only models that integrate images sequentially via lateral recurrence mirrored human performance (N=36) and were predictive of trial-by-trial responses across image durations (13-80 ms/image) while also displaying a temporal correspondence. Augmenting this model with adaptation markedly improved dynamic recognition and accelerated its representational dynamics, thereby predicting human trial-by-trial responses using fewer processing resources. These findings provide new insights into the mechanisms rendering object recognition so fast and effective in a dynamic visual world.

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Sörensen, L. K. A., Bohte, S. M., De Jong, D., Slagter, H. A., Scholte, H. S.. 2022-04-10. Mechanisms of human dynamic object recognition revealed by sequential deep neural networks. https://doi.org/10.1101/2022.04.06.487259

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