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

ABSTRACT: Humans can quickly 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). Importantly, models with sequential lateral-recurrent in

SUBMITTER: Sorensen LKA 

PROVIDER: S-EPMC10306191 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature