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Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception.

Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures.

Here are the five models that we used, in increasing order of adversarialrobustness: = 0,0.5,1.0,3.0,5.0. Three ImageNet-trained vision transformer (ViT) models [47] were obtained from pytorch-image-models [48]. Note that the "imagenet1k" suffixinthe model names does not mean the model wasonly trained on ImageNet1K. Observation: A vision transformer (ViT-S) indeed shows higher error consistency with ResNet-50 than with BagNet-9 (see Table 1). Further insights could be gained by testing successively more constrained versions of the samebasemodel.

Toanswer this question, we tested human observers on abroad range of out-ofdistribution (OOD) datasets, recording 85,120 psychophysical trials across 90 participants.