Humans are capable of solving complex abstract reasoning tests. Whether this ability reflects a learning-independent inference mechanism applicable to any novel unlearned problem or whether it is a manifestation of extensive training throughout life is an open question. Addressing this question in humans is challenging because it is impossible to control their prior training. However, assuming a similarity between the cognitive processing of Artificial Neural Networks (ANNs) and humans, the extent to which training is required for ANNs' abstract reasoning is informative about this question in humans. Previous studies demonstrated that ANNs can solve abstract reasoning tests. However, this success required extensive training. In this study, we examined the learning-independent abstract reasoning of ANNs. Specifically, we evaluated their performance without any pretraining, with the ANNs' weights being randomly-initialized, and only change in the process of problem solving. We found that naive ANN models can solve non-trivial visual reasoning tests, similar to those used to evaluate human learning-independent reasoning. We further studied the mechanisms that support this ability. Our results suggest the possibility of learning-independent abstract reasoning that does not require extensive training.

翻译：人类能够解决复杂的抽象推理测试。这种能力究竟是反映了适用于任何未学习过的新问题的、独立于学习的推理机制，还是其一生中广泛训练的表现，仍是一个悬而未决的问题。在人类身上探讨这个问题具有挑战性，因为无法控制其先前的训练。然而，假设人工神经网络（ANNs）与人类的认知处理具有相似性，那么ANNs进行抽象推理所需训练的程度，对于理解人类的这一问题具有参考价值。先前的研究表明，ANNs能够解决抽象推理测试，但这一成功需要大量的训练。在本研究中，我们考察了ANNs独立于学习的抽象推理能力。具体而言，我们在没有任何预训练的情况下评估了它们的性能，ANNs的权重是随机初始化的，并且仅在问题解决过程中发生变化。我们发现，未经训练的ANN模型能够解决非平凡的视觉推理测试，类似于用于评估人类独立于学习的推理能力的测试。我们进一步研究了支持这种能力的机制。我们的结果表明，可能存在一种不需要大量训练的、独立于学习的抽象推理能力。