Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization has been incorporated into artificially intelligent systems to solve complex problems in computer vision and navigation domains. Here we describe and test two novel lateralized artificial intelligent systems that simultaneously represent and address given problems at constituent and holistic levels. The experimental results demonstrate that the lateralized systems outperformed state-of-the-art non-lateralized systems in resolving complex problems. The advantages arise from the abilities, (i) to represent an input signal at both the constituent level and holistic level simultaneously, such that the most appropriate viewpoint controls the system; (ii) to avoid extraneous computations by generating excite and inhibit signals. The computational costs associated with the lateralized AI systems are either less than the conventional AI systems or countered by providing better solutions.

翻译：偏侧化在脊椎动物大脑中普遍存在，除了在运动控制中的作用外，它被认为是生物智能的重要因素。偏侧化既与低效表现相关，也与高效表现相关。已有假设认为，偏侧化具有可能抵消其代价的收益。鉴于偏侧化的普遍性，它可能具有对人工智能有益的优点。反过来，偏侧化人工智能系统可用作工具，以增进对生物智能中偏侧化的理解。近年来，偏侧化已被纳入人工智能系统，用于解决计算机视觉和导航领域的复杂问题。本文描述并测试了两种新型偏侧化人工智能系统，它们能够在组成部分层面和整体层面同时表示和处理给定问题。实验结果表明，偏侧化系统在解决复杂问题方面优于最先进的非偏侧化系统。这些优势源于以下能力：（i）同时在组成部分层面和整体层面表示输入信号，使得最合适的视角控制系统；（ii）通过生成兴奋和抑制信号来避免额外计算。与偏侧化AI系统相关的计算代价要么低于传统AI系统，要么通过提供更优的解决方案而得到抵消。