Higgen Focko L, Ruppel Philipp, Görner Michael, Kerzel Matthias, Hendrich Norman, Feldheim Jan, Wermter Stefan, Zhang Jianwei, Gerloff Christian
Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Department of Informatics, Universität Hamburg, Hamburg, Germany.
Front Robot AI. 2020 Dec 23;7:540565. doi: 10.3389/frobt.2020.540565. eCollection 2020.
The quality of crossmodal perception hinges on two factors: The accuracy of the independent unimodal perception and the ability to integrate information from different sensory systems. In humans, the ability for cognitively demanding crossmodal perception diminishes from young to old age. Here, we propose a new approach to research to which degree the different factors contribute to crossmodal processing and the age-related decline by replicating a medical study on visuo-tactile crossmodal pattern discrimination utilizing state-of-the-art tactile sensing technology and artificial neural networks (ANN). We implemented two ANN models to specifically focus on the relevance of early integration of sensory information during the crossmodal processing stream as a mechanism proposed for efficient processing in the human brain. Applying an adaptive staircase procedure, we approached comparable unimodal classification performance for both modalities in the human participants as well as the ANN. This allowed us to compare crossmodal performance between and within the systems, independent of the underlying unimodal processes. Our data show that unimodal classification accuracies of the tactile sensing technology are comparable to humans. For crossmodal discrimination of the ANN the integration of high-level unimodal features on earlier stages of the crossmodal processing stream shows higher accuracies compared to the late integration of independent unimodal classifications. In comparison to humans, the ANN show higher accuracies than older participants in the unimodal as well as the crossmodal condition, but lower accuracies than younger participants in the crossmodal task. Taken together, we can show that state-of-the-art tactile sensing technology is able to perform a complex tactile recognition task at levels comparable to humans. For crossmodal processing, human inspired early sensory integration seems to improve the performance of artificial neural networks. Still, younger participants seem to employ more efficient crossmodal integration mechanisms than modeled in the proposed ANN. Our work demonstrates how collaborative research in neuroscience and embodied artificial neurocognitive models can help to derive models to inform the design of future neurocomputational architectures.
独立单通道感知的准确性以及整合来自不同感觉系统信息的能力。在人类中，需要认知能力的跨通道感知能力会随着年龄增长而下降。在此，我们提出一种新的研究方法，通过利用最先进的触觉传感技术和人工神经网络（ANN）复制一项关于视觉 - 触觉跨通道模式辨别的医学研究，来探究不同因素在多大程度上影响跨通道处理以及与年龄相关的衰退。我们实现了两个ANN模型，特别关注跨通道处理流中感觉信息早期整合的相关性，这是一种被认为在人类大脑中实现高效处理的机制。通过应用自适应阶梯程序，我们在人类参与者和ANN中都实现了两种模式下可比的单通道分类性能。这使我们能够比较不同系统之间以及系统内部的跨通道性能，而不受潜在单通道过程的影响。我们的数据表明，触觉传感技术的单通道分类准确率与人类相当。对于ANN的跨通道辨别，在跨通道处理流的早期阶段整合高级单通道特征比独立单通道分类的后期整合具有更高的准确率。与人类相比，ANN在单通道和跨通道条件下的准确率都高于老年参与者，但在跨通道任务中低于年轻参与者。综上所述，我们可以表明，最先进的触觉传感技术能够在与人类相当的水平上执行复杂的触觉识别任务。对于跨通道处理，受人类启发的早期感觉整合似乎可以提高人工神经网络的性能。然而，年轻参与者似乎采用了比所提出的ANN模型中建模的更有效的跨通道整合机制。我们的工作展示了神经科学和具身人工神经认知模型的合作研究如何有助于推导模型，为未来神经计算架构的设计提供参考。
