Lingang Laboratory, Shanghai, China.
Institute for Future, Qingdao University, Qingdao, China.
Adv Exp Med Biol. 2024;1455:171-195. doi: 10.1007/978-3-031-60183-5_10.
A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.
认知神经科学中常用的研究方案是在记录大脑活动的同时训练受试者进行精心设计的实验,目的是了解认知背后的大脑机制。然而,很少有人讨论过该研究方案的结果如何应用于技术领域。在这里,我以大脑时间处理的研究为例,综述了该研究方案以及神经科学的两个主要应用领域(神经工程和类脑人工智能)。时间处理是认知的基本维度,而时间也是技术中处理的任何实际信号所不可缺少的维度。因此,可以预期认知中关于时间处理的研究将深刻影响与大脑相关的技术。令人惊讶的是,我发现认知研究中关于时间处理的结果在解决实际问题方面几乎没有帮助。这种尴尬的局面可能是由于认知研究结果在很大程度上缺乏可推广性,这些结果是在实验室的严格控制条件下得出的,而不能推广到实际生活情况中。这种缺乏可推广性可能源于世界(包括认知)的基本不可知性。总的来说,本文对认知神经科学的上述研究方案的有用性和前景提出了质疑和批评。然后,我提出了未来研究的三个建议。首先,为了提高研究的可推广性,最好在现实生活条件下而不是在严格控制的实验室实验中研究大脑活动。其次,为了克服世界的不可知性,我们可以设计一个易于接近的被研究对象的替代品,这样我们就可以通过在替代品上进行实验来预测被研究对象的行为。第三,本文呼吁进行面向技术的研究,旨在创造技术而不是发现知识。