  A pervasive research protocol of cognitive neuroscience is to train subjects
to perform deliberately designed experiments and record brain activity
simultaneously, aiming to understand the brain mechanism underlying cognition.
However, how the results of this protocol can be applied in technology is
seldom discussed. Here, I review the studies on time processing of the brain as
examples of this protocol, as well as two main application areas of
neuroscience (neuroengineering and brain-inspired artificial intelligence).
Time processing is an indispensable dimension of cognition; time is also an
indispensable dimension of any real-world signal to be processed in technology.
So 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
above-mentioned 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 by experimenting on the surrogate. Third, I call for technology-oriented
research, with the aim of technology creation instead of knowledge discovery.
