Scientific progress often depends on amplifying or visualizing previously imperceptible or difficult to observe phenomena.
It’s easy to produce examples of this:
- Galvanometers → electrical currents
- Microscopes → cells and microbes
- Telescopes → distant celestial bodies
- fMRI/EEG → brain activity
Of course not all breakthroughs are about amplification. But in a domain like ours, the so-called black box of the mind, we need all of the help that we can get. Psychology in part developed to provide a consistent and useful way to work with introspection but it has struggled greatly to achieve its goals there.
There are aspects of thought that we can observe presently:
- Neural activity patterns
- Behavioral outputs
- Reaction times
- Linguistic outputs
- Phenomenological self-observations of the content and process of thought
And there have been other proposals, like David Bohm made pertaining to dialogue, to allow for the slowing down of thought and observing it’s collective nature.
But there is no easy and consistent way to convincingly demonstrate, visualize, or amplify the fault, incoherence, or operational failures in one’s thinking. You could put someone with an expert and the expert can respond to the non-expert’s errors, but this is more limited than it sounds and the non-expert often resists evidence to the contrary of their held beliefs for a variety of reasons that will not be explored in this proposal.
There is a risk that this proposal has a categorical error in it. Physical phenomena can be amplified, but can thought be instrumentally measured or amplified? However, the core hypothesis that cognition might require new forms of observability sounds very reasonable. And apart from certain established technologies and approaches like EEG and fMRI and phenomenological methodologies, real-time awareness of thinking processes is an under-explored domain. There may also be a role for utilizing AI for such real-time monitoring of thought, but there are also significant risks and potential issues with using AI in this manner that would require a very careful investigation for this direction.
A good starting point would be the literature review from different domains (neuroscience, cognitive science, linguistics, computer science, etc.) to see what has already been accomplished or proposed in this direction.
But at some point we must go into the unknown with this, and experiment accordingly, to see if there are any undiscovered aids to making the process of thinking more easily observable. For there is no doubt that we need the help.