The cognitive challenge in working with things we cannot directly see

In my first week at university, I walked into a genetics lab. The professor showed us an electrophoresis gel, pointed to a few fluorescent bands, and said: “Look, DNA.”

I remember exactly what I thought: “But where is the double helix?”

I realized 5s later how ridiculous my question was, but that was too late. Later I realized that reaction revealed something important: a real cognitive challenge in working with things we cannot directly see.

I remember that was the day I decided I wouldn’t go into genetics and DNA: I wanted to work with tangible things! So I took marine biology. A fish was a fish. Even a copepod could easily be seing in simple lenses.

That changed during my PhD. My friend Milton Moraes was working with gene expression in Hansen disease and he could predict the degree of inflammation BEFORE it happened. I remembering the Wow moment: can I diagnose the damage of a pollutant BEFORE it was visible? I could, and I did. And that was the begning of my love affair with DNA.

Working with gene expression, I began to understand the power of that invisible layer. Molecular changes I could not see directly could explain why an organism was healthy or sick, why it responded in a particular way to an environmental stressor, and why apparently small impacts could produce major biological consequences.

The invisible was producing the visible.

Love became respect and I became an Environmental Molecular Biologist deciphering the DNA of biodiversity.

Today, I often find myself on the same side of the laboratory bench of that genetics professor, having to convince engineers, companies, and environmental agencies to use DNA to measure biodiversity. And I can almost hear their discomfort when they look at a FASTA file or even an species heatmap: “but where is the double helix?”

It is easy to trust a fish you can hold in your hand, a photograph of a jaguar from a camera trap, the sound of a blue macau recorded by an acoustic sensor: cognitively comfortable evidence. Our brains recognize them immediately.

But with DNA you MUST look at a representation, which requires trust in the measurement process. As Fred Mercury would say, ‘its a king of magic’.

But it is not magic, it is science, and it does not require blind trust, because the results can be validated in the real world: the DNA profile of a forest is different from the DNA profile of a degraded pasture. Significantly, statistically, different. And that matters, because it is a robust discriminant power that capture based morphology taxonomy cannot deliver.

Much of modern technology depends on things we cannot perceive directly. We do not see ultrasound, microwaves, infrared, ultraviolet, electromagnetic waves in general, satellite signals… we do not directly perceive most of the variables that industrial sensors measure every second. DNA is just one more of those.

But rationality requires that cognitive disconfort cannot be an excuse for lack of adoption forever. Beyond the overwheelming scintific evidence of eDNA efficacy and efficience, there is plenty of social proof of eDNA as well, from the IUCN letter to society, to the ISO procedure for eDNA sampling. If one wants to go beyond that and judge for themselves, the understanding of the calibration, and its ability to distinguish signal from noise requires an investment of time and energy: the burden of the understanding is in the recipient of the information, not in the technology.

When the goal is to characterize biodiversity, DNA-based methods can provide an amount of information and a level of discrimination that direct observation often cannot. They can detect organisms that are difficult to find, distinguish closely related species, characterize entire communities, and turn a small environmental sample into a large amount of biological information. That does not mean images, morphology, or acoustics have lost their value. Different technologies answer different questions. but they become the complementary ones, not the other way around.

The problem begins when we prefer a less informative technology simply because its evidence feels more intuitive. I understand that discomfort because I once felt it, but science and engineering exist, in large part, to let us move beyond the limits of our senses. The world does not become less real because we need instruments to measure it.

I start to understand that one of the hardest shifts in adopting DNA for environmental monitoring is learning to separate how easy evidence is to understand from how good that evidence is. But in the end, we should not trade measurement precision for cognitive comfort.