Some evolutionary explanations sound convincing because they make sense. But what makes such explanations reliable?

Evolutionary explanations often begin with a plausible story. A trait appears useful. We imagine a past environment in which this usefulness would have increased survival. The explanation feels coherent.

But plausibility is not the same as evidence.

In evolutionary biology, a distinction is often made between how-possibly and how-actually explanations. A how-possibly explanation shows how a trait could have evolved. It provides a scenario that is logically consistent and fits with general evolutionary principles. A how-actually explanation goes further. It requires evidence that this process actually took place.

This difference is crucial.

Many psychological traits can be explained by constructing plausible scenarios about ancestral environments. But without supporting evidence, such explanations remain speculative. They are often referred to as “just-so stories.”

To move beyond plausibility, several kinds of evidence are needed:

• A clear account of the ecological conditions in which the trait would have been advantageous
• Evidence about the environment and population in which the organism evolved
• Evidence that variation in the trait existed and was heritable
• Evidence that selection acted on this variation

Only when such evidence is available can we speak of a well-supported adaptation.

Returning to our example, the tendency to interpret ambiguous signals as threats can be explained in a plausible way. But what exactly would we be claiming is an adaptation here? The general pattern of erring on the side of caution?

Strictly speaking, the false alarm bias is not a single trait with a single selection history. To establish such a bias as an adaptation, we would need evidence that it evolved under specific conditions and was shaped by selection. It is better understood as a general design principle: a recurring pattern that selection tends to produce in any decision system operating under asymmetric costs. The principle is closer to a consequence of how optimization under uncertainty works than to a specific trait shaped for a specific function. What can be specific adaptations are the particular systems that instantiate this principle.

The threat detection systems introduced earlier in this chapter provide a concrete illustration:

Research suggests that the human fear system responds preferentially to ancestral threats – such as snakes and spiders – but not to modern dangers like guns or electrical outlets, even though the latter are statistically more lethal. This response is automatic, fast, and difficult to override consciously. It is centered on a dedicated neural structure, the amygdala. The specificity to ancestral threats is difficult to explain by learning alone.

Neuroscience reveals a similar picture. Primates have dedicated brain circuits that respond to snake images faster and more strongly than to other visual stimuli – including faces. And primate species that evolved without venomous snakes, such as lemurs on Madagascar, have measurably poorer vision than those that co-evolved with them. This cross-species pattern is what we would expect if the system was shaped by selection (and difficult to explain as a developmental constraint or a product of individual learning).

Developmental evidence strengthens the case further. Children as young as three detect snakes faster than neutral objects like flowers or frogs, despite having little or no negative experience with snakes. The same attentional bias appears in infants as young as eight months. The early emergence of this pattern, well before relevant learning could account for it, suggests that the system does not depend on individual experience alone. It appears to be prepared in advance.

Author: Fabian Müller

---

References

Haselton, M. G., & Buss, D. M. (2000). Error management theory: A new perspective on biases in cross-sex mind reading. Journal of Personality and Social Psychology, 78(1), 81–91. {.citation-indented} Isbell, L. A. (2006). Snakes as agents of evolutionary change in primate brains. Journal of Human Evolution, 51(1), 1–35.

Isbell, L. A. (2009). The Fruit, the Tree, and the Serpent: Why We See So Well. Harvard University Press.

Johnson, D. D. P., Blumstein, D. T., Fowler, J. H., & Haselton, M. G. (2013). The evolution of error: Error management, cognitive constraints, and adaptive decision-making biases. Trends in Ecology & Evolution, 28(8), 474–481.

LoBue, V., & DeLoache, J. S. (2008). Detecting the snake in the grass: Attention to fear-relevant stimuli by adults and young children. Psychological Science, 19(3), 284–289.

LoBue, V., & DeLoache, J. S. (2010). Superior detection of threat-relevant stimuli in infancy. Developmental Science, 13(1), 221–228.

Nesse, R. M. (2001). The smoke detector principle: Natural selection and the regulation of defensive responses. Annals of the New York Academy of Sciences, 935(1), 75–85.

Nesse, R. M. (2005). Natural selection and the regulation of defenses: A signal detection analysis of the smoke detector principle. Evolution and Human Behavior, 26(1), 88–105.

Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483–522.

Van Le, Q., Isbell, L. A., Matsumoto, J., Nguyen, M., Hori, E., Maior, R. S., … & Nishijo, H. (2013). Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snakes. Proceedings of the National Academy of Sciences, 110(47), 19000–19005.