Chair of Developmental Psychology in Society, University of Bristol; Author, The Self-Illusion, Founder of Speakezee
Biological Models of Mental Illness Reflect Essentialist Biases

In 2010, in England alone, it was estimated that mental illness cost over £100 billion to the economy. Around the same time, the cost in the US was estimated at $318 billion annually. It is important that we do what we can to reduce this burden. However, we have mostly been going about it the wrong way because the predominant models of mental illness do not work. They are mostly based on the assumption that there are discrete underlying causes, but this approach to mental illness reflects an essentialist bias that we readily apply when trying to understand complexity.

It seems trite to point out that humans are complex biological systems and that the way we operate requires sophisticated interactions at many different levels. It is even more remarkable then, that it has taken over 100 years of research and effort to finally recognize that when things break down, they involve multiple systems of failure and yet, until the last couple of years, many practitioners in the psychiatric industry of Western culture have been reluctant to abandon the notion that there are qualitatively distinct mental disorders that have core causal dysfunctions. Or at least that’s how the treatment regimes seem to have been applied.

Ever since Emil Kraepelin at the end of the 19th century advocated that mental illnesses could be categorized into distinct disorders with specific biological causes, research and treatment has focused efforts on building classification systems of symptoms as a way of mapping the terrain for discovering the root biological problem and corresponding course of action. This medical model approach led to development of clinical nosology and the accompanying diagnostic manuals such as the Diagnostic and Statistical Manual of Mental Disorder (DSM)—the most recent fifth version published in 2013. However, that very same year, the National Institute of Mental Health announced that it would no longer be funding research projects that relied solely on the DSM criteria. This is because the medical model lacks validity.

A recent analysis by Denny Borsboom in the Netherlands revealed that 50 percent of the symptoms of the DSM are correlated, indicating that comorbidity is the rule, not the exception, which explains why attempts to find biological markers for mental illness either through genetics or imaging have proved largely fruitless. It does not matter how much better we build our scanners, or refine our genetic profiling, as mental illness will not be reducible to Kraepelin’s vision. Rather, new approaches consider the way symptoms have causal effects between them rather than arising from an underlying primary latent variable.

Approaches to mental illness are changing. It is not clear what will happen to the DSM as there are vested financial interests in maintaining the medical model, but in Europe there is a notable shift towards symptom-based approaches of treatment. It is also not in our nature to consider the complexity of humans other than with essentialist biases. We do this for race, age, gender, political persuasion, intelligence, humor and just about every dimension we use to describe someone—as if these attributes are at the core of who they are.

The nature of the human mind is to categorize the world; to carve Nature up at its joints as it were, but in reality, experience is continuous. Moreover, the boundaries we create are more for our benefit than as a reflection of any true structures that exist. As complex biological systems, we evolved to navigate the complex world around us and thus developed the capacity to represent it in the most useful way as discrete categories. This is a fundamental feature of our nervous system from the input of raw sensory signals to the output of behavior and cognition. Forcing Nature into discrete categories optimizes the processing demands and the number of responses that need to be made, so it makes perfect sense from an engineering perspective.

Such insights are not particularly recent scientific discoveries and we should all be aware of them and yet, the essentialist perspective continues to shape the way that we go about building theories to investigate the world. Maybe it’s the best strategy when dealing with unknown terrain—assume patterns and discontinuities with broad strokes before refining your models to reflect complexity. The danger lies in assuming the frameworks you construct are real.