How does the brain transform a chaotic stream of sensory signals into a stable, shared experience of reality? This theoretical paper proposes that the brain does not passively receive the world, but actively projects it – it continuously generates predictions about sensory inputs and updates its internal model when predictions fail, a process formally described by the free energy principle and active inference. We argue that this process can be understood through an analogy with quantum decoherence – the collapse of multiple possibilities into a single actuality. By integrating insights from this analogy with active inference, we show how the brain resolves sensory uncertainty into a coherent projection of reality. A central question we address is whether projecting reality changes it. We argue that it does: a strong, coherent projection reduces uncertainty, while a weak or fragmented one amplifies prediction errors, and a distorted one – diverging from social consensus – leads to alternative realities that resist correction. This projection operates in discrete, rhythmic cycles governed by brain waves and is fine-tuned by neurochemicals that determine how much we “trust” our predictions versus new sensory data. The Default Mode Network – the brain’s inner “sea” of self-awareness, memories, and context – acts as a central hub that integrates and updates these projections. Crucially, individual projections are stabilized into a shared, “solid” reality through social interaction, language, and culture – a process we call collective active inference. Within this framework, psychiatric disorders are understood as disorders of projection – distortions in how the brain generates and stabilizes its model of reality. This perspective reverses the traditional explanatory gradient: psychopathology is not merely a sensory or chemical problem, but a fundamental disruption of the brain’s capacity to project a coherent world. This unifying framework offers a new foundation for both pharmacological and psychotherapeutic interventions, grounding them in a common computational logic.
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G. et al. (2026) studied this question.
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