Book Review: A Contrarian View of How Consciousness Evolved

The article reviews the book Natural Intelligence: How Consciousness Evolved by psychologist and neuroscientist Michael S. A. Graziano. The book challenges mainstream scientific views on how consciousness emerged in humans and other animals. Graziano argues that consciousness is not a mysterious or supernatural phenomenon but a natural product of evolution, shaped by biological and cognitive processes. He proposes that consciousness arises from the brain’s ability to model its own attention, a concept he calls the attention schema theory. The book synthesizes decades of research in neuroscience, psychology, and evolutionary biology to present a contrarian perspective on a topic often dominated by philosophical debates. Graziano’s work is positioned as a bridge between hard science and broader public understanding of consciousness.

At the core of Graziano’s argument is the attention schema theory, which suggests that consciousness is the brain’s way of modeling its own attention. In this framework, the brain creates a simplified representation—a schema—of how it focuses on objects or thoughts, and this representation is what we experience as consciousness. The theory posits that consciousness is not an all-or-nothing phenomenon but a spectrum, with varying degrees of self-awareness across species. Graziano compares this to how a computer might simulate its own processing to optimize performance, though he emphasizes that biological brains do this in a far more complex way. The theory attempts to explain why humans and some animals exhibit behaviors that suggest self-awareness, such as recognizing themselves in mirrors or making decisions based on internal states.

Graziano’s book stands out for its contrarian view in a field often dominated by philosophers and neuroscientists who treat consciousness as an intractable mystery. While mainstream research frequently frames consciousness as a hard problem—a term coined by philosopher David Chalmers to describe the difficulty of explaining why and how subjective experience arises from physical processes—Graziano argues that it is not inherently mysterious. He contends that consciousness can be studied empirically, much like other cognitive functions, and that dismissing it as unknowable is a self-imposed limitation. The book critiques the tendency to treat consciousness as a dualistic phenomenon, separate from the brain’s physical workings, and instead frames it as a natural evolutionary adaptation.

Graziano’s theory places consciousness within an evolutionary context, suggesting it evolved as a tool for survival and social interaction. He proposes that the ability to model attention allowed early humans and other species to predict the behavior of others, enhancing cooperation and competition. For example, the capacity to infer what another individual is paying attention to could have been crucial for hunting, avoiding predators, or navigating complex social hierarchies. The book draws on evidence from comparative neuroscience, showing that brain structures involved in attention are present in a wide range of species, from primates to birds. Graziano argues that this shared neural machinery supports the idea that consciousness is not unique to humans but exists on a continuum across the animal kingdom.

The article highlights Graziano’s critique of two dominant perspectives in consciousness studies: integrated information theory (IIT) and global workspace theory (GWT). IIT, developed by neuroscientist Giulio Tononi, proposes that consciousness corresponds to the brain’s capacity to integrate information in a highly interconnected way, with a measure called phi quantifying this capacity. Graziano argues that IIT is overly abstract and fails to explain how subjective experience arises from physical processes. Similarly, GWT, which posits that consciousness emerges from the brain’s ability to broadcast information globally, is criticized for lacking a clear mechanism. Graziano’s alternative, the attention schema theory, is presented as a more parsimonious and testable explanation that aligns with empirical data.