This post is Part 1 of a series.<\/p>\n
In an earlier post<\/a>, I explored how meaning might arise in a physical, meaningless universe\u2014drawing in part on physicist Carlo Rovelli\u2019s relational account, which treats meaning as emerging when physical correlations acquire evolutionary significance.[1] But that post left largely unexplored how this actually happens in brains. How do electrical signals come to be about something? How does significance arise from circuitry?<\/p>\n This four-part series explores how the brain generates meaning, tracing how meaning emerges in living systems\u2014from biological value and goal-directedness (Part 1), through the neural representations that guide action (Part 2), to shared symbols grounded in social cognition<\/a> (Part 3), and finally to the cultural institutions and personal narratives that give meaning its richest human forms (Part 4).<\/p>\n The Gap Between Pattern and Purpose<\/p>\n Physical systems exhibit patterns\u2014molecular arrangements, light wavelengths, temperature distributions, etc.\u2014that we can describe in informational terms.<\/p>\n Claude Shannon\u2019s information theory, developed in the 1940s for telecommunications, formalizes informational description by treating unpredictability as the measure of a signal. Predictable patterns (like \u201cAAAAA\u201d) contain little Shannon information because you already know what\u2019s coming. Random patterns (like \u201cXQJKZPM\u201d) contain maximal Shannon information because every letter is unpredictable. Yet random strings mean nothing\u2014they carry no semantic content. Shannon information says nothing about meaning.[2]<\/p>\n But meaning clearly exists for organisms with brains. A scent<\/a> can signal food or danger to an animal. The brain\u2019s representation <\/a>of that scent is about something in the world. Philosophers refer to this property as \u201caboutness,\u201d or intentionality. It arises when living systems register environmental patterns in relation to their own needs, capacities, and stakes in survival.<\/p>\n Meaning Is Fundamentally Relational<\/p>\n Meaning, however, exists not in neural patterns alone but in relationships between those patterns, the organism’s evolutionary history, its current goals<\/a>, and the environment<\/a> it navigates. A pattern of neural firing becomes meaningful through how it was shaped by natural selection, how it’s been tuned by the organism’s individual learning, and how it’s currently being used to guide behavior.<\/p>\n Consider place cells in a mouse\u2019s hippocampus. When the mouse occupies a specific location, particular neurons fire. That pattern represents location because evolution favored spatial tracking, learning refined it through experience, and downstream circuits use it to guide navigation.<\/p>\n The meaning isn\u2019t in the firing pattern itself but in its web of functional and evolutionary relations.[3] But this raises a deeper question: What makes these relations matter for the organism in the first place?<\/p>\n Value: The Missing Ingredient<\/p>\n Living systems must maintain themselves against thermodynamic decay. This creates intrinsic goals (that is, biologically grounded needs and action tendencies): to maintain viability and reproduce. As the neuroanthropologist Terrence Deacon argues, this organizational vulnerability gives rise to genuine teleology: Systems that can fail have goals, and goals create value.[4]<\/p>\n This is where a semiotic framework becomes useful (the study of signs and how they acquire meaning): it distinguishes correlations that merely occur from those that function as signs for an organism. Signs, in this sense, are correlations that an organism interprets and uses relative to its goals.[5]<\/p>\n From Directive to Descriptive<\/p>\n Early in evolution, meaning-bearing signs take the form of simple biological signals\u2014internal states that primarily control action rather than describing the world. When a bacterium detects a toxin, the internal signal doesn\u2019t represent \u201cDangerous chemical X is present.\u201d It functions, in effect, as \u201cMove!\u201d\u2014a pragmatic control signal in neuroscientist<\/a> and geneticist Kevin Mitchell\u2019s sense, guiding behavior directly rather than encoding an explicit description of the world.[3]<\/p>\n Consciousness Essential Reads<\/p>\n But as nervous systems evolved to process long-range senses like vision, something changed: Directive signals were increasingly supplemented by descriptive models of the world. You can\u2019t directly detect objects\u2014only photons striking the retina\u2014so additional processing evolved to infer objects from light patterns. This produced internal representations of world states rather than mere action commands. Crucially, these representations were decoupled from obligatory action and could be held \u201cin mind,\u201d compared, and evaluated before guiding behavior.<\/p>\n Predictive Brains and Valued Predictions<\/p>\n Rather than passively receiving input, brains continuously generate expectations shaped by prior experience and goals, updating them when predictions err.[6] When your visual cortex represents an apple, that representation is meaningful because it predicts features relevant to eating and action\u2014sweetness, texture, and graspability. These predictions aren\u2019t neutral; they\u2019re saturated with value, and the brain doesn\u2019t predict all features equally. Prediction errors drive learning because they signal that something relevant to action went differently than expected[7]\u2014for example, when an apple that looks ripe turns out to be sour or inedible.<\/p>\n Artificial Intelligence and Meaning<\/p>\n Could any sufficiently complex computer generate meaning by instantiating patterns and predictions like those found in brains? Not as computers are currently designed. Computers can instantiate patterns and predictions, but meaning emerges only in systems with intrinsic goals\u2014systems for which outcomes genuinely matter. When a chess program evaluates positions, nothing matters to the program itself. When a brain generates prediction errors, something genuinely matters: The organism is navigating toward self-maintenance and reproduction, ends that are inherent in its organization as a living system.[3,4] Whether artificial systems could develop genuine meaning for themselves remains an open question, but it would require them to have stakes in their own continued existence.[8]<\/p>\n Consciousness and the Evaluation of Meaning<\/p>\n