Now, for the first time, we are observing the brain at work in a global manner with such clarity that we should be able to discover the overall programs behind its magnificent powers.
The mind, in short, works on the data it receives very much as a sculptor works on his block of stone. In a sense the statue stood there from eternity. But there were a thousand different ones beside it, and the sculptor alone is to thank for having extricated this one from the rest. Just so the world of each of us, howsoever different our several views of it may be, all lay embedded in the primordial chaos of sensations, which gave the mere matter to the thought of all of us indifferently. We may, if we like, by our reasonings unwind things back to that black and jointless continuity of space and moving clouds of swarming atoms which science calls the only real world. But all the while the world we feel and live in will be that which our ancestors and we, by slowly cumulative strokes of choice, have extricated out of this, like sculptors, by simply rejecting certain portions of the given stuff. Other sculptors, other statues from the same stone! Other minds, other worlds from the same monotonous and inexpressive chaos! My world is but one in a million alike embedded, alike real to those who may abstract them. How different must be the worlds in the consciousness of ant, cuttle-fish, or crab!
Is intelligence the goal, or even a goal, of biological evolution? Steven Pinker writes, “We are chauvinistic about our brains, thinking them to be the goal of evolution,”1 and goes on to argue that “that makes no sense…. Natural selection does nothing even close to striving for intelligence. The process is driven by differences in the survival and reproduction rates of replicating organisms in a particular environment. Over time, the organisms acquire designs that adapt them for survival and reproduction in that environment, period; nothing pulls them in any direction other than success there and then.” Pinker concludes that “life is a densely branching bush, not a scale or a ladder, and living organisms are at the tips of branches, not on lower rungs.”
With regard to the human brain, he questions whether the “benefits outweigh the costs.” Among the costs, he cites that “the brain [is] bulky. The female pelvis barely accommodates a baby’s outsized head. That design compromise kills many women during childbirth and requires a pivoting gait that makes women biomechanically less efficient walkers than men. Also a heavy head bobbing around on a neck makes us more vulnerable to fatal injuries in accidents such as falls.” He goes on to list additional shortcomings, including the brain’s energy consumption, its slow reaction time, and the lengthy process of learning.
While each of these statements is accurate on its face (although many of my female friends are better walkers than I am), Pinker is missing the overall point here. It is true that biologically, evolution has no specific direction. It is a search method that indeed thoroughly fills out the “densely branching bush” of nature. It is likewise true that evolutionary changes do not necessarily move in the direction of greater intelligence—they move in all directions. There are many examples of successful creatures that have remained relatively unchanged for millions of years. (Alligators, for instance, date back 200 million years, and many microorganisms go back much further than that.) But in the course of thoroughly filling out myriad evolutionary branches, one of the directions it does move in is toward greater intelligence. That is the relevant point for the purposes of this discussion.
Physical layout of key regions of the brain.
The neocortex in different mammals.
Suppose we have a blue gas in a jar. When we remove the lid, there is no message that goes out to all of the molecules of the gas saying, “Hey, guys, the lid is off the jar; let’s head up toward the opening and out to freedom.” The molecules just keep doing what they always do, which is to move every which way with no seeming direction. But in the course of doing so, some of them near the top will indeed move out of the jar, and over time most of them will follow suit. Once biological evolution stumbled on a neural mechanism capable of hierarchical learning, it found it to be immensely useful for evolution’s one objective, which is survival. The benefit of having a neocortex became acute when quickly changing circumstances favored rapid learning. Species of all kinds—plants and animals—can learn to adapt to changing circumstances over time, but without a neocortex they must use the process of genetic evolution. It can take a great many generations—thousands of years—for a species without a neocortex to learn significant new behaviors (or in the case of plants, other adaptation strategies). The salient survival advantage of the neocortex was that it could learn in a matter of days. If a species encounters dramatically changed circumstances and one member of that species invents or discovers or just stumbles upon (these three methods all being variations of innovation) a way to adapt to that change, other individuals will notice, learn, and copy that method, and it will quickly spread virally to the entire population. The cataclysmic Cretaceous-Paleogene extinction event about 65 million years ago led to the rapid demise of many non-neocortex-bearing species that could not adapt quickly enough to a suddenly altered environment. This marked the turning point for neocortex-capable mammals to take over their ecological niche. In this way, biological evolution found that the hierarchical learning of the neocortex was so valuable that this region of the brain continued to grow in size until it virtually took over the brain of Homo sapiens.
Discoveries in neuroscience have established convincingly the key role played by the hierarchical capabilities of the neocortex as well as offered evidence for the pattern recognition theory of mind (PRTM). This evidence is distributed among many observations and analyses, a portion of which I will review here. Canadian psychologist Donald O. Hebb (1904–1985) made an initial attempt to explain the neurological basis of learning. In 1949 he described a mechanism in which neurons change physiologically based on their experience, thereby providing a basis for learning and brain plasticity: “Let us assume that the persistence or repetition of a reverberatory activity (or ‘trace’) tends to induce lasting cellular changes that add to its stability…. When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.”2 This theory has been stated as “cells that fire together wire together” and has become known as Hebbian learning. Aspects of Hebb’s theory have been confirmed, in that it is clear that brain assemblies can create new connections and strengthen them, based on their own activity. We can actually see neurons developing such connections in brain scans. Artificial “neural nets” are based on Hebb’s model of neuronal learning.
The central assumption in Hebb’s theory is that the basic unit of learning in the neocortex is the neuron. The pattern recognition theory of mind that I articulate in this book is based on a different fundamental unit: not the neuron itself, but rather an assembly of neurons, which I estimate to number around a hundred. The wiring and synaptic strengths within each unit are relatively stable and determined genetically—that is, the organization within each pattern recognition module is determined by genetic design. Learning takes place in the creation of connections between these units, not within them, and probably in the synaptic strengths of those interunit connections.