The subject of intelligence has been dominated by psychologists using IQ tests. They are collections of short questions and puzzles, selected because performances on them (as overall scores) correlate with educational and social criteria that are themselves assumed to be “measures” of intelligence. There is little other theory involved. That leaves a lot of questions about the validity of the tests and the nature of intelligence.

More importantly it leaves a theoretical vacuum inside the whole subject of intelligence. Understanding Intelligence tries to tackle that problem by putting intelligence in the context of living functions in general. After critically dismissing the concept of IQ, and the nature-nurture debate around it, attention is drawn to the origins of life itself. It shows that living systems – able to survive and maintain integrity across changing environments – could only have arisen as ‘intelligent systems’. By that is meant able to anticipate future states by registering how changes in one variable is associated with (depends upon) changes in others, at different levels (as with driving in busy traffic: across numerous variables, signals and manoeuvres are always context-dependent). By assimilating deep statistical dependencies self-organising molecular mixes were able to mutually compensate for, or even anticipate, changes caused by changes in their environments. Such abilities are now being regularly reported, even in single cells.

The book argues that such intelligent systems became the real basis of the evolutionary fountain biologists report, starting from single cells. A famous example is the sporadic cooperation between otherwise solitary amoeba into ‘social’ bodies called slime moulds. The patterns of signals between cells, rapidly changing over space and time, not only directs individual motion, within cells. Along with feedforward/feedback loops they give rise to a new intelligent system between them, sometimes referred to as the ‘language’ or ‘cognition’ of slime moulds.

Multicellular organisms could not have evolved without such systems of intelligent communication and action. They reproduce from a single fertilized egg, from which develops hundreds of different cells. As they differentiate they arrive in just the right place in the developing embryo at just the right time. They all have the same genes, so they can only ‘know’ how to do that through the space- and time-dependent signals between them.

In more evolved species, cells became tissues to form distinct organs. Local communication between cells continues. But it is now nested within a wider, hierarchical system we call physiology. That makes the whole much more sensitive to changes in the outside world, and able to coordinate responses within for the general good. We now know how physiology is an emergent intelligent system, functioning through the interdependencies of signals at many levels, with numerous feedforward/feedback loops, and the predictably engendered.

Increasing sensitivity and responsiveness to rapid change in the outside world fostered behaviour. But that needed nervous systems and then brains, and the cognitive functions we most think of as intelligence. These have evolved as amplifications of physiology. Instead of signals being somewhat haphazardly ‘broadcast’, they can be routed down long fibres to special connections (synapses) on cell clusters, the beginnings of brains. Even in the brains of insects and spiders, with a few hundred to a few thousand brain cells, a staggering depth of statistical structure can be captured for predictability and behavior. More advanced systems have millions of cells connecting with thousands of others through billions of connections. With the enormous plasticity of connections, changes in the world are continuously tracked, dependencies learned, and futures anticipated.

The thing is that those patterns, transcending mere input-output streams, are what we call cognition – a new intelligent system, emergent from precursors, for dealing with more rapid and complex change and creating novel responses to it. These patterns themselves interact, and new ones emerge in the process, with staggering depths of statistical structure. In that way, the world constructed by the brain, “inside”, is far more complex and informative than that directly experienced.

With such behaviours, brains and cognitive systems, individual bodies themselves could interact and cooperate. The amazing constructiveness of ants and bees is often referred to as ‘swarm’ intelligence. But the three vital ingredients are clear: a signaling or communication system (here, mostly chemical ‘pheromones’); feedforward/feedback loops; and the emergence of patterns of statistical dependencies.

That social intelligence amounts to a further, evolved, intelligent system. Later, in the evolution of flocks, shoals and herds of animals it relied increasingly on bigger brains. As more complex arrays of variables needed to be comprehended, social intelligence needed still bigger brains. The tendency reached a kind of peak in monkeys and apes, and much has been made of their human-like characteristics and cognitive abilities. But real cooperation among individuals in their groups is limited.

There is little doubt that even the early ancestors of humans were cooperative hunters as a significant part of their way of life, millions of years ago. Cooperative hunting places unprecedented demands on cognitive systems: deeper dependencies across many more variables: targets rapidly moving, in different and constantly changing directions; perceptions and responses needing to be tuned to those of other group members over space and time; integrating streams of visual, vocal and other cues; and so on. In sum, a more complex intelligence between brains became demanded to regulate what goes on within them.

It constitutes a new, and unique, intelligent system, a socio-cognitive one. By analogy with the ‘language’ of slime moulds, or of ant colonies, it entails emergent statistical structures between members, only now actualised in shared symbolic forms: the grammar of a unique language; a myriad hardware and social and cognitive “tools” fashioned for sharing and collaboration: hammers, visible and audible cues and gestures, emblems, laws, customs, marriage rules, and so on, and so on. This is what we call human culture.

The important point is that culture is more than merely copying habits from others, as many evolutionary psychologists have assumed. In the constant interaction between and within brains, and many feedforward/feedback loops, novel cultural tools are being constantly created. They both deal with environmental change and change it in return, as modern science and technology emphatically demonstrate. The consequence is that, unlike all other species, humans adapt the world to themselves rather than vice versa. The story of life is truly the story of intelligence.

Title: Understanding Intelligence

Author: Ken Richardson

Paperback ISBN: 9781108940368