Two Framing Ideas About Human Evolution.
Human evolutionary change has been rapid and extensive; so much so that the genetic similarity and recent divergence between the human and the chimp lineages came as a profound surprise. Three million years ago humans were relatively minor elements of a rich East African mammalian fauna. Since then, our lineage has expanded geographically, demographically and ecologically. Over roughly the same period, our lineage has experienced an explosive increase in co-operation. We are the only large mammal that depends for essential resources on co-operation with non-relatives. Likewise, tool-use. Beginning about 2.5 million years ago, we became obligate technovores, with the pace of innovation picking up over the last 200,000 years. These changes have been accompanied by others in morphology, life history and family organization. We are not what we used to be. Tellingly, this pattern has not been mirrored in other lineages, as it would be if this trajectory had an external cause. So a first framing idea is that human evolutionary change has been self-generated through positive feedback. Specifically: a feedback loop driven by the increasing complexity of human social environments, and by the problems this complexity causes for co-operation management.
The second idea is that human cognitive competences have a high information load. Ordinary adaptive decision-making depends on an agent being sensitive to complex, subtle features of their environment. The information-hungry nature of human action first became apparent in thinking about language, but other competences turn out to be information-hungry as well. For instance, in much social interaction we are effortlessly tuned to the moods, intentions and beliefs of others. If you have made some horrendous social blunder, it is almost impossible not to be aware of it. Yet since others obey norms of politeness in such situations, this awareness often depends on sensitivity to small cues of posture, expression, tone of voice; shifts in conversational focus and so forth. As with language, so with the skills of social navigation, bargaining, co-ordinating effective plans, acquiring and using technology. Almost all of us acquire these skills, but they depend on acquiring and deploying significant informational resources.
Both these framing ideas are on the money. Positive feedback loops are indeed critical in driving human evolution, and much routine human action has a high information load. But I am sceptical about the way these framing ideas have been developed, and in the rest of this essay I explain that scepticism and gesture towards an alternative.
Co-operation, Defection, Co-ordination.
Increased levels of co-operation in early humans was probably triggered by some external change. Our environments became more seasonal and open, and perhaps that exposed our ancestors to more predators, so they had to act together to survive. But once co-operation became a key feature of human lifeways, it induced an arms race. Agents acting together can secure resources, construct technologies and defend territories that would be utterly beyond any lone wolf. But often the profit of co-operation is not contingent on everyone paying its full costs, and hence there is a temptation to secure a share of co-operation’s profits while avoiding its costs. Co-operation is thus risky as well as profitable; and so co-operators must be vigilant. Vigilant co-operation selects for intelligence. But as others become more intelligent, defection threatens to become increasingly well disguised. As co-operating groups became built from increasingly intelligent agents; as co-operating groups become increasing differentiated through division of labour; and as they become larger; effective vigilance became ever more difficult. Hence selection for vigilance selects for still higher intelligence, establishing a positive feedback loop between individual intelligence and social complexity.
This picture identifies co-operation management as the key to human cognitive evolution. Each agent has an interest in maximising return from co-operation while minimising investment in co-operative activities. This sets up a vigilance requirement that becomes steadily more challenging. However, while stable co-operation depends on policing defection, in small scale social worlds, this picture overstates the problem of identifying cheats, and understates the informational challenge of co-ordination; of making co-operation work. In a village, everyone knows who the bastards are. The ancient policing problem is not one of identification but motivation; of risking confrontation when they are bad bastards. Once something like language evolved, bastards were fingered by effective gossip networks. But they may have been outed even earlier, by an ancient feature of human economic life: our evolution as social foragers. There has been a revolution in human foraging. Our lineage has evolved from being omnivorous marginal scroungers to being dominant predators; from being predator targets to taking the food from other predators.
There is much controversy about this process: about its timing; about the relative importance of scavenging and hunting; about female co-operation and the role of plant foods and small game in human diets; about the invention of cooking and its role in human evolutionary history. But by 400,000 years ago, humans had evolved into social foragers exploiting high value, heavily defended resources. Large and medium size game animals had become a key resource. Large herbivores (buffalos, for example) are neither easy nor safe to kill, especially with short-range technology. So this foraging mode depended hunting peoples having intimate knowledge of their targets and the local terrain. Social foraging requires information: and the more ambitious, profitable, or varied the targets, the more information it requires. It depended on technology, and the skills to use it expertly. And it depended on co-ordinated co-operation; hunting large animals with short-range weapons poses a formidable co-ordination challenge. Lone hunters or small parties can kill large animals once lethal standoff technology has been invented: spear-throwers; bows; poison darts. But 400,000 years ago, these innovations lay in the distant future.
Thus generating the profit from collective foraging was informationally demanding. But identifying the shirkers who threatened that profit was not. Killing safely with stone-tipped spears required groups to act together in co-ordinated yet flexible ways, in conditions of stress, danger and time pressure. Such conditions generate information: agents leak information about their character, judgement and capacities when engaged in intimate, high-stress collective activity, especially when the activity persists for days. On a three-day hike, you find out a lot about your companions, especially if you get lost or the weather turns foul. That is true even when you have modern equipment and face no real danger. The greater the stress, discomfort or danger, the more you and they learn. Collective foraging would frequently involve stress, discomfort and danger, and shirkers and cheats would unambiguously identify themselves. Our ancestors did not need the social sensitivity of Jane Austen to know with whom they were dealing. But they did need to be informed and intelligent to act together to extract resources from a recalcitrant and dangerous world.
Modularity, Novelty, Expertise.
Foraging illustrates the information-hungry character of human skills. Famously, in theories of human evolution, information-hunger has been linked to a modularity hypothesis. Adaptive response to our complex environment depends on innate, evolved, special purpose cognitive mechanisms, for it is only such mechanisms that enable us gather and deploy the information on which action depends. In turn, the modularity hypothesis presupposes that information demands are discrete and predictable. So while ordinary human action involves solving problems with a high information load, both the problems, and the information needed to solve them, are relatively constant over evolutionary time. Hence we get the famous “massive modularity” hypothesis, and the idea that our minds are ensembles of innately equipped special purpose devices; devices which adapt us to the challenges posed to our foraging Pleistocene ancestors; challenges which largely persist today. Our minds are integrated arrays of devices each of which solves a particular problem with remarkable efficiency. These devices are efficient because they come pre-equipped with much of the information they need. We are born knowing what human languages are like, what human minds are like, what human social worlds are like. Children need to learn the specific moral norms of their community. But they do not need to learn what a moral norm is.
This nativist explanation of information-hungry competence presupposes that the information structure of human selective environments is stable. If the information a child needs about her world is stable over evolutionarily significant time frames, selection can build that information into human minds. Not otherwise. Some domains are stable. Material technology is a plausible example of a discrete, stable, and fitness-critical domain, for the causal properties of sticks, stones and bones do not change. However, many central aspects of the human world have changed fundamentally. Think, for example, of the Holocene Revolution. Over the last 10,000 years, humans became sedentary rather than transient. Most abandoned foraging for other economic activities, with much co-operation mediated by market mechanisms. We began living in much larger, much more stratified groups, with formal institutions and top-down decision making. Sexual relations changed, with reproductive skew becoming an important factor in many human cultures. Technology, including specialised information technology, elaborated, and the pace of technological change ultimately quickened so that real change took place within a single generation. We live in a new world (as did many of our more recent ancestors). If we really had stone-age minds in an electronics-age world, we would be crippled by adaptive lag. But we remain competent in responding to many of these novel challenges; for example, most of us work competently in formal institutions. The modularity hypothesis embraces the centrality of learning, but modules channel learning. To the extent that the modularity hypothesis explains competent response to information-hungry problems by appeal to pre-loaded information, it is poorly posed to explain competent response to evolutionarily novel challenges.
Competence in the face of evolutionarily novel problems depends on skill. Skills are phenomenologically akin to modules: they are fast, automatic, and task specific. Without the special training of a professional linguist, it is impossible to hear speech in a familiar language as mere noise: I cannot but hear English speech as words, sentences, conversations. Likewise, I cannot but see written English words as words; I cannot see them as mere shapes. But reading is a response to a novel feature of the world; language in a new medium. Moreover writing makes a new kind of communication possible: decontextualised, often one-off communication with strangers, sometimes displaced in space and time. Once the capacity is acquired, reading seems as natural as listening. But like many skills, reading depends on a long learning history in organised developmental environments. The problem is to characterise this environment and explain its evolution.
The evolved apprentice.
On both the modularity hypothesis and the apprentice learning alternative, human minds are evolved learning machines. But on the apprentice model, our minds are adapted to evolutionarily salient channels, sources and contexts of learning (and teaching), as much as (or more than) our minds are evolved to learn about specific factual domains. Human lifeways came to depend on social learning, for co-operative foraging depends on synthesising social, ecological and technical information. So successful social foraging — especially in high risk environments — depends on each partner having a well-tuned sense of the skills and intentions of the others. It depends on communication and planning. But it also depends on rich, accurate local knowledge; on a detailed grip of the natural history of target species; on locally made technology used with great expertise. No generation acquires these informational resources from scratch: the cognitive capital on which successful foraging depends is acquired by cross-generation information pooling. The informational resources one generation inherits will be modified (as conditions change and through innovation) and transmitted for further modification to the next.
High volume, high fidelity, inter-generational cultural learning coevolves with social foraging. There is feedback. As the fidelity of social learning improves, social foraging becomes more effective, for technology and technique improve across the generations. As social foraging becomes more profitable, adults can more effectively support the next generation while they acquire skills and information. This loop depends on the fact that humans organise the learning world of the next generation. Humans (like many other organisms) modify their own environment. One important form of human niche construction is informational engineering. Humans of one generation act in ways that transform the learning environment of the next generation. Cultural learning is obviously central to human social life. But most cultural learning is hybrid learning; it is culturally enhanced trial and error learning. Very few humans acquire significant life skills just from instruction and demonstration; very few learn skills by unassisted exploration. Human children explore and experiment on their physical and social environment. But they often explore environments which have been shaped to make it easier or safer for them to acquire critical capacities.
Apprentice learning is a good model of this form of cultural learning. Apprentice learning is a very powerful mode of social learning, making possible the reliable acquisition of complex and difficult skills. Apprentice learning is hybrid learning: an apprentice combines information from the social world with information from the physical-biological environment. It is learning by doing, but in supervised and enriched learning environments. Apprentices are assigned tasks that are both appropriate to their current skill levels, but which stretch them, and which scaffold the acquisition of new skills. In their work they are surrounded by props: tools; completed and partially completed artefacts; raw materials in various stages of preparation; errors — examples of what can go wrong. Often they have sources of advice and demonstration, for learning is often social and collaborative. Apprentice learning depends on individual cognitive adaptations, including those for social learning, but it depends as well on these adaptively structured learning environments.
In my view, craft expertise — skill sets of the kind forager lives depend on — are fine-tuned at a generation, and reliably transmitted across generations, by this mode of organised human learning environments. Novices learn by doing in an environment seeded with informational resources and with their learning trajectory partially organised by experts. The expert organize the trial and error learning of the less expert by a combination of (i) task decomposition: (ii) ordering skill acquisition, so each step prepares the next; (iii) well chosen exemplars. Such expert-structured and supervised learning by doing is very powerful, as craft apprenticeship learning shows. To the extent that skill acquisition in forager societies is similar to this mode of hybrid learning, it makes possible high volume, high fidelity social learning. This model acknowledges the importance of individual cognitive adaptations, but equally calls attention to the role of adapted environments.
As I see it, the apprentice learning model has important virtues. First: it identifies a form of learning that can be assembled incrementally. As Eva Jablonka has shown, the reliable transmission of skill can begin as a side-effect of adult activity, without adult teaching or adaptations for social learning in the young. For an innovation that becomes central to adult economic activity as a side-effect transforms the local environment that juveniles explore, positively biasing learning probabilities. Once established, skill transmission then brings with it selection for cognitive and social changes that increase the reliability or reduce the cost of learning. But rudimentary but reliable skill transmission does not presuppose the presence of such adaptations. Second, apprentice learning is known to support high fidelity, high bandwidth knowledge flow. Until recently, much technical competence depended on such learning. Third, this organisation of learning remains powerful in changing environments, so long as the pace of change is not catastrophically rapid. Fourth, the model fits the ethnographic and archaeological data. For example, in many forager societies children’s toys and games practice crucial skills, and those societies organise and enhance children’s participation in economic activity. All this supports the transmission of traditional craft skills. Finally, this basic model has broad application, beyond formal apprenticeship in highly skilled craft guilds. For instance, I have argued elsewhere that it fits the cross-generation transmission of norms and values. There were no Palaeolithic craft guilds akin to those of early modern Europe, but there are quite striking similarities between skill transmission in formal apprenticeships and skill transmission in traditional society. They both depend on socially organised and adapted learning environments which marry the power of trial and error learning to that of cultural transmission. Such environments make possible the transmission of high fidelity, high volume information across the generations. That in turn makes possible the reliable acquisition of complex, learning-dependent competences in a changing world.