Testing the Model. I agree that the issue of testability is of great significance, though I would not agree that testability is best understood as falsifiability, in Popper’s sense. So in a forthcoming paper in Philosophical Transactions (From Hominins to Humans: How Sapiens Became Behaviourally Modern), I devote the final section to testability, testing the idea both against the anthropological and the archaeological record. I argue, for example, that on the evolved apprentice model, the human capacity to maintain and expand cognitive capital depends on both the intrinsic cognitive capacities of the human mind, and the organisation of the learning environment. On cognitive breakthrough models we should expect to see pulses and plateaus; on the evolved apprentice model, partial reversals are to be expected, and that indeed is what the record shows.

Sheldon Davies

Neural bases of behaviour. This essay is a synopsis of a monograph, and in the first draft I had a quite long section arguing that the evolved apprentice model fitted neuroscientific data quite well. I have cut that section completely. There is, indeed, quite a lot of evidence of neural plasticity and of environmental effects on neural organisation (e.g. work on the effects of environmental enrichment in animals). But these results are no more than consist with the evolved apprentice model; they do not directly support it. Moreover, since I am persuaded that gene-culture coevolution has long been important, and that there are important environment-cortical organisation interactions in development, I would be extremely cautious about following Sheldon Davies’ lead in making claims about cortical organisation of hominins of 400 kya. Even if we could clearly establish the function of some cortical system in living populations of humans (and that is far from trivial), we cannot safely assume that homologous structures in ancestral populations have the same structure.

Affect. I agree with Sheldon Davies that to understand the descent of mind, we have to understand the evolutionary transformations of affect and motivation, not just of cognitive systems narrowly understood. But I would not regard this as especially controversial now. Sarah Hrdy (in Mothers and Others), in arguing for her “reproductive co-operation” model of human evolution makes the transformation of emotion and affect central. Michael Tomasello and his colleagues have recently focuses on the pivotal role (as they see it) of joint attention and collective intentionality, and for them, collective intentionality has an important motivational element; it is not just cognitive.

Catherine Driscoll

Novelty and Modules. Unsurprisingly, I am unconvinced by Catherine on this. First: it is not enough that the problem be stable in the Pleistocene to (late) Holocene transition. The kind of information needed to solve it must be stable, too, if prewiring is to explain our cognitive competence. And while the problem of detecting cheaters may well be stable, I think I is most unlikely that the way cheats are exposed is stable — at any useful level of abstraction — in that transition. (I agree face recognition may well be a module, though it is probably perceptual rather than cognitive). Second: the more one think of a set of modules as operating simultaneously, sharing data, sharing components, the less obvious one still has the explanatory clout that the appeal to modules was supposed to buy: their speed, their independence (and on some views of modules, their double dissociablity). They are no longer autonomous systems. Of course, I accept that modularity is a matter of degree. But this suggestion risks saving the term at the cost of its original substance.

Social Learning and Nativism. I am not sure how fundamentally I disagree with Catherine here, because I am not sure of just how much she includes in “many of these things are not things a scaffolded environment can teach a child”. Obviously: there is initial structure in human minds, and that is different from the initial structure of infant chimps and infant gorillas. There is a distinctive organismal contribution, and this no-one denies; Steve Downes points this out too. In my essay (and still more, in my other recent work), I have been concerned to emphasise how rich the stimulus is; children develop in a very information rich world, and often a world organised to make that information salient to them. Salience, of course, is sensitive to initial, and then typically developing, structure. Pointing is salient to young humans but not young chimps. So yes: there is initial structure, and sometimes that initial structure will develop adaptively relatively independently of organised informational inputs (quite likely, in the case of “folk physics”). In many cases, it will not. Part of the argument, then, is about cases, and about how much structure, and how specific it is to specific learning tasks. But that is not all the argument there is. For it is a further claim that this initial structure is well characterised as pre-installed information. Pat Bateson and Paul Griffiths, in their different ways, are two sceptics about this further claim. That further claim might be true, but it certainly does not follow immediately from the claim that initial structure is necessary.

Edouard Machery

I agree with Edouard, of course, that hybrid models are possible; indeed, they are plausible. As I mentioned above, I think a modular view of folk physics is very plausible; a modular view of language is still a viable option, though the case for it is much less overwhelming than it once seemed to be. I also agree that modules can be seen, and often are seen, as learning machines. The question, then, is how much modules as learning machines shape or constrain the direction of learning. For modular hypotheses to explain our cognitive competence in the face of high information load problems, it seems to me that they have to shape or constrain learning very significantly: we are good at cheater detection, mate choice, predator avoidance, because our innate, pre-wired, modules tells us what we need to learn. But if that is right, it seems to me that the social problems of large scale, hierarchically organised, societies really should generate massive adaptive lag problems. In contrast to Edouard, I do not think that the problem of mate choice is roughly the same in small scale, egalitarian societies in which almost everyone knows almost everything about everyone else, and hierarchical, informationally opaque large scale social worlds. I guess the difference between Edouard and me here is one of degree: I see some elements of stability, but very large elements of quite fundamental change. He sees the opposite.

Stephen Downes

Steve is right of course: emphasising the information rich nature of the environment, and emphasising the fact that this richness is no accident, by no means rules out innate structure. I know Steve is sceptical about the Baldwin Effect, but I am less so. Learning has costs, so where fitness critical features of the environment are stable, I would expect a total or partial canalisation of development; I would expect it to become less dependent on specific learning experiences, even if competent response was originally mediated by learning.

Daniel Kelly

Skills and modules. I take the paradigm cognitive module (language, theory of mind, moral cognition) to develop rapidly; to be relatively independent of rich, specific informational inputs; to be relatively invariant across the (relevant) population. Arguments for modularity, in this sense, often depend on some combination of invariance, poverty of the stimulus, and early development. I take skills to contrast in all three dimensions: they are not invariant within or across population; they often develop slowly (as in the “10,000 hour rule”); they often require rich and highly specific inputs. That said, of course they can both depend on, and interact with, modules, both perceptual and cognitive. If folk physics is a genuine module, many artisan skills depend on it (and, of course, on many perceptual modules). As modules typically develop early and skills develop slowly, this relationship is not symmetrical. If it really were a module, mate choice might be an exception, since it is relevant only to adult life (perhaps there are others like this).

One clear feature has to do with their sources: much or most of the information relevant to a skill is acquired, or derived from the (often structured) social environment, rather than being innately specified or brought to bear on the task of learning. For example, contrasting comprehension of spoken language with comprehension of written language, he holds that “like many skills, reading depends on a long learning history in organized developmental environments.” Skills are also the types of things that can be subject to cultural evolution, since they are transmitted from one generation to the next, like beliefs, values, norms, stories, and so forth.

Sterelny also seems to contrast skills with modules. He holds that “Skills are phenomenologically akin to modules: they are fast, automatic, and task specific.” While I take the gist of this last point, I’m unsure how to flesh it out. Are skills and modules antithetical to each other? One would think not, for a skill might be subserved or implemented by one or several modules. Take even the example of reading: surely, exercising this skill engages a slew of cognitive mechanisms that are paradigmatic modules, namely ones that subserve vision. Likewise, our ability to predict and make sense of others by ascribing to them mental states like beliefs and desires, and making inferences about the connections between those mental states and behavior – what is sometimes called our capacity for mindreading – might naturally be described as a skill, at least given the colloquial usage of that term. However, while different theorists might agree on how to characterize this skill, they might (and have, and do) at the same time disagree about the nature of the cognitive architecture that undergirds it, including whether or not that architecture is modular, or how much of it is innately specified and how much is learned, etc.

I have used different words – “subserve”, “implement” “engage” “undergird” – to point to one crude but viable model with which to understand the relationship between a skill and a module. Surely this needs to be fleshed out more, but given the contrast Sterelny implies between modules and skills, it does not seem to be one that he would be willing to embrace.

Environments are dynamic or stable, transient or permanent, but also, as other evolutionary theorists have pointed out, selectively relevant or irrelevant. What Sterelny helps us to see is that in lots of cases, it is a lot easier — in terms of energy expenditure, time spent and so on – to get a reasonably plastic individual to perform a demanding cognitive task by structuring their environment than it is to wait around the requisite time for their ilk to evolve a ready to order cognitive mechanism for the task. The relevant environmental structure has many dimensions. When I first walked into my grandfather’s tool shed in his yard, I was about six years old. The shed was full of all manner of foreign objects with plenty of potential. My grandfather’s demonstration of how to use a plane, a saw and a chisel provided crucial additional structure to the environment. My subsequent practice under his watchful eye got the process of skill transmission in motion.

All this being said, I do not see Sterenly, here or anywhere else, ruling out evolved internal structures. As he says here “if the information a child needs about her world is stable over evolutionarily significant time frames, selection can build that information into human minds.” What he emphasizes is that this approach will not explain all of human cognitive evolution. A vast amount left unexplained by modularity theorists will succumb to a combination of approaches that place heavy emphasis on carefully characterizing relevant environments.

This model also fits well with the hypothesis of an evolved pedagogy put forward by Csibra and Gergely (2009) and with what is known about the evolution of childhood and the juvenile period (for review, see Kaplan et al., 2000 and for an evolutionary model, see Kaplan & Robson, 2002). (Incidentally, Kim, how do you view your hypothesis in relation to these hypotheses and models?)

That said, I have reservations about the contrast Kim draws between the massive modularity hypothesis and the apprentice learning model. It is misleading to treat (even for expository purposes) the modularist conception of the evolution of the mind (when it is not reduced to a convenient strawman) and the apprentice learning model as incompatible alternatives.

First, the fact that we acquire much knowledge and skills through this form of social learning is compatible with the existence of numerous modules fulfilling various evolutionary important functions (mate choice, predator detection, cheater (bastard?) detection, and so on). That modules are not the whole story does not mean that they are not an important part of the story.

Second, as most (if not all) modularists maintain, modules are typically learning systems because the information required to fulfill their function (e.g., the information needed to detect predators) was not stable over evolutionary time. For this very reason, Clark Barrett (e.g., 2005) insists that the hypothesized cognitive system involved in learning to identify predators is fundamentally a learning system that helps children learn which animals are predators in their environments. The existence of modules is thus not incompatible with the importance of learning and of teaching. Quite the contrary in fact: the apprentice model complements nicely the modularist approach. Because in many (but not in all) domains information was not stable over evolutionary time, we have evolved task-specific learning systems, and we have also evolved a disposition to create learning environments for the acquisition of the relevant information.

Whether or not (and in what way) learning is central to particular evolved cognitive system) depends on the nature of the functions these evolved cognitive systems fulfill, and on the nature of the information that is needed to fulfill these functions (Fessler & Machery, forthcoming). This varies from domain to domain and from function to function. Little understanding is gained by opposing the modularist approach to the apprentice model.

Kim acknowledges that modules are not incompatible with learning, but goes on all the same opposing the modularist approach with the apprentice learning model. The reason is that Kim does not see how evolved modules could underlie much of our behavior since (1) modules are inflexibly geared toward solving those past problems that were relevant during human evolution, (2) our environment has changed in tremendous ways (“many central aspects of the human world have changed fundamentally”), and (3) we are adapted to our environments (“we remain competent in responding to many of these novel challenges”). He concludes that “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.”

My comments above about the place of learning in the modularist approach suggests that (1) is dubious, but in spite of the suggestive examples given by Kim (2) is also controversial. In many domains (mate choice, group identification, etc.), one does not need to look very hard behind the variable aspects of human life to identify constancies. People all over the world pay much attention to group membership and advertise their membership in groups by means of ethnic markers, the pragmatics of conversation are, as far as we know, the same all over the world, and so on. Indeed, one of the mechanisms mentioned by Kim to characterize the apprentice hypothesis—niche construction—explains why there is so much stability behind the appearances of variability. Because humans build their social world and to some extent their physical world, they create similar social organizations across generations and cultures exactly as beavers make sure that the environment is adapted to their needs by creating dams. For instance, as Richerson and Boyd (1999) have shown, one finds again and again large groups of thousands of individuals in a range of social and historical contexts.

References
Barrett, H. C. (2005). Adaptations to predators and prey. In D. M. Buss (ed.), The Handbook of Evolutionary Psychology (pp. 200-223). Hoboken, NJ: John Wiley & Sons.
Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13, 148-154.
Fessler, D., & Machery, E. (Forthcoming). Culture and cognition. In E. Margolis, S. Laurence, & S. Stich (Eds.), Oxford Handbook of Philosophy and Cognitive Science. Oxford University Press.
Kaplan, H. S., & Robson, A. J. (2002) The emergence of humans: The coevolution of intelligence and longevity with intergenerational transfers. PNAS, 99, 10221-10226.
Kaplan, H. S., Hill, K. R., Lancaster, J. B., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology, 9, 156-185.
Richerson, P. J., and Boyd, R. (1999). The evolutionary dynamics of a crude super organism. Human Nature, 10, 253-289.

Edouard Machery

To understand that the environment around here is full of “things” with which it is possible for me to physically interact; that “things” behave in certain ways. That there are thing-types: “knives”, “spears”, “pieces of wood”.

To understand (when watching Mary carve a spear with a knife) that there are two things (a knife and a spear), and Mary is doing something with one thing to the other thing. That Mary can do something with one thing to the other thing of the same type, on multiple occasions (that there are such things as action types and that “carving” is an action type). I need to understand which bits of what Mary actually does are relevant to something counting as a token of the action type – stabbing herself in the finger with the knife and yelling “ow!” isn’t relevant, nor is the way that Mary moves her elbows, but the direction in which she moves the knife is relevant.

To understand that Mary intends something by the knife and wood activity, and what it is she might intend (she is trying to make a spear). That sometimes she might not intend anything (she’s just whittling).

To understand why what Mary is doing matters, and why it is relevant to me (spears might help me live successfully in this environment, just as they do for Mary). That because it is relevant to me, it should be something to which I should attend; I should be motivated to copy Mary.
To understand that Mary is worth paying more attention to than John, and this is because John’s spears are less good than Mary’s with respect to the features that matter to me.

To understand that all these funny looking objects are partially made spears; they are on the way to being spears; that these are not just weirdly caved sticks, or mistakes, or sticks that got like that accidentally. That there is a difference between made artifacts and accidentally caused things.

And so on ad nauseam.

Many of these things are not things that a scaffolded learning environment can teach a child. They are things that the child has to be able to understand or learn, by themselves, in order to make use of a scaffolded environment. They are skills that the successful use of a scaffolded environment is predicated upon. How much of all this requires genuinely innate knowledge, and how much can be done using simple innate learning biases (such as attention-based and motivation biases) is up for grabs. But it does suggest that “evolved apprenticeship” requires an interaction between innate knowledge or biases and cross-generationally structured learning.

1. Domain specificity, novelty and “information load”
Kim argues that modules are poor handlers of novelty – modular minds “should be crippled by adaptive lag” in the face of “electronic age technology and institutions”. It’s not obvious to me that a modular mind of the sort actually thought to exist by some evolutionary social scientists would be routinely unable to handle novelty. The “domain” of a module can be almost anything – including some problem or set of properties of an environment abstract enough to be present in both the Stone Age and the Electronic Age. Face recognizers and cheater detectors – classic central processing “modules” – are interestingly adaptive even in modern societies, where there are still faces and cheaters. Modules only become maladaptive where the modern environment changes in a way that cuts across their capacity; Kim’s main example, formal institutions, isn’t obviously a case where this should happen. Stone Age civilizations may not have had big complicated formal institutions like universities, governments and so on, but they certainly had simpler social institutions with the attendant symbolism, roles and social norms. Modules of a relatively abstract sort, able to handle he abstract features of these sorts of institutions, might well be able to extend themselves to handle modern ones. Some of our failures to handle the electronic age are consistent with the view that our intelligence is a bit modular – ask any iPad designer about creating technology for creatures whose minds are designed for interaction with tools via touch, for pushing, pulling and dragging objects that obey certain elementary physical laws.

Another misunderstanding about minds composed of domain specific mechanisms is that they require problem solving and information management to be piecemeal, with one module handling one problem at a time. This is simply not true – modules in the sense of domain specific mechanisms might handle more complex problems by interacting together – by sharing components of a larger task and engaging in complex feedback interactions (this post is already too long so I’ll give an example only if asked). With enough modules, modules handling abstract enough representations, and a few domain general mechanisms for handling storage and memory of socially learnt information, modular minds might also handle rapidly changing environments.
Consequently, unlike Kim, I’m still agnostic about whether our minds are partly or largely modular. They might be.

Mammal species differ in cortical structures but hardly at all in mid- and lower-brain structures. The latter are largely homologous with shared systemic functions. In humans, as in rats and cats, electrical stimulation of specific tracts in the lateral hypothalamus arouses the same autonomic responses and also the same proximate behavior – vigorous exploration of the environment. Jaak Panksepp calls this the SEEKING system and hypothesizes that it is, among other things, the neural basis for foraging behavior.

SEEKING, along with three other systems identified by Panksepp, are fundamental to mammalian sociality. The PANIC system, for instance, can be triggered by stimulating homologous subcortical structures in mammal brains, and these stimulations elicit the very same behaviors as when infants are separated from caregivers – distress cries, spikes in stress hormones, etc. This system is also triggered when adult humans suffer severe loneliness or grief and is quieted when attachments are reestablished – when social homeostasis obtains – or when chemical surrogates (such as heroin) are administered. But PANIC also moves us to seek and maintain social attachments; it is an anticipatory system against the pain of future loss. It is, in consequence, a robust processor and evaluator of social information, and a trigger for gathering more information. So our ancestors 400,000 years ago foraged together for more than food; they were warding off the pain of hunger and of isolation.

These and other ancient affective systems are primitives of the mammalian mind. They are ancestral capacities from which current cortical capacities evolved and without which cortical structures lose their efficacy. If, for example, we remove the cortex of non-human animals early in life, they nevertheless exhibit integrated affective behavior later in life. Humans who do not develop a cortex are much the same; despite devastating cognitive deficits, their behavior is affectively coherent.

We cannot hope to understand cortically-based capacities without understanding the pervasive and foundational effects of mammalian affective capacities. Nothing in mammalian psychology makes sense except in light of evolved affective capacities – including our capacities to respond to novelty in a changing world. We are evolved apprentices by virtue of being affective apprentices.

Doesn’t this beg the question of how an electronics-age world could come to exist among stone-age minds in the first place? I suppose an advanced life form from another world might visit, but shy of that… Still the concept of an adaptive lag might actually be a potential testing point – in regard to Bjorn’s question. I’m not a cultural anthropologist so this notion could simply be a naive one, but are there any extant groups with survival skill sets (Aboriginal perhaps?) so far removed from our “modern” (electronic-age) ones that they might be tested for an adaptive lag?