Lorenz proposed in his (1935) articulation of a theory of behavioral instincts that the objective of ethology is to distinguish behaviors that are “innate” from behaviors that are “learned” (or “acquired”). Lorenz’s motive was to open the investigation of certain “adaptive” behaviors to evolutionary theorizing. Accordingly, since innate behaviors are “genetic”, they are open to such investigation. By Lorenz’s light an innate/acquired or learned dichotomy rested on a familiar Darwinian distinction between genes and environments. Ever since Lorenz, ascriptions of innateness (...) have become widespread in the cognitive, behavioral, and biological sciences. The trend continues despite decades of strong arguments that show, in particular, the dichotomy that Lorenz invoked in his theory of behavioral instincts is literally false: no biological trait is the product of genes alone. Some critics suggest that the failure of Lorenz’s account shows that innateness is not well-defined in biology and the practice of ascribing innateness to various biological traits should be dropped from respectable science. Elsewhere (Ariew 1996) I argued that despite the arguments of critics, there really is a biological phenomenon underlying the concept of innateness. On my view, innateness is best understood in terms of C.H. Waddington’s concept of “canalization”, i.e. the degree to which a trait is innate is the degree to which its developmental outcome is canalized. The degree to which a developmental outcome is canalized is the degree to which the developmental process is bound to produce a particular endstate despite environmental fluctuations both in the development’s initial state and during the course of development. The canalization account differs in many ways to the traditional ways that ethologists such as Konrad Lorenz originally understood the concept of innateness. Most importantly, on the canalization account the distinction between innate and acquired is not a dichotomy, as Konrad Lorenz had it, but rather a matter of degree difference that lies along a spectrum with highly canalized development outcomes on the one end and highly environmentally sensitive development outcomes on the other end. Nevertheless, I justified the canalization account on the basis of a set of desiderata or criteria that I suggested falls-out of what seemed uncontroversial about Lorenz’s account of innateness (briefly): innateness is a property of a developing individual, innateness denotes environmental stability, and innate-ascriptions are useful in certain natural selection explanations (more below). From that same set of desiderata I argued (in my 1996) that neither the concept of heritability nor of norms of reactions—two concepts from population genetics—suffice to ground innateness. In this essay, I wish to provide further support of the canalization account in two ways. First, I wish to better motivate the desiderata by revisiting a debate between Konrad Lorenz and Daniel Lehrman over the meaning and explanatory usefulness of innate ascriptions in ethology. Second, I wish to compare my canalization account of innateness with accounts proposed by contemporary philosophers, one by Stephen Stich (1975), another by Elliott Sober (forthcoming), and a third by William Wimsatt (1986). (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Cognitive scientists often employ the notion of innateness without defining it. The issue is, how is innateness defined in biology? Some critics contend that innateness is not a legitimate concept in biology. In this paper I will argue that it is. However, neither the concept of high heritability nor the concept of flat norm of reaction define innateness. An adequate account is found in developmental biology. I propose that innateness is best defined in terms of C. H. Waddington's concept of (...) canalization. (shrink)