It is clear throughout Cognitive Gadgets Heyes believes the development of cognitive capacities results from the interaction of genes and experience. However, she opposes cognitive instincts theorists to her own view that uniquely human capacities are cognitive gadgets. Instinct theorists believe that cognitive capacities are substantially produced by selection, with the environment playing a triggering role. Heyes’s position is that humans have similar general learning capacities to those present across taxa, and that sophisticated human cognition is substantially created by our (...) socioculturally transmitted environment. It is a core strategy of Heyes to provide evidence of learning altering a cognitive capacity to conclude that a capacity is a cognitive gadget and not an instinct. We draw on recent work on the evolution of learning preparedness to examine the adequacy of this strategy. In particular, we analyse experimental evolution work showing how selection affects cognition within the laboratory. First, this work reveals that change due to learning can still be retained under genetic assimilation. This suggests that domain-specific adaptation can coexist with learning, moderate nativism, an option missed by the instinct versus gadget distinction. Second, we describe the conditions that select for increased preparedness in learning: certainty, reliability, and particular costs. We consider how these conditions can be used when conducting evolutionary reasoning about cognition, applying them to the important capacity for imitation. We find that the conditions lend theoretical support to moderate nativism about the capacity to imitate, which is supported by psychological evidence. (shrink)

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by (...) computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. (shrink)

Sean B. Carroll’s new book, The Serengeti Rules: The Quest to Discover How Life Works and Why it Matters, is a well-written mix of history of science and philosophy of biology. In his book, Carroll articulates a set of ecological generalisations, the Serengeti Rules, which are supposed to make salient the structures in ecosystems that ensure the persistence of those ecosystems. In this essay review, I evaluate Carroll’s use of the controversial concept of regulation and his thesis that ecosystems have (...) a natural balance comparable to that of human bodies. My conclusion is optimistic. Carroll’s generalisations provide a tool-kit for building relatively simple models that are accurate enough to be widely applied in experimental ecology and conservation science, guiding interventions upon unhealthy ecosystems. (shrink)