Tom Griffiths

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners

Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity.

George Seddon takes a cheeky pride in his native wit, in his ability to improvise, invent, and to trip lightly over difficult terrain. These are the bush virtues of the Man from Snowy River. In this essay I reflect upon the interdisciplinary (and undisciplined) nature of Seddon's vision and practice, and place him in a tradition of nature and landscape writing in Australia that goes back to the 19th century. But I also suggest that he has been ahead of his time in many ways, particularly in his anticipation of the field of environmental history. He has been one of the people who has generated new environmental narratives that indigenize Australia, and which undermine the dominant imperialist, diffusionist, one-way accounts of origins. In this sense, Seddon has creatively embraced - and sometimes overturned - the predicament of the antipodean. Seddon happily combines poetry and practice. He became known as the `powerline man' in landscape planning, and enjoyed quoting William Blake to the State Electricity Commission of Victoria in his report on the Loy Yang coal field development. By analysing his 1998 submission to the Snowy Water Inquiry, the essay shows how Seddon unites `good science, good planning, good design, and good communication', and offers environmental wisdom that is both highly intellectual and extremely practical