Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

Bonawitz, Elizabeth; Denison, Stephanie; Gopnik, Alison; Griffiths, Thomas L.

doi:10.1016/j.cogpsych.2014.06.003

Cited by 114 publications

(96 citation statements)

References 60 publications

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“…It is impossible for any system, human or computer, to consider and compare all of the possible hypotheses relevant to a realistic learning problem. Computer scientists and statisticians often use "sampling" to help solve this problem-stochastically selecting some hypotheses rather than others-and there is evidence that people, including young children, do something similar (43)(44)(45).…”

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confidence: 99%

Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

Gopnik

O’Grady

Lucas

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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How was the evolution of our unique biological life history related to distinctive human developments in cognition and culture? We suggest that the extended human childhood and adolescence allows a balance between exploration and exploitation, between wider and narrower hypothesis search, and between innovation and imitation in cultural learning. In particular, different developmental periods may be associated with different learning strategies. This relation between biology and culture was probably coevolutionary and bidirectional: life-history changes allowed changes in learning, which in turn both allowed and rewarded extended life histories. In two studies, we test how easily people learn an unusual physical or social causal relation from a pattern of evidence. We track the development of this ability from early childhood through adolescence and adulthood. In the physical domain, preschoolers, counterintuitively, perform better than school-aged children, who in turn perform better than adolescents and adults. As they grow older learners are less flexible: they are less likely to adopt an initially unfamiliar hypothesis that is consistent with new evidence. Instead, learners prefer a familiar hypothesis that is less consistent with the evidence. In the social domain, both preschoolers and adolescents are actually the most flexible learners, adopting an unusual hypothesis more easily than either 6-y-olds or adults. There may be important developmental transitions in flexibility at the entry into middle childhood and in adolescence, which differ across domains.causal reasoning | social cognition | cognitive development | adolescence | life history O ne of the most distinctive biological features of human beings is our unusual life history. Compared with our closest primate relatives, we have a dramatically extended childhood, including an exceptionally long middle childhood and adolescence. Moreover, humans have shorter interbirth intervals than our closest primate relatives, producing an even greater number of less-capable children (1). There is evidence for other human adaptations that helped cope with this flood of needy young. In contrast to our closest primate relatives, human children enjoy the benefits of care from three sources in addition to biological mothers: pair-bonded fathers (2), alloparents (3), and postmenopausal women, in particular, grandmothers (4).It may seem evolutionarily paradoxical that humans would have developed a life history that includes such expensive and vulnerable young for such a long period. However, across many different species, including birds and both placental and marsupial mammals, there is a very general (although not perfect) correlation between relative brain size, intelligence and a reliance on learning, and an extended period of immaturity (5-7). This correlation suggests a relation between our distinctive human life history and our equally distinctive large brains and reliance on learning, particularly cultural learning. Such a relation between biology and culture ...

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mentioning

confidence: 99%

Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

Gopnik

O’Grady

Lucas

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

256

216

View full text Add to dashboard Cite

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“…For example, Shi, Griffiths, Feldman, & Sanborn (2010) discuss how exemplar models may provide a possible mechanism for implementing Bayesian inference, since these models allow an approximation process called importance sampling. Other examples include the work of Bonawitz et al (2011), who discuss how a simple sequential algorithm can be used to approximate Bayesian inference in a basic causal learning task, and that of Pearl, Goldwater, and Steyvers (2011), who (as described in section 3) investigated various online algorithms for Bayesian models of word segmentation. See also McClelland (1998) for a discussion of how neural network architectures can be used to approximate optimal Bayesian inference (again emphasizing that the connectionist and Bayesian frameworks are not so much in opposition as they are addressing different aspects of the learning problem, with one focusing on the description of the task and the other focusing on the implementation).…”

Section: Algorithmsmentioning

confidence: 99%

Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

Pearl¹,

Goldwater²

2016

Oxford Handbooks Online

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Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.

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“…Another example is Monte Carlo approximation of Bayesian inference (e.g., Denison, Bonawitz, Gopnik, & Griffiths, 2013;Shi, Griffiths, Feldman, & Sanborn, 2010;Bonawitz, Denison, Chen, Gopnik, & Griffiths, 2011), which may show how a learning process could typically produce correct conclusions by approximating (in a bounded way) complex functions using simple evolved capacities (e.g., exemplar-based reasoning).…”

Section: A General Framework For Adaptively Rational Learningmentioning

confidence: 99%

Adaptively Rational Learning

Wellen

Danks

2015

Minds & Machines

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Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent's goals. We then use this high-level formal framework on specific cases by (a) demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and (b) characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.

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Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

Cited by 114 publications

References 60 publications

Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

Adaptively Rational Learning

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