Probabilistic Models, Learning Algorithms, Response Variability: Sampling in Cognitive Development

Bonawitz, Elizabeth; Griffiths, Tom; Gopnik, Alison

doi:10.31219/osf.io/q9pjb

Cited by 12 publications

(14 citation statements)

References 5 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

261

216

View full text Add to dashboard Cite

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“…The above contradictory results argue for more studies on the nature of infants' internal representations of probabilistic structure and the algorithms infants use to approximate the analytical solution (cf. Bonawitz, Denison, Griffiths, & Gopnik, 2014). These future studies should also overcome some of the limitations of the above studies in that some of them only use one preference trial and fail to control for parental interference (e.g.…”

Section: 1: the Developmental Origins Of Probabilistic Inferencementioning

confidence: 99%

Infant Development as Uncertainty Reduction: Bayesian Insights on Phonological Acquisition

Cmm¹,

Fusaroli²,

Keren‐Portnoy³

et al. 2020

Preprint

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Bayesian accounts of development posit that infants form predictions about the causes of sensory signals in their environment and select actions that resolve the largest amount of uncertainty. This paper considers how this approach to infant development can inform and unify insights from experimental research on early cognitive development and language acquisition. In order to establish whether infants’ early inferential abilities conform to the basic assumptions of a Bayesian approach to cognition, we first conduct a systematic review of experimental studies on infants’ ability to form predictions about probabilistic contingencies. These studies provide evidence that infants exhibit sensitivity to the probabilistic structure of their surrounding environment and recruit their own uncertainty to guide their exploration of information in the world. We then demonstrate how these Bayesian computational principles may apply in the context of language acquisition by conducting a second systematic review of experiments on the facilitative role of infants’ vocal production. These studies indicate that infants are more likely to produce and allocate attention to those speech sounds that best afford the opportunity to reduce prediction error over time. This paper demonstrates how Bayesian models of cognition can offer a unifying framework to advance the understanding of cognitive processes in early development. This framework not only gives a larger perspective to current findings, but also provides conceptual tools to enable investigation of infants’ individual trajectories of behavioural change.

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“…We call this the START model, for "STochastic search with smART initializations." The notion of theory learning as a stochastic search has been proposed as one way of connecting Bayesian models of cognitive development with the dynamics of children's learning (Ullman et al, 2012;Bonawitz et al, 2014), but it also offers a promising way to think about learning in our dynamic physics tasks.…”

Section: Smart Initialization and Short Search: The Start Modelmentioning

confidence: 99%

Learning Physical Parameters From Dynamic Scenes

Ullman¹

2018

Preprint

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Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

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Probabilistic Models, Learning Algorithms, Response Variability: Sampling in Cognitive Development

Cited by 12 publications

References 5 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

Infant Development as Uncertainty Reduction: Bayesian Insights on Phonological Acquisition

Learning Physical Parameters From Dynamic Scenes

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