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120
Language Evolution by Iterated Learning With Bayesian Agents
, 2007
"... 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 Ba ..."
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Cited by 39 (8 self)
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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 widelyused 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 expectationmaximization (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.
Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
 Psychological Review
, 2006
"... A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probab ..."
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Cited by 36 (7 self)
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A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trialbytrial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The backpropagation of target values to attention allows the model to show trialorder effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.
Encoding multielement scenes: Statistical learning of visual feature hierarchies
 Journal of Experimental Psychology: General
, 2005
"... The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind t ..."
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Cited by 33 (6 self)
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The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.
Seeing versus doing: Two modes of accessing causal knowledge
 Journal of Experimental Psychology: Learning, Memory, and Cognition
, 2005
"... The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, w ..."
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Cited by 32 (8 self)
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The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and
Assessing the Distinguishability of Models and the Informativeness of Data
"... A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by mod ..."
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Cited by 30 (9 self)
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A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by model B, and vice versa (a technique that we call landscaping), much safer inferences can be made about the meaning of a models fit to data. We demonstrate the landscaping technique using four models of retention and 77 historical data sets, and show how the method can be used to (1) evaluate the distinguishability of models, (2) evaluate the informativeness of data in distinguishing between models, and (3) suggest new ways to distinguish between models. The generality of the method is demonstrated in two other research areas (information integration and categorization), and its relationship to the important notion of model complexity is discussed.
Bayesian generic priors for causal learning
 Psychological Review
, 2008
"... The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high ..."
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Cited by 28 (2 self)
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The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.
Exemplar models as a mechanism for performing Bayesian inference
"... Probabilistic models have recently received much attention as accounts of human cognition. problems behind cognitive tasks and their optimal solutions, rather than considering mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models that ..."
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Cited by 27 (7 self)
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Probabilistic models have recently received much attention as accounts of human cognition. problems behind cognitive tasks and their optimal solutions, rather than considering mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models that use an inventory of stored examples to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling, and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference. Exemplar models and Bayesian inference 3 Exemplar models as a mechanism for performing Bayesian inference
Learning a Theory of Causality
"... The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework, and the role for innate structure. We focus on knowledge about causality, seen as a domaingeneral intuit ..."
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Cited by 21 (7 self)
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The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework, and the role for innate structure. We focus on knowledge about causality, seen as a domaingeneral intuitive theory, and ask whether this knowledge can be learned from cooccurrence of events. We begin by phrasing the causal Bayes nets theory of causality, and a range of alternatives, in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence, and find that a collection of simple “perceptual input analyzers ” can help to bootstrap abstract knowledge. Together these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality, but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion. Preprint June 2010—to appear in Psych. Review.
The Role of Causality in Judgment Under Uncertainty
"... Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (Tversky & Kahneman, 1974) or frequentist (Gigerenzer & Hoffrage, 1995) norms. We argue that these frameworks have limited ability t ..."
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Cited by 21 (0 self)
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Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (Tversky & Kahneman, 1974) or frequentist (Gigerenzer & Hoffrage, 1995) norms. We argue that these frameworks have limited ability to explain the success and flexibility of people's realworld judgments, and propose an alternative normative framework based on Bayesian inferences over causal models. Deviations from traditional norms of judgment, such as "baserate neglect", may then be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Four experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms.
Intuitive theories as grammars for causal inference
 In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation
, 2007
"... This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recogniz ..."
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Cited by 21 (8 self)
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This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing