journal homepage: www.elsevier.com/locate/COGNIT The evolution of frequency distributions: Relating regularization

regularization to inductive biases through iterated learning

Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the capacities of the agents involved. In the 1930s, Sir Frederic Bartlett explored the influence of memory biases in “serial reproduction” of information, in which one person’s reconstruction of a stimulus from memory becomes the stimulus seen by the next person. These experiments were done using relatively uncontrolled stimuli such as pictures and stories, but suggested that serial reproduction would transform information in a way that reflected the biases inherent in memory. We formally analyze serial reproduction using a Bayesian model of reconstruction from memory, giving a general result characterizing the effect of memory biases on information transmission. We then test the predictions of this account in two experiments using simple one-dimensional stimuli. Our results provide theoretical and empirical justification for the idea that serial reproduction reflects memory biases. 1

Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called “iterated learning, ” in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people’s predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model (Griffiths & Tenenbaum, 2006) and a recently-proposed alternative model that assumes people store only a few instances of each type of event in memory (MinK; Mozer, Pashler, & Homaei, 2008). Although testing these models using standard experimental procedure is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.

In 1969, Berlin and Kay proposed that there exist crosscultural universals in the form of basic color terms. To test this hypothesis, the World Color Survey (WCS) collected color naming data from 110 non-industrial societies, identifying regularities in the structure of languages with different numbers of terms. This leaves us with the question of where these universals come from. We use a simple model of cultural evolution known as “iterated learning ” to explore the hypothesis that universals emerge from human perceptual and learning biases. We conducted an experiment simulating the process of cultural transmission in the laboratory, and compared the results to the systems of color terms that appear in the WCS data. Our results show that cultural evolution results in convergence of systems of color terms towards a form consistent with the WCS, supporting the hypothesis that universals are the result of perceptual and learning biases.