An Investigation of Human Inductive Biases in Causality and Probability Judgments

People often makes inductive inferences that go beyond the data that are given. In order to generate these inferences, people must rely on inductive biases - constraints on learning that guide conclusion from limited data. This thesis presents a survey of three topics concerning people's inductive biases.The first part of this thesis examines people's expectations about the strengths of causes in elemental causal induction - learning about the relationship between a single cause and effect. These expectations are formalized as prior probabilities in a Bayesian model. We estimate people's prior beliefs concerning the variables involved in such causal systems using the technique of iterated learning and demonstrate that a Bayesian model using the priors which are produced by this experiment performs well in predicting human behavior.The second part attempted to capture people's inductive biases in causal relationships by expressing them in logical rules, and assign prior probabilities in a way that favors simplicity. Experimental data shows that it captured how people responded to causal reasoning tasks and helped explain the biases towards simpler representations.The final part of this thesis investigated the differences in inductive biases between people from North American and Chinese cultures concerning judgment discrimination - how people differentiate choices of higher and lower values. We found cultural differences in a wide range of domains. The findings demonstrate the extents to which cultural influences contribute to judgment discrimination. These results were attributed to the differences in inductive biases of the two cultures.These investigations combined to demonstrate the strong and broad influence of inductive biases on human reasoning, learning, judgments, and decision making. They suggest the importance of understanding these inductive biases in our attempt to understand human cognition