Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the (...) reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma–Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information‐theoretic formalism. (shrink)

One way to learn about the world is by asking questions. We investigate how younger children (7- to 8-year-olds), older children (9- to 11-year-olds), and young adults (17- to 18-year-olds) ask questions to identify the cause of an event. We find a developmental shift in children’s reliance on hypothesis-scanning questions (which test hypotheses directly) versus constraint-seeking questions (which reduce the space of hypotheses), but also that all age groups ask more constraint-seeking questions when hypothesis-scanning questions are least likely to pay (...) off: When the solution is one among equally likely alternatives (Study 1) or when the problem is difficult (Studies 1 and 2). These findings are the first to demonstrate that even young children dynamically adapt their strategies for inquiry to increase the efficiency of information search. (shrink)

In some circumstances, children of 5 produce identical classifications to 10 year olds when asked to sort a collection of objects. This has been interpreted as meaning that the process of constructing classifications is very similar at 5 and 10 years. But this conclusion rests on a comparison of the product of children's sortings, rather than on a study of their activity in producing sortings. The present paper argues that the process of classifying is in fact very different for 5 (...) and 10 year olds: whereas the older children treat the whole classification as a single unit composed of interrelated classes, the younger children proceed as though each class were independent of the others. At around 7 years, there is evidence for a transitional phase, in which children directly work on the relations between classes and so organize their initially “juxtaposed” procedures into more coherent systems. The tendency to work on the relations between elements in the process of classifying is not, however, simply an age‐related phenomenon: evidence for a similar effect can be observed in children throughout the age range of 5 to 10 years, during the course of short experimental sessions. Furthermore, the effect is not merely a response to errors or difficulties: the tendency to work on classifying relations can be seen in 5‐year‐old children in a task in which they are already successful. It seems that children spontaneously discover and use information about their procedures and the relations between them. This process is an end in itself. It occurs whether or not its results yield more successful performance. The approach taken in this paper is not confined to one area of behavior. This analysis of children's classifying fits closely with work in a very different domain, i.e. Karmiloff‐Smith's work on children's language and other representational systems. The analysis has implications for computer models of skill acquisition, as well as for psychological theories of development. (shrink)