This paper examines the recent literature on meta-cognitive processes in non-human animals, arguing that in each case the data admit of a simpler, purely first-order, explanation. The topics discussed include the alleged monitoring of states of certainty and uncertainty, knowledge-seeking behavior in conditions of uncertainty, and the capacity to know whether or not the information needed to solve some problem is stored in memory. The first-order explanations advanced all assume that beliefs and desires come in various different strengths, or degrees.

This paper examines the recent literature on meta-cognitive processes in non-human animals, arguing that in each case the data admit of a simpler, purely first-order, explanation. The topics discussed include the alleged monitoring of states of certainty and uncertainty, the capacity to know whether or not one has perceived something, and the capacity to know whether or not the information needed to solve some problem is stored in memory. The first-order explanations advanced all assume that beliefs and desires come in (...) various different _strengths_, or _degrees_. (shrink)

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses (...) on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints. (shrink)

This paper argues that a set of questioning attitudes are among the foundations of human and animal minds. While both verbal questioning and states of curiosity are generally explained in terms of metacognitive desires for knowledge or true belief, I argue that each is better explained by a prelinguistic sui generis type of mental attitude of questioning. I review a range of considerations in support of such a proposal and improve on previous characterizations of the nature of these attitudes. I (...) then broaden their explanatory scope to include a number of forms of exploratory search. The paper has three main goals: to characterize the nature of the questioning attitudes, outlining their causal role and type of content; to argue that they are fundamental components of the mind, being widespread among animals and not constructed during ontogeny out of other attitudes; and to suggest that they explain a great deal more behavior than one might think. (shrink)