The study of intellectual humility is still in its early stages and issues of definition and measurement are only now being explored. To inform and guide the process of defining and measuring this important intellectual virtue, we conducted a series of studies into the implicit theory – or ‘folk’ understanding – of an intellectually humble person, a wise person, and an intellectually arrogant person. In Study 1, 350 adults used a free-listing procedure to generate a list of descriptors, one for (...) each person-concept. In Study 2, 335 adults rated the previously generated descriptors by how characteristic each was of the target person-concept. In Study 3, 344 adults sorted the descriptors by similarity for each person-concept. By comparing and contrasting the three person-concepts, a complex portrait of an intellectually humble person emerges with particular epistemic, self-oriented, and other-oriented dimensions. (shrink)
People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...) participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. (shrink)
Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)
A brief statement of medieval linguistic analysis as found chiefly in the works of Thomas Aquinas. A thin survey of contemporary analysis is offered in order to contrast the purported acceptance of analysis as the end of philosophy with Thomas' use of analysis as method. Unfortunately, most of the constructive work in language analysis during the past ten years is not considered. The brevity of the text precludes persuasive treatment; yet the book succeeds in its expressed purpose of (...) sketching some chief differences between the medieval and contemporary approaches in the hope that the confrontation of these currents will bring to the fore the analytic work of Aquinas himself.--D. P. B. (shrink)
Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.
