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Who Peeked? Children Infer the Likely Cause of Improbable Success.
Dev Sci
January 2025
Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada.
Some outcomes are brought about by intentional agents with access to information and others are not. Children use a variety of cues to infer the causes of outcomes, such as statistical reasoning (e.g.
View Article and Find Full Text PDFProbabilistic medical predictions of large language models.
NPJ Digit Med
December 2024
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and decision-making. While explicit prompts can lead LLMs to generate probability estimates, their numerical reasoning limitations raise concerns about reliability.
View Article and Find Full Text PDFHow Likely Is it that I Would Act the Same Way: Modeling Moral Judgment During Uncertainty.
Cogn Sci
November 2024
Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign.
Moral rules come with exceptions, and moral judgments come with uncertainty. For instance, stealing is wrong and generally punished. Yet, it could be the case that the thief is stealing food for their family.
View Article and Find Full Text PDFModus Darwin redux.
Stud Hist Philos Sci
December 2024
Department of Philosophy, University of British Columbia, 1866 Main Mall E370, Vancouver, BC V6T 1Z1, Canada. Electronic address:
How should we evaluate Darwin and Wallace's arguments for common ancestry over separate ancestry? Elliott Sober defends a likelihood reconstruction of Darwin's reasoning that he dubs modus Darwin: similarity, therefore common ancestry. One assumption of Sober's approach is that separate ancestors have traits that are probabilistically independent. I motivate an objection to this assumption by appeal to 19th century naturalist alternatives such as those of Geoffroy and Owen.
View Article and Find Full Text PDFBlending simulation and abstraction for physical reasoning.
Cognition
January 2025
Department of Psychology, Harvard University, 52 Oxford St, Cambridge MA 02138, USA; Center for Brains, Minds, and Machines, MIT, 43 Vassar St, Cambridge 02139, USA.
How are people able to understand everyday physical events with such ease? One hypothesis suggests people use an approximate probabilistic simulation of the world. A contrasting hypothesis is that people use a collection of abstractions or features. While it has been noted that the two hypotheses explain complementary aspects of physical reasoning, there has yet to be a model of how these two modes of reasoning can be used together.
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