8 research outputs found

    Perceiving deviance

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    I defend the claim that we have the capacity to perceptually represent objects and events in experience as deviating from an expectation, or, for short, as deviant. The rival hypothesis is that we may ascribe the property of deviance to a stimulus at a cognitive level, but that property is not a representational content of perceptual experience. I provide empirical reasons to think that, contrary to the rival hypothesis, we do perceptually represent deviance

    Instrumental Reasoning in Nonhuman Animals

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    Great apes and human children rationally monitor their decisions

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    Several species can detect when they are uncertain about what decision to make –revealed by opting out of the choice, or by seeking more information before deciding. But we do not know whether any nonhuman animals recognize when they need more information to make a decision because new evidence contradicts an already-formed belief. Here we explore this ability in great apes and human children. First, we show that after great apes saw new evidence contradicting a prior belief about which of two rewards was greater, they stopped to look for more information before deciding. They did not just register their own uncertainty, but attempted to resolve the contradiction between their belief and the new evidence, indicating rational monitoring of the decision-making process. Children did the same at five years of age, but not at three. In a second study, participants formed a belief about a reward’s location, but then a social partner contradicted them, by picking the opposite location. This time even three-year old children looked for more information, while apes ignored the disagreement. While apes were sensitive only to the conflict in physical evidence, the youngest children were more sensitive to peer disagreement than conflicting physical evidence.PostprintPeer reviewe

    Correlation between present-day model simulation of Arctic cloud radiative forcing and sea ice consistent with positive winter convective cloud feedback

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    A positive feedback on winter sea-ice loss, based on warming due to radiative forcing caused by the onset of convective clouds in response to sea-ice loss, has recently been proposed. This feedback has thus far been investigated using a hierarchy of climate models in high CO[subscript 2] scenarios. This paper examines the possibility that such feedback may be active within present-day like Arctic variability, using model output from two reanalysis models. It is emphasized that Arctic surface fluxes, radiative fluxes and clouds are effectively unconstrained by observations in reanalysis products. Consequently, the results here should be viewed only as a model study of the feedback in present-day model climate variability. Model winter sea ice and cloud radiative forcing are found to co-vary strongly and locally, consistent with a strong convective cloud feedback, which may contribute to sea ice variability. Furthermore, the anti-correlation between the two variables is found to be as strong in the model output analyzed here as in the IPCC global climate models that simulate the convective cloud feedback most strongly at high CO[subscript 2]. In those IPCC models the convective cloud feedback contributes to a total loss of winter sea ice in a CO[subscript 2] quadrupling scenario. These results do not necessarily prove that this feedback exists in the present-day Arctic and demonstrating this will require further study using actual Arctic observations.National Science Foundation (U.S.). Paleo Perspectives on Climate Change (ATM-0902844

    The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*

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    The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project
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