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https://www.selleckchem.com/products/Cyclopamine.html No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).Most accounts of behavior in nonhuman animals assume that they make choices to maximize expected reward value. However, model-free reinforcement learning based on reward associations cannot account for choice behavior in transitive inference paradigms. We manipulated the amount of reward associated with each item of an ordered list, so that maximizing expected reward value was always in conflict with decision rules based on the implicit list order. Under such a schedule, model-free reinforcement algorithms cannot achieve high levels of accuracy, even after extensive training. Monkeys nevertheless learned to make correct rule-based choices. These results show that monkeys' performance in transitive inference paradigms is not driven solely by expected reward and that appropriate inferences are made despite discordant reward incentives. We show that their choices can be explained by an abstract, model-based representation of list order, and we provide a method for inferring the contents of such representations from observed data. Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).Background Alpha-gal food allergy is a life-threatening, newly discovered condition with limited presence in authoritative information sources. Sufferers seeking diagnosis are likely to encounter clinicians unfamiliar with the condition. Objective To understand information practices of individuals diagnosed with alpha-gal allergy, how they obtained diagnosis, and their perceptions of health-care providers' awareness of the condition. Methods Semistructured interviews with open- and closed-ended questions were completed with a chro

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