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"REASONING"
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Analogical investigations : historical and cross-cultural perspectives on human reasoning
Western philosophy and science are responsible for constructing some powerful tools of investigation, aiming at discovering the truth, delivering robust explanations, verifying conjectures, showing that inferences are sound and demonstrating results conclusively. By contrast reasoning that depends on analogies has often been viewed with suspicion. Professor Lloyd first explores the origins of those Western ideals, criticises some of their excesses and redresses the balance in favour of looser, admittedly non-demonstrative analogical reasoning. For this he takes examples both from ancient Greek and Chinese thought and from the materials of recent ethnography to show how different ancient and modern cultures have developed different styles of reasoning. He also develops two original but controversial ideas, that of semantic stretch (to cast doubt on the literal/metaphorical dichotomy) and the multidimensionality of reality (to bypass the realism versus relativism and nature versus nurture controversies). Cover p. 4.
Book
Correction: Clinical reasoning education in the clerkship years: A cross-disciplinary national needs assessment
[This corrects the article DOI: 10.1371/journal.pone.0273250.].
Journal Article
Neurosymbolic AI: the 3rd wave
Current advances in Artificial Intelligence (AI) and Machine Learning have achieved unprecedented impact across research communities and industry. Nevertheless, concerns around trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neurosymbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability by offering symbolic representations for neural models. In this paper, we relate recent and early research in neurosymbolic AI with the objective of identifying the most important ingredients of neurosymbolic AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. Finally, this review identifies promising directions and challenges for the next decade of AI research from the perspective of neurosymbolic computing, commonsense reasoning and causal explanation.
Journal Article
Visual Spatial Reasoning
Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: The human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs’ by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.
Journal Article
Why do humans reason? Arguments for an argumentative theory
Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.
Journal Article