Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life.

Everyone allows that we can reason to a new belief from beliefs that we already have. Aristotle thought that we could also reason from beliefs to action. Practical Shape: A Theory of Practical Reasoning establishes this possibility of reasoning to action, in a way that allows also for reasoning to intention, hope, fear, and doubt. While many philosophers have found little sense in Aristotle's claim, Dancy offers a general theory of reasoning that is sensitive to current debates but still Aristotelian in spirit. The text clearly sets out the similarities between reasoning to action and reasoning to belief, which are far more striking than any dissimilarities. Its detailed account of practical reasoning, a topic inadequately covered in current literature, is presented in such a way as to be intelligible to a variety of readers, making it an ideal resource for students of philosophy but also of interest to academics in related disciplines.

Koslowski boldly criticizes many of the currently classic studies and musters a compelling set of arguments, backed by an exhaustive set of experiments carried out during the last decade.

The Mental Models Theory of Reasoning presents theoretical and empirical research on an area of growing interest, the status of mental models in deductive reasoning. As research in the framework of the mental models theory flourishes, this book answers a need to assess the contribution of the notion of training and content. It covers the central issues of propositional, relational, causal and probabilistic reasoning, and argumentation and development. In addition, this work presents data regarding strategies, argumentation, and the development of reasoning. Special features of this text include: *sharp theoretical analyses as well as important new empirical data offered by theorists who work in the framework of the mental models theory; *a critical and empirically driven account of content effects in conditional and linear reasoning; and *an original account on the influence of pragmatics on reasoning. The Mental Models Theory of Reasoning will be of interest to researchers and advanced students of cognitive psychology, and will be valuable to individuals working in Artificial Intelligence, as it highlights theoretical and empirical data on how humans use mental models when tackling deductive puzzles.

2022-23 SSC Reasoning Chapter-wise Solved Papers

“Brilliant...Timely and necessary.” —Financial Times “Especially timely as we struggle to make sense of how it is that individuals and communities persist in holding beliefs that have been thoroughly discredited.” —Darren Frey, Science If reason is what makes us human, why do we behave so irrationally? And if it is so useful, why didn’t it evolve in other animals? This groundbreaking account of the evolution of reason by two renowned cognitive scientists seeks to solve this double enigma. Reason, they argue, helps us justify our beliefs, convince others, and evaluate arguments. It makes it easier to cooperate and communicate and to live together in groups. Provocative, entertaining, and undeniably relevant, The Enigma of Reason will make many reasonable people rethink their beliefs. “Reasonable-seeming people are often totally irrational. Rarely has this insight seemed more relevant...Still, an essential puzzle remains: How did we come to be this way?...Cognitive scientists Hugo Mercier and Dan Sperber [argue that] reason developed not to enable us to solve abstract, logical problems...[but] to resolve the problems posed by living in collaborative groups.” —Elizabeth Kolbert, New Yorker “Turns reason’s weaknesses into strengths, arguing that its supposed flaws are actually design features that work remarkably well.” —Financial Times “The best thing I have read about human reasoning. It is extremely well written, interesting, and very enjoyable to read.” —Gilbert Harman, Princeton University

The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.