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The Reasoning Brain: The Interplay between Cognitive Neuroscience and Theories of Reasoning

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889451180 Year: Pages: 178 DOI: 10.3389/978-2-88945-118-0 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2017-07-06 13:27:36
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Abstract

Despite the centrality of rationality to our identity as a species (let alone the scientific endeavour), and the fact that it has been studied for several millennia, the present state of our knowledge of the mechanisms underlying logical reasoning remains highly fragmented. For example, a recent review concluded that none of the extant (12!) theories provide an adequate account (Khemlani & Johnson- Laird, 2011), while other authors argue that we are on the brink of a paradigm change, where the old binary logic framework will be washed away and replaced by more modern (and correct) probabilistic and Bayesian approaches (see for example Elqayam & Over, 2012; Oaksford & Chater, 2009; Over, 2009). Over the past 15 years neuroscience brain imaging techniques and patient studies have been used to map out the functional neuroanatomy of reasoning processes. The aim of this research topic is to discuss whether this line of research has facilitated, hindered, or has been largely irrelevant for understanding of reasoning processes. The answer is neither obvious nor uncontroversial. We would like to engage both the cognitive and the neuroscience community in this discussion. Some of the questions of interest are: How have the data generated by the patient and neuroimaging studies: • influenced our thinking about modularity of deductive reasoning • impacted the debate between mental logic theory, mental model theory and the dual mechanism accounts • affected our thinking about dual mechanism theories • informed discussion of the relationship between induction and deduction • illuminated the relationship between language, visual spatial processing and reasoning • affected our thinking about the unity of deductive reasoning processes Have any of the cognitive theories of reasoning helped us explain deficits in certain patient populations? Do certain theories do a better job of this than others? Is there any value to localizing cognitive processes and identifying dissociations (for reasoning and other cognitive processes)? What challenges have neuroimaging data raised for cognitive theories of reasoning? How can cognitive theory inform interpretation of patient data or neuroimaging data? How can patient data or neuroimaging data best inform cognitive theory? This list of questions is not exhaustive. Manuscripts addressing other related questions are welcome. We are interested in hearing from skeptics, agnostics and believers, and welcome original research contributions as well as reviews, methods, hypothesis & theory papers that contribute to the discussion of the current state of our knowledge of how neuroscience is (or is not) helping us to deepen our understanding of the mechanisms underlying logical reasoning processes.

Computational Methods for Understanding Complexity: The Use of Formal Methods in Biology

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450428 Year: Pages: 111 DOI: 10.3389/978-2-88945-042-8 Language: English
Publisher: Frontiers Media SA
Subject: General and Civil Engineering --- Biotechnology --- Science (General) --- Genetics
Added to DOAB on : 2018-02-27 16:16:44
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The complexity of living organisms surpasses our unaided habilities of analysis. Hence, computational and mathematical methods are necessary for increasing our understanding of biological systems. At the same time, there has been a phenomenal recent progress allowing the application of novel formal methods to new domains. This progress has spurred a conspicuous optimism in computational biology. This optimism, in turn, has promoted a rapid increase in collaboration between specialists of biology with specialists of computer science. Through sheer complexity, however, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. We are in the process of learning what kind of model and what kind of analysis and synthesis techniques to use for a particular problem. Some existing formalisms have been readily used in biological problems, others have been adapted to biological needs, and still others have been especially developed for biological systems. This Research Topic has examples of cases (1) employing existing methods, (2) adapting methods to biology, and (3) developing new methods. We can also see discrete and Boolean models, and the use of both simulators and model checkers. Synthesis is exemplified by manual and by machine-learning methods. We hope that the articles collected in this Research Topic will stimulate new research.

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