Gorka Navarrete

Vinod Goel

Ira A. Noveck

Jérôme Prado

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. References Elqayam, S., & Over, D. E. (2012). Probabilities, beliefs, and dual processing: the paradigm shift in the psychology of reasoning. Mind & Society, 11(1), 27–40. doi:10.1007/s11299-012-0102-4 Khemlani, S. S., & Johnson-Laird, P. N. (2011). Theories of the syllogism: A meta-analysis, (571). Oaksford, M., & Chater, N. (2009). Précis of bayesian rationality: The probabilistic approach to human reasoning. The Behavioral and brain sciences, 32(1), 69–84; discussion 85–120. doi:10.1017/S0140525X09000284 Over, D. E. (2009). New paradigm psychology of reasoning. Thinking & Reasoning, 15(4), 431–438. doi:10.1080/13546780903266188

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

David R. Mandel

We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types.

Gorka Navarrete

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