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From Is to Ought: The Place of Normative Models in the Study of Human Thought

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198962 Year: Pages: 187 DOI: 10.3389/978-2-88919-896-2 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Psychology
Added to DOAB on : 2016-01-19 14:05:46
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In the study of human thinking, two main research questions can be asked: “Descriptive Q: What is human thinking like? Normative Q: What ought human thinking be like?” For decades, these two questions have dominated the field, and the relationship between them generated many a controversy. Empirical normativist approaches regard the answers to these questions as positively correlated – in essence, human thinking is what it ought to be (although what counts as the ‘ought’ standard is moot). In contemporary theories of reasoning and decision making, this is often associated with a Panglossian framework, an adaptationist approach which regards human thinking as a priori rational. In contrast, prescriptive normativism sees the answers to these two questions as negatively correlated. Normative models are still relevant to human thought, but human behaviour deviates from them quite markedly (with the invited conclusion that humans are often irrational). Prescriptive normativism often results in a Meliorist agenda, which sees rationality as amenable to education. Both empirical and prescriptive normativism can be contrasted with a descriptivist framework for psychology of human thinking. Following Hume’s strict divide between the ‘is’ and the ‘ought’, descriptivism regards the descriptive and normative research questions as uncorrelated, or dissociated, with only the former question suitable for psychological study of human behaviour. This basic division carries over to the relation between normative (‘ought’) rationality, based on conforming to normative standards; and instrumental (‘is’) rationality, based on achieving one’s goals. Descriptivist approaches regard the two as dissociated, whereas normativist approaches tend to see them as closely linked, with normative arguments defining and justifying instrumental rationality. This research topic brings together diverse contributions to the continuing debate. Featuring contributions from leading researchers in the field, the e-book covers a wide range of subjects, arranged by six sections: The standard picture: Normativist perspectivesIn defence of soft normativismExploring normative modelsDescriptivist perspectivesEvolutionary and ecological accountsEmpirical reports With a total of some 24 articles from 55 authors, this comprehensive treatment includes theoretical analyses, meta-theoretical critiques, commentaries, and a range of empirical reports. The contents of the Research Topic should appeal to psychologists, linguists, philosophers and cognitive scientists, with research interests in a wide range of domains, from language, through reasoning, judgment and decision making, and moral judgment, to epistemology and theory of mind, philosophical logic, and meta-ethics.

Improving Bayesian Reasoning: What Works and Why?

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889197453 Year: Pages: 207 DOI: 10.3389/978-2-88919-745-3 Language: English
Publisher: Frontiers Media SA
Subject: Psychology --- Science (General)
Added to DOAB on : 2016-04-07 11:22:02
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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.

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