We argue that an understanding of the faculty of language requires substantial interdisciplinary cooperation. We suggest how current developments in linguistics can be profitably wedded to work in evolutionary biology, anthropology, psychology, and neuroscience. We submit that a distinction should be made between the faculty of language in the broad sense (FLB)and in the narrow sense (FLN) . FLB includes a sensory-motor system, a conceptual-intentional system, and the computational mechanisms for recursion, providing the capacity to generate an infinite range of expressions from a finite set of elements. We hypothesize that FLN only includes recursion and is the only uniquely human component of the faculty of language. We further argue that FLN may have evolved for reasons other than language, hence comparative studies might look for evidence of such computations outside of the domain of communication (for example, number, navigation, and social relations).

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The faculty of language: what is it, who has it, and how did it evolve?

This accessible guide provides clear, practical explanations of key research methods in business studies, presenting a step-by-step approach to data collection, analysis and problem solving. Readers will learn how to formulate a research question, choose an appropriate research method, argue and motivate, collect and analyse data, and present findings in a logical and convincing manner. The authors evaluate various qualitative and quantitative methods and their consequences, guiding readers to the most appropriate research design for particular questions. Furthermore, the authors provide instructions on how to write reports and dissertations in a clearly structured and concise style. Now in its fifth edition, this popular textbook includes new and dedicated chapters on data collection for qualitative research, qualitative data analysis, data collection for quantitative research, multiple regression, and additional methods of quantitative analysis. Cases and examples have been updated throughout, increasing the applicability of these research methods across various situations.

Research methods in business studies

Rev Soc Bras Fonoaudiol. 2007;12(4):343-5 (1) Pós-graduanda em Lingüística e Língua Portuguesa pela Pontifícia Universidade Católica de Minas Gerais – PUC-Minas – Belo Horizonte (MG), Brasil; Professora da Pontifícia Universidade Católica de Minas Gerais – PUCMinas – Belo Horizonte (MG), Brasil. Endereço para correspondência: Denise Brandão de Oliveira e Britto. R. Desembargador Jorge Fontana, 250/1201, bloco 1, Belvedere, Belo Horizonte – MG, CEP 30320-670. E-mail: denisebrandao@pucminas.br The faculty of language

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The faculty of language

accademiche [38]. Ada [45]. Adrian [45]. African [56]. Age [39, 49, 61]. Al [23]. Al-Rawi [23]. Aldous [68]. Alex [15]. Allure [46]. America [60, 66]. American [49, 69, 61, 52]. ancienne [25]. Andreas [28]. Angela [42]. Animals [16]. Ann [26]. Anna [19, 47]. Annotated [46]. Annotations [28]. Anti [37]. Anti-Copernican [37]. Antibiotic [64]. Anxiety [51]. Apocalyptic [61]. Archaeology [26]. Ark [36]. Artisan [32]. Asylum [48]. Atri [54]. Audra [65]. Australia [41]. Authorship [15]. Axelle [29].

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A Complete Bibliography of Publications in Isis, 2000{2009

In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility -- the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993.

Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research

Scientists and laypeople alike use the sense of understanding that an explanation conveys as a cue to good or correct explanation. Although the occurrence of this sense or feeling of understanding is neither necessary nor sufficient for good explanation, it does drive judgments of the plausibility and, ultimately, the acceptability, of an explanation. This paper presents evidence that the sense of understanding is in part the routine consequence of two well‐documented biases in cognitive psychology: overconfidence and hindsight. In light of the prevalence of counterfeit understanding in the history of science, I argue that many forms of cognitive achievement do not involve a sense of understanding, and that only the truth or accuracy of an explanation make the sense of understanding a valid cue to genuine understanding.

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Scientific Explanation And The Sense Of Understanding

Bishop and Trout here present a unique and provocative new approach to epistemology (the theory of human knowledge and reasoning). Their approach aims to liberate epistemology from the scholastic debates of standard analytic epistemology, and treat it as a branch of the philosophy of science. The approach is novel in its use of cost-benefit analysis to guide people facing real reasoning problems and in its framework for resolving normative disputes in psychology. Based on empirical data, Bishop and Trout show how people can improve their reasoning by relying on Statistical Prediction Rules (SPRs). They then develop and articulate the positive core of the book. Their view, Strategic Reliabilism, claims that epistemic excellence consists in the efficient allocation of cognitive resources to reliable reasoning strategies, applied to significant problems. The last third of the book develops the implications of this view for standard analytic epistemology; for resolving normative disputes in psychology; and for offering practical, concrete advice on how this theory can improve real people's reasoning. This is a truly distinctive and controversial work that spans many disciplines and will speak to an unusually diverse group, including people in epistemology, philosophy of science, decision theory, cognitive and clinical psychology, and ethics and public policy.

Epistemology and the Psychology of Human Judgment

Humans are hobbled by costly and stubborn biases of reason and emotion. We severely underestimate our health risks and discount the future value of resources. These biases of reason and emotion are not exotic; they afflict normal people under normal stresses. I will argue that nonpaternalistic, institutional assistance can improve judgment, and that these improvements can both promote our autonomy and avoid costly outcomes when the biases go unchecked. In these cases, much of the crucial information is theoretically arcane. Had people known that their reasoning was so unreliable, they would have chosen strategies to counteract such sorry decision-making performance.

Paternalism and Cognitive Bias

Philosophers agree that scientific explanations aim to produce understanding, and that good ones succeed in this aim. But few seriously consider what understanding is, or what the cues are when we have it. If it is a psychological state or process, describing its specific nature is the job of psychological theorizing. This article examines the role of understanding in scientific explanation. It warns that the seductive, phenomenological sense of understanding is often, but mistakenly, viewed as a cue of genuine understanding. The article closes with a discussion of several new paths of research that tie the psychology of scientific explanation to cognate notions of learning, testimony, and understanding.

The Psychology of Scientific Explanation

Chapter 1: Epistemology: A First Look 1: Why Study Knowledge? 2: Some Doubts about Knowledge 3: Traditional Definition of Knowledge 4: Knowledge and Experience 5: Intuitions and Theory Chapter 2: Explaining Knowledge 1: The Scope of Epistemology 2: The Concept of Knowledge 3: Epistemology, Naturalism, and Pragmatism 4: Value in Epistemology Chapter 3: Belief 1: Belief and Representational States 2: Belief and Belief-Ascription 3: Are Beliefs Transparent? 4: Belief and Theoretical Ideals 5: Eliminativism and Prediction Chapter 4: Truth 1: Relativism 2: Truth and Correspondence 3: Truth and Coherence 4: Truth and Pragmatic Value 5: Kinds and Notions of Truth Chapter 5: Jusitification and Beyond 1: Justifications, Truth and Defeat 2: Inferential Justification and the Regress Problem 3: Supplementing Justification: The Gettier Problem Chapter 6: Sources of Knowledge 1: Rationalism, Empiricism, and Innatissm 2: Empiricism, Positivism, and Underdetermination 3: Intuitions and First-Person Reports 4: Memory 5: Theoretical Unification 6: Testimony and Social Dependence Chapter 7: Rationality 1: Preliminary Distinctions 2: Rational Inference: Normative and Descriptive 3: Consistency and Wayward Beliefs 4: Rationality and Decision Under Uncertainty 5: Integrative Considerations about Rationality Chapter 8: Skepticism 1: Some Species of Skepticism 2: Some Skeptical Arguments 3: A Reply from Common Sense 4: Skepticism, Naturalism, and Broad Explanation Chapter 9: Epistemology and Explanation 1: Origins of Contemporary Epistemology 2: Ultimate Epistemological Authority 3: Explanation and Knowledge References For Further Reading Glossary Index

J. D. Trout

Papers

Scientific Explanation And The Sense Of Understanding

Epistemology and the Psychology of Human Judgment

Paternalism and Cognitive Bias

The Psychology of Scientific Explanation

The Theory of Knowledge: A Thematic Introduction