DOCSLIB.ORG

This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: • This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. • A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. • This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. • The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. • When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Concept Learning Challenged Richard St¨ockle-Schobel PhD (Philosophy) The University of Edinburgh 2013 Abstract In my thesis, I argue that the philosophical and psychological study of concept-learning mechanisms has failed to take the diversity of learning mechanisms into account, and that consequently researchers should embrace a new way of thinking about concept learning: `concept learning' as a class of psychological mechanisms is not a natural kind lending itself to unified study and should be eliminated. To arrive at this, I discuss several concept-learning models that attempt to overcome Jerry Fodor's challenge and base my judgment on the plurality of feasible concept-learning mechanisms and on criteria for theoretical notions from the philosophy of science. Chapter 1 serves as an introduction to the topic `concept learning' and highlights its importance as a research topic in the study of the mind. I argue that a mechanistic understanding of the shape of concept learning is best suited to explain the phenomena, in line with the recent resurgence of mechanism-based explanation in the philosophy of mind. As the main challenge to the idea that concepts can be learnt, I proceed to set up Fodor's challenge for concept learning in Chapter 2. This challenge is the idea that concepts cannot be learnt given the logically possible mechanisms of concept learning. I lay out the argumentative structure and background assumptions that support Fodor's argument, and propose to scrutinise his empirically based premise most closely in my thesis: this empirically based premise is that the only possible mechanism of concept learning is the process of forming and testing hypotheses. As replies to Fodor's challenge, I discuss Perceptual Learning (R. Goldstone), Per- ceptual Meaning Analysis (J. Mandler), Quinean Bootstrapping (S. Carey), pattern- governed learning (W. Sellars), joint-attentional learning (M. Tomasello), and the Syndrome-Based Sustaining Mechanism Model (E. Margolis and S. Laurence). I argue that almost every mechanism I discuss has some leverage against Fodors argument, suggesting that there may be a wide variety of non-hypothesis-based concept-learning mechanisms. The final chapter of my thesis, Chapter 7, takes a step back and reviews the fate of the notion of concept learning in light of the diverse set of learning mechanisms brought up in my thesis. My first and main worry is that it is questionable whether the previously discussed mechanisms of concept learning share many scientifically relevant properties that would justify seeing them as instances of the natural kind `concept learning mechanism'. I argue that the substantiation of this worry would necessitate the elimination of `concept learning' and `concept-learning mechanism' as terms of the cognitive sciences. The chapter lays out the argumentative structure on which Concept Learning Eliminativism (CLE) rests, along with a discussion of questions about natural kinds and pragmatics in theory construction. This is inspired by Edouard Machery's argument for the elimination of `concept', but independent of Machery's own project. With this in place, I go on to give a conclusive argument that supports CLE, based on the claims that `concept learning' is not a natural kind and that there are pragmatic advantages to eliminating `concept learning'. In this final chapter, I also raise pragmatic considerations that support the argument for CLE, and propose new research directions that could profit from the eliminativist position. Contents Declaration v Note on publications vi Acknowledgements vii 1 Introduction 1 1.1 The tale of the aardvark . .2 1.2 Challenging concept learning . .2 1.3 Mapping the territory . .3 1.3.1 What is a concept? . .3 1.3.2 Desiderata for a theory of concepts . .4 1.3.3 What is concept learning? . .6 1.3.4 Why should we look for mechanisms? . .7 1.4 The structure of the thesis . .7 2 Fodor's Challenge for theories of concept learning 10 2.1 Introduction . 11 2.2 Fodor's paradox . 11 2.3 Innateness in Empiricist and Rationalist theories . 13 2.4 A more detailed look at the steps of Fodor's argument for the paradox . 15 2.4.1 (4) The Hypothesis-Formation-and-Confirmation Model . 15 2.4.2 (7)+(8) Spelling out the paradox . 16 2.4.3 (3) The representational theory of mind (RTM) . 16 2.4.4 (9) The anti-learning conclusion . 19 2.5 The doorknob/DOORKNOB problem . 21 2.5.1 The metaphysical solution . 22 2.5.2 The neurological solution . 23 2.5.3 The upshot of Fodor's position . 26 2.6 Fodor's Challenge . 27 i 3 Developmental accounts of concept learning 29 3.1 The challenge for developmental psychology . 30 3.2 The `Evidence for Prelinguistic Concept Learning' hypothesis . 30 3.2.1 The dominant research paradigms in early infancy . 31 3.2.2 The argument against EPCL . 34 3.3 Jean Mandler . 40 3.3.1 Perceptual Meaning Analysis . 42 3.3.2 Empirical evidence for PMA . 43 3.3.3 Problems for PMA . 44 3.4 Susan Carey . 48 3.4.1 Foundations of Quinian Bootstrapping . 49 3.4.2 Analysis of Quinian Bootstrapping . 51 3.4.3 Evidence for Quinian Bootstrapping . 54 3.4.4 Problems for Quinian Bootstrapping . 54 3.5 Summary . 57 4 Perceptual Learning as a concept-learning mechanism 58 4.1 Perceptual Learning and feature-based approaches to concepts . 59 4.2 Fixed-feature approaches and Fodor's paradox . 59 4.3 Perceptual Learning as a reply to Fodor's Challenge . 61 4.3.1 Unitisation . 64 4.3.2 Differentiation . 65 4.4 Concept learning or conceptual change? . 67 4.4.1 Do unitisation and differentiation operate on concepts? . 67 4.4.2 Intermission: Kinds of learning . 68 4.4.3 Features as concepts { a related issue . 70 4.4.4 Landy and Goldstone's reply: the Pask device . 71 4.4.5 Intermediate conclusion . 72 4.5 The notions `feature' and `stimulus' . 72 4.5.1 The Compatibility criterion . 72 4.5.2 The proximity of stimuli . 73 4.5.3 Feature detectors and maturation . 75 4.5.4 Physical information . 75 4.6 Perspectives for an amendment of the Perceptual Learning approach . 76 4.6.1 Three kinds of percept-concept interactions in Perceptual Learning 76 4.6.2 Blurring a boundary: brute-causal and rational acquisition . 78 4.7 Conclusion . 81 5 Sellarsian concept learning 83 5.1 Introduction . 84 5.2 The Augustinian Conception . 84 ii 5.3 Sellars's argument against AC { a reconstruction . 87 5.3.1 The Awareness challenge . 88 5.3.2 The Holist challenge . 89 5.3.3 The Relational challenge . 90 5.4 Sellarsian concept learning . 92 5.4.1 Logical spaces . 92 5.4.2 Learning without `knowing the rules' . 95 5.4.3 The advantages and the problem of Sellars-type learning . 97 5.5 Gauker's view: imagistic cognition before conceptual thought . 99 5.5.1 Categorisation tasks in early infancy . 100 5.5.2 Are similarity judgments conceptual? . 101 5.6 Developmental evidence for prelinguistic concepts . 103 5.6.1 Person, agent, gaze-following . 103 5.6.2 Social referencing . 104 5.6.3 Pointing . 105 5.6.4 A cut-off point for speaking of concepts? . 106 5.7 A second kind of social concept learning mechanism: Tomasello's Social- Pragmatic Theory of Word Learning . 107 5.7.1 The Social-Pragmatic Theory of Word Learning . 108 5.7.2 Examining the fit of SPT and Sellars's model . 109 5.7.3 Standing up against Fodor's Challenge . 109 5.8 Conclusion . 110 6 Learning concepts with sustaining mechanisms 111 6.1 Introduction . 112 6.2 Margolis and Laurence's response to Fodor . 113 6.2.1 The simple Fodor puzzle . 113 6.2.2 Arguments for complex concept learning . 113 6.2.3 The argument for primitive concept learning . 114 6.2.4 The syndrome-based sustaining mechanism model . 117 6.2.5 Things to like about the sustaining mechanism model . 121 6.2.6 Problems for the SBSM . 123 6.3 Criteria for concept learning . 126 6.4 Between brute-causal and rational acquisition . 130 6.4.1 The HF model and SBSM learning . 131 6.4.2 The Pask device's learning . 131 6.4.3 Could triggering qualify as learning by this measure? . 134 6.4.4 Lessons from applying the learning criteria . 135 6.5 Conclusion: Defending the rationality of concept acquisition . 136 iii 7 Concept learning eliminated 138 7.1 The state of play . 139 7.2 The claim . 139 7.3 The Horizontal Argument for Scientific Eliminativism . 140 7.3.1 Types of eliminativist arguments . 141 7.3.2 Natural kinds . 144 7.3.3 Pragmatic grounds . 146 7.4 The argument for Concept Learning Eliminativism . 147 7.4.1 Against CL as a natural kind .
Load more

Recommended publications
  • Pre´Cis of the Origin of Concepts
    BEHAVIORAL AND BRAIN SCIENCES (2011) 34, 113–167 doi:10.1017/S0140525X10000919 Pre´cis of The Origin of Concepts Susan Carey Department of Psychology, Harvard University, Cambridge, MA 02138 [email protected] Abstract: A theory of conceptual development must specify the innate representational primitives, must characterize the ways in which the initial state differs from the adult state, and must characterize the processes through which one is transformed into the other. The Origin of Concepts (henceforth TOOC) defends three theses. With respect to the initial state, the innate stock of primitives is not limited to sensory, perceptual, or sensorimotor representations; rather, there are also innate conceptual representations. With respect to developmental change, conceptual development consists of episodes of qualitative change, resulting in systems of representation that are more powerful than, and sometimes incommensurable with, those from which they are built. With respect to a learning mechanism that achieves conceptual discontinuity, I offer Quinian bootstrapping. TOOC concludes with a discussion of how an understanding of conceptual development constrains a theory of concepts. Keywords: bootstrapping; concept; core cognition; developmental discontinuity; innate primitive 1. Introduction determines the content of any given mental symbol (i.e., what determines which concept it is, what determines The human conceptual repertoire is a unique phenom- the symbol’s meaning). (In the context of theories of enon, posing a formidable challenge to the disciplines of mental or linguistic symbols, I take “content” to be the cognitive sciences. How are we to account for the roughly synonymous with “meaning.”) The theory must human capacity to create concepts such as electron, also specify how it is that concepts may function in cancer, infinity, galaxy, and wisdom? thought, by virtue of what they combine to form prop- A theory of conceptual development must have three ositions and beliefs, and how they function in inferential components.
    [Show full text]
  • Curriculum Vitae: Lucas P
    LUCAS P. BUTLER Department of Human Development & Quantitative Methodology 3942 Campus Drive University of Maryland College Park, MD 20742 Email: [email protected] Phone: 202-262-2962 Lab Website: cogdevlab.umd.edu Personal Website: lucaspbutler.com EDUCATION 2012 Ph.D. in Psychology Stanford University, Stanford, CA Committee: Ellen M. Markman (chair), Herbert H. Clark, Michael C. Frank, Daniel L. Schwartz 2009 M.A. in Psychology Stanford University, Stanford, CA 2005 A.B. cum laude in Psychology Harvard University, Cambridge, MA Thesis Advisor: Susan Carey PROFESSIONAL APPOINTMENTS 2015-present Assistant Professor Department of Human Development & Quantitative Methodology University of Maryland, College Park Faculty Affiliate Neuroscience and Cognitive Science Program University of Maryland, College Park Faculty Affiliate Language Science Center University of Maryland, College Park 2012-2014 Alexander von Humboldt Postdoctoral Research Fellow Department of Developmental and Comparative Psychology Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Advisor: Michael Tomasello AWARDS & HONORS 2018 Excellence in Scholarship Award, University of Maryland College of Education 2017 Research and Scholarship Award, University of Maryland 2016 Rising Star Award, Association for Psychological Science 2015-2016 SPARC Assistant Professor Award, University of Maryland Curriculum Vitae: Lucas P. Butler 2 2012-2014 Postdoctoral Research Fellowship, Alexander von Humboldt Foundation 2011 Student Travel Award, Society for Research in Child Development 2011 Norman H. Anderson Research Fund, Stanford University 2011 Dissertation Research Award, American Psychological Association 2009-2012 Graduate Research Fellowship, National Science Foundation 2007-2008 Sidney Siegel Fellowship, Stanford University 2005 Harvard Psychology Faculty Prize for Distinguished Honors Thesis 2005 Jerome Kagan Undergraduate Research Award, Harvard University PUBLICATIONS *Student author †Authors contributed equally Books Butler, L.
    [Show full text]
  • Curriculum Vitæ
    TANIA LOMBROZO Curriculum Vitae December 2018 CONTACT INFORMATION Department of Psychology Princeton University Peretsman Scully Hall Princeton, NJ 08540 E-mail: [email protected] EDUCATION 2006 - Ph.D. in Psychology, Harvard University, Cambridge, MA. Thesis: Understanding explanation: Studies in teleology, simplicity, and causal knowledge. (Advisor: Susan Carey) 2004 - A.M. in Psychology, Harvard University, Cambridge, MA. 2002 - B.S. in Symbolic Systems (with distinction), Stanford University, Stanford, CA. B.A. in Philosophy (with distinction), Stanford University, Stanford, CA. Thesis: Optimality and teleology in adaptationist explanations of cognition. (Advisor: Peter Godfrey-Smith) PROFESSIONAL POSITIONS 2018 – Professor, Department of Psychology, Princeton University Associated Faculty, Department of Philosophy, Princeton University Faculty Associate, University Center for Human Values 2017 – 2018 Professor (Class of 1944 Chair), Department of Psychology, UC Berkeley Affiliate Faculty Member, Department of Philosophy, UC Berkeley 2013 – 2017 Associate Professor, Department of Psychology, UC Berkeley Affiliate Faculty Member, Department of Philosophy, UC Berkeley 2006 – 2013 Assistant Professor, Department of Psychology, UC Berkeley Affiliate Faculty Member, Department of Philosophy, UC Berkeley HONORS & AWARDS American Council of Learned Societies (ACLS) Fellow, Fall 2017 American Psychological Association (APA) Distinguished Scientific Award for Early Career contributions to psychology, awarded August, 2016 Roger N. Shepard
    [Show full text]
  • Feiman, R.,Mody, S., Sanborn, S., Carey, S
    Language Learning and Development ISSN: 1547-5441 (Print) 1547-3341 (Online) Journal homepage: https://www.tandfonline.com/loi/hlld20 What Do You Mean, No? Toddlers’ Comprehension of Logical “No” and “Not” Roman Feiman, Shilpa Mody, Sophia Sanborn & Susan Carey To cite this article: Roman Feiman, Shilpa Mody, Sophia Sanborn & Susan Carey (2017) What Do You Mean, No? Toddlers’ Comprehension of Logical “No” and “Not”, Language Learning and Development, 13:4, 430-450, DOI: 10.1080/15475441.2017.1317253 To link to this article: https://doi.org/10.1080/15475441.2017.1317253 Published online: 28 Jun 2017. Submit your article to this journal Article views: 225 View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=hlld20 LANGUAGE LEARNING AND DEVELOPMENT 2017, VOL. 13, NO. 4, 430–450 https://doi.org/10.1080/15475441.2017.1317253 What Do You Mean, No? Toddlers’ Comprehension of Logical “No” and “Not” Roman Feiman a, Shilpa Mody b, Sophia Sanbornc, and Susan Careyb aDepartment of Psychology, University of California, San Diego; bDepartment of Psychology, Harvard University; cDepartment of Psychology, University of California ABSTRACT For adults, “no” and “not” change the truth-value of sentences they com- pose with. To investigate children’s emerging understanding of these words, an experimenter hid a ball in a bucket or a truck, then gave an affirmative or negative clue (Experiment 1: “It’s not in the bucket”; Experiment 2: “Is it in the bucket?”; “No, it’s not”). Replicating Austin, Theakston, Lieven, & Tomasello (2014), children only understood logical “no” and “not” after age two, long after they say “no” but around the time they say “not” and use both words to deny statements.
    [Show full text]
  • Numerical Transformations in Five-Month-Old Human Infants
    MATHEMATICAL COGNITION, 1997, 3 (2), 89–104 89 Numerical Transformations in Five-month-old Human Infants Etienne Koechlin EHESS–CNRS & INSERM-CREARE, Paris, France Stanislas Dehaene EHESS–CNRS & INSERM, Paris, France Jacques Mehler EHESS–CNRS, Paris, France We explore numerical abilities in five-month-old infants. Wynn (1992) showed that, when an object is added to or removed from a set of one or two hidden objects, five- month-old infants can infer the result of this transformation. However, in Wynn’s experiment, the objects were always placed at the same two locations. Hence, it remained an open question whether infants developed numerical or location-based expectations. To address this question, 56 five-month-old infants were tested using a violation of expectation paradigm with possible (1+1=2 or 2–1=1) and impossible (1+1=1 or 2–1=2) events. One group was tested in conditions identical to Wynn (1992). The other group saw objects being placed on a rotating tray. Since the locations of objects were not predictable, the development of non-numerical, location-based expectations was prevented. Infants in both groups looked longer at numerically impossible events than at the related possible events. These results suggest that infants use a more abstract representation than object location, the numerical nature of which is discussed. Recent experiments suggest that very young infants, and even newborns, are able to discriminate small sets of items according to their numerosity. Ten- and four- month-old infants have been shown to habituate to a set of two or three dots and to dishabituate when the numerosity changes from two to three or vice versa (Starkey & Cooper, 1980; Strauss & Curtis, 1981).
    [Show full text]
  • Precis Of: How Children Learn the Meanings of Words, by Paul Bloom
    How children learn the meanings of words Page 1 of 18 Below is the unedited draft of: The precis of: How Children Learn the Meanings of Words, By Paul Bloom. (2000) Behavioral and Brain Sciences 24 (X): XXX-XXX. This is the unedited précis of a book that is being accorded BBS multiple book review (Copyright 2000: Cambridge University Press U.K./U.S..) The précis is for inspection only, to help prospective book reviewers decide whether or not they wish to prepare a formal review. The review is of the book, not the précis. Please do not prepare a review unless you have received a hard copy of the invitation, instructions and deadline information. (Please also let us know whether you already have a copy of the book or would require one.) For information on becoming a commentator on this or other BBS target articles, write to: [email protected] For information about subscribing or purchasing offprints of the published version, with commentaries and author's response, write to: [email protected] (North America) or [email protected] (All other countries). Precis of: HOW CHILDREN LEARN THE MEANINGS OF WORDS Cambridge, MA: MIT Press 2000 (312 pp.) Paul Bloom Department of Psychology Yale University P.O. Box 208205 New Haven, CT 06520 [email protected] http://pantheon.yale.edu/~pb85 Paul Bloom is Professor of Psychology at Yale University. He is the author of over 50 scientific publications in psychology, linguistics, and cognitive science, and has written or edited four books. He serves as Associate Editor for Developmental Psychology and Language and Cognitive Processes.
    [Show full text]
  • Behavioral and Social Sciences in Stem (Science, Technology, Engineering and Mathematics) Education
    BEHAVIORAL AND SOCIAL SCIENCES IN STEM (SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS) EDUCATION A WORKSHOP SUMMARY July 13, 2010 5635 Fishers Lane Rockville, Maryland Sponsored by the Office of Behavioral and Social Sciences Research (OBSSR), National Institutes of Health (NIH) OBSSR Workshop: Behavioral and Social Sciences in STEM Education July 13, 2010 CONTENTS Welcome, Introduction, and Plans for the Day Deborah H. Olster, PhD, Acting Director, Office of Behavioral and Social Sciences Research (OBSSR), National Institutes of Health (NIH) ............................................................................ 4 Framing the Issue Elisa Klein, PhD, Society for Research in Child Development/American Association for the Advancement of Science (AAAS) Science and Technology Policy Fellow, OBSSR, NIH ........... 5 Federal Perspectives on the Social and Behavioral Sciences and STEM Education and STEM Education Reform ........................................................................................................................... 6 Steven J. Robinson, PhD, White House Domestic Policy Council ............................................. 6 Michael Lach, Special Assistant for STEM Education, US Department of Education............... 6 Discussion ................................................................................................................................... 7 What Are the Core STEM Sciences? A Discussion led by Paula R. Skedsvold, JD, PhD, Executive Director, Federation of Associations in Behavioral and Brain
    [Show full text]
  • COMMENTARIES Evidence for Numerical Abilities in Young Infants
    202 Commentaries COMMENTARIESBlackwell Publishers Ltd EvidenceCommentaries for numerical abilities in young infants: a fatal flaw? Susan Carey Harvard University, USA Wynn’s (1992) well-known and oft-replicated1 Nature tion when attributing sophisticated abilities, such as the paper established that 4-month-old infants look longer capacity to add and subtract, to very young infants. at impossible than at possible outcomes in ‘addition’ and Although Cohen and Marks’ remarks are aimed at ‘subtraction’ events. In a 1 + 1 addition event, for Wynn’s proposals, their data bear equally on the other instance, the infant was shown a single object on a stage, major interpretation of the infant addition/subtraction which was then covered by a screen. A second object was data, called the ‘object file’ model by Uller, Huntley- then introduced behind the screen and the screen then Fenner, Carey and Klatt (1999). According to the object was lowered, revealing either the possible outcome of 2 file model, infants establish a representation of each objects or an impossible outcome of 1 object. In this individual object behind the screen, updating these rep- 1 + 1 = 2 or 1 event, infants looked longer at the imposs- resentations as objects are added to or subtracted from ible outcome of 1 object. Similarly, infants look longer the set (see also Scholl & Leslie, 1999; Simon, 1997). The at the impossible outcome in a 2 − 1 = 2 or 1 subtraction representation of the updated hidden set is compared to event or a 1 + 1 = 2 or 3 addition event. the set of objects revealed when the screen is removed on Wynn took these findings to show that very young the basis of 1–1 correspondence, a computation that infants symbolically represent the number of hidden established numerical equivalence (but see Feigenson, objects behind screens, and can operate on symbolic rep- Carey & Spelke, in press, and Feigenson, Carey & resentations of number by adding or subtracting 1, Hauser, in press).
    [Show full text]
  • Knowledge Enrichment and Conceptual Change in Folkbiology: Evidence from Williams Syndrome
    COGNITIVE PSYCHOLOGY 37, 156±200 (1998) ARTICLE NO. CG980695 Knowledge Enrichment and Conceptual Change in Folkbiology: Evidence from Williams Syndrome Susan C. Johnson University of Pittsburgh and Susan Carey New York University Ten participants with Williams syndrome (WS) (average verbal mental age of 11;5) were compared to two groups of normally developing children (average men- tal ages 10;11 and 6;7 years) with respect to intuitive biological knowledge about people, animals, and plants. Participants in the older control group were individually matched to the participants with WS on verbal mental age. The probes for biological understanding were drawn from the existing literature on the development of folkbi- ology and were divided into two batteries based on the hypothesized distinction of (1) general knowledge consistent with the conceptual repertoire of normally devel- oping preschool children (the T1/T2-Neutral Animal Knowledge battery) and (2) folkbiological concepts normally acquired between ages 6 and 12 which require conceptual change for their construction (life, death, people-as-one-animal-among- many, species kind as determined by origin of the animal; the T2-Dependent bat- tery). The two task batteries were equated for task demands, differing only in the content of the concepts probed. It was hypothesized that if this distinction is a false one, and the construction of folkbiology is accomplished entirely by enrichment of the preschooler's knowledge, there should never be a population with differential performance on these two batteries. People with WS were nonetheless found to be differentially impaired on the T2-Dependent battery. They performed at the level of the older control group on the T1/T2-Neutral battery, but at the level of the 6- year-olds on the T2-Dependent battery.
    [Show full text]
  • The Psychological Representation of Modality
    The psychological representation of modality Jonathan Phillips and Joshua Knobe Abstract A series of recent studies have explored the impact of people’s judgments regarding physical law, morality, and probability. Surprisingly, such studies indicate that these three apparently unrelated types of judgments often have precisely the same impact. We argue that these findings provide evidence for a more general hypothesis about the kind of cognition people use to think about possibilities. Specif- ically, we suggest that this aspect of people’s cognition is best understood using an idea developed within work in the formal semantics tradition, namely the notion of modality. On the view we propose, people may have separate representations for physical, moral and probabilistic considerations, but they also integrate these various considerations into a unified representation of modality. The past few decades have witnessed an explosion of research on the way that humans understand physics, morality, and probability. This research has explored the impact on people’s cognition of regarding an event as physically impossible, morally wrong, or highly improbable (Griffiths et al. 2010, Knobe 2010, Marr 1982, Spelke 1990). One remarkably underappreciated fact is that these factors often have the exact same impact. That is, there are a number of different respects in which a judgment that something violates physical law has the same impact as a judgment that it is morally wrong, which in turn has the same impact as a judgment that it is statistically improbable. As we go on to argue, the number of different phenomena in which this precise pattern can be found suggests that there is some deeper connection between these factors.
    [Show full text]
  • Rebecca Saxe John W Jarve (1978) Professor of Cognitive Neuroscience Academic Positions 2018
    Rebecca Saxe John W Jarve (1978) Professor of Cognitive Neuroscience Academic Positions 2018 - present MIT, Brain and Cognitive Sciences Associate Department Head 2015 - present MIT, Brain and Cognitive Sciences Professor 2012 - present McGovern Institute for Brain Research Associate Member 2011 - 2015 MIT, Brain and Cognitive Sciences Associate Professor 2006 - 2011 MIT, Brain and Cognitive Sciences Assistant Professor 2003 - 2006 Harvard University, Society of Fellows Junior Fellow Degrees and Education 2003 MIT, Cambridge MA PhD in Cognitive Science, Outstanding Thesis Award 2000 Oriel College, Oxford University, Oxford, UK BA in Psychology and Philosophy, Congratulatory First Awards (selected) 2020 Guggenheim Fellow 2018 MIT Committed to Caring Award 2018 Fellow, American Psychological Association 2017 BCS Awards for Excellence: in Graduate Mentoring; and in Undergraduate Teaching 2015 Arthur C Smith Award for dedication to student life and learning, MIT 2014 Troland Award, National Academy of Sciences 2012 Chosen as a World Economic Forum Young Global Leader 2011 Doc Edgerton Junior Faculty Achievement award, MIT 2010 School of Science Prize for Undergraduate Teaching, MIT 2009 American Psychological Association Robert L. Fantz Award for Young Psychologists 2008 Cognitive Neuroscience Society Young Investigator Award 2008 Popular Science “Brilliant 10” scientists under 40. Impact http://en.wikipedia.org/wiki/Rebecca_Saxe 2009 TED talk (> 3 million views, translated into 32 languages) Citations (Google Scholar): >28,000; h-index: 75, i10-index: 131; Funding: >$8 million 1 Publications — Journal Articles 1. Jamali, M., Grannan, B. L., Fedorenko, E., Saxe, R., Báez-Mendoza, R., & Williams, Z. M. (2021). Single-neuronal predictions of others’ beliefs in humans. Nature, 1-5. 2. Tomova, L., Wang, K.
    [Show full text]
  • SOPHIE R. WHARTON [email protected] 6 Washington Place (914) 772-1048 New York, NY 10003
    SOPHIE R. WHARTON [email protected] 6 Washington Place (914) 772-1048 New York, NY 10003 EDUCATION HARVARD UNIVERSITY 2007 - 2011 • A.B. degree with highest honors in Psychology, Magna cum laude, 2011 • Completed the Cognitive Science Track of the Mind, Brain & Behavior (MBB) honors program • Honors Thesis: “Thou Shalt Versus Thou Shalt Not: The Neural Processes Underlying Decisions to Help Versus Decisions to Avoid Doing Harm,” Advisors: Joshua Greene and Fiery Cushman AWARDS & HONORS • Phi Beta Kappa, Alpha Iota Chapter, Harvard College 2011 • Thomas T. Hoopes Prize for outstanding senior thesis 2011 • Gordon W. Allport Prize, departmental award for thesis of exceptionally high quality in personality psychology, 2011 the study of prejudice, social psychology, rumor, or social ethics • John Harvard Scholarship, awarded to top 5% of class 2009-2010 • Mary Gordon Roberts Summer Mind, Brain, Behavior Award, grant for thesis research 2010 • Harvard College Research Program (HCRP) grant for work done in Greene Lab 2008 RESEARCH EXPERIENCE SUSAN CAREY, DEVELOPMENTAL PSYCHOLOGY LAB, HARVARD UNIVERSITY 2007 Research Assistant under Fiery Cushman • Studied the role of intentions and consequences in children’s judgments of wrongness and punishment • Worked at the Discovery Center in the Boston Museum of Science testing moral judgments of young children • Created computer animations for a separate social evaluation study testing toddlers’ abilities to attribute morality and mental states to animate vs. inanimate objects JOSHUA GREENE, MORAL COGNITION LAB, HARVARD UNIVERSITY 2008-2011 Research Assistant under Fiery Cushman • Spring 2008: Designed and conducted study of adult moral judgment, assessing the effect of “transfer of intent” on subjects’ beliefs about suitable punishments for various crimes.
    [Show full text]