DOCSLIB.ORG

Beyond Clever Hans: Learning from People Without Their Really Trying

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Beyond Clever Hans: Learning from People Without Their Really Trying Beyond Clever Hans: Learning From People Without Their Really Trying by Vivek Veeriah A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science University of Alberta c Vivek Veeriah, 2017 Abstract Facial expressions and other body language are important for human commu- nication. They complement speech and make the process of communication simple and sustainable. However, the process of communication using existing approaches to human-machine interaction is not intuitive as that of human communication. Specifically, the existing approaches to human machine inter- action do not learn from whatever subtle non-verbal cues produced by a user. Many of the existing approaches map body language cues to instructions or re- wards and use them to train a machine. These mappings are not learned from ongoing interactions and are assumed to be defined by some external source. As a consequence, the communicative process through these approaches can cause significant cognitive load on the user. This is an important problem that needs to be addressed if we are to amplify our existing cognitive and physical capabilities through intelligent machines. Towards addressing this, we intro- duce our idea that allows machines to learn from whatever subtle cues people produce during the process of interaction. Particularly, the agent learns a value function that maps the user's non-verbal cues to later rewards. By using this value function, the user can teach their agent to complete a task according to their preferences, and consequently maximize their satisfaction. We demon- strate this by training an agent using facial expressions. Furthermore, we show that these learned value functions can be successfully transferred across tasks. In conclusion, our approach is the first that allows people to teach their machines using whatever subtle cues they produce and could take us far in achieving sustainable forms of human-machine interaction. ii To Mom and Dad iii Acknowledgements I would like to thank my supervisors, Richard S. Sutton and Patrick M. Pi- larski, for being amazing mentors and for introducing me to this exciting field of reinforcement learning. In particular, I am thankful to Rich for showing me how to critically develop my own thoughts, and more importantly, for in- spiring me to pursue ambitious and important ideas. I would like to thank Patrick for his never-ending enthusiasm towards research and for his consistent encouragement. His excitement has always been contagious! Further, I would like to thank Pooria Joulani, Craig Sherstan, Rupam Mahmood, Roshan Shariff, Marlos Machado, Kenny Young, Tian Tian, Eric Graves, Gautham Vasan and Sanket Kumar Singh for all their help along the way. My experiences at University of Alberta and at the RLAI Lab wouldn't have been a joyful one without them. iv Table of Contents 1 Beyond Clever Hans 1 1.1 Horse that Answered Questions . 1 1.2 Human-Machine Interaction using Body Language . 2 1.3 Moving Beyond Clever Hans . 3 1.4 Outline . 5 1.5 Contributions . 5 2 Background 7 2.1 The Reinforcement Learning Problem . 7 2.2 Value Functions . 9 2.3 Temporal-Difference (TD) Learning . 11 2.3.1 Prediction Algorithm: TD(λ) Learning . 13 2.3.2 Control Algorithm: Sarsa(λ) algorithm . 14 2.4 Actor-Critic Methods . 15 2.5 Linear Function Approximation Using Tile Coding . 17 3 Existing Approaches to Human-Machine Interaction 19 3.1 Interactions in the Form of Rewards . 19 3.2 Interactions in the Form of Demonstrations and Instructions . 22 3.3 Relevance of these Related Works to this Thesis . 23 4 Learning From Prospective Body Language 24 4.1 Towards Natural Forms of Human-Machine Interaction . 24 4.2 Our Approach: Learning from Prospective Body Language Cues 26 4.3 Existing Approaches that Use Body Language . 28 4.3.1 Body Language as Instructions to Machines . 28 4.3.2 Body Language as Rewards to Machines . 30 4.4 Comparison to Existing Human-Machine Interaction Approaches 31 5 Using Sarsa To Learn From Prospective Body Language 33 5.1 Implementation using Sarsa . 34 5.1.1 State Representation: Processing Raw Images into Fa- cial Expression Features . 35 5.1.2 Sarsa Learning Algorithm . 36 5.2 Grip-Selection Task . 38 v 5.2.1 State Space . 39 5.2.2 Action Space . 39 5.2.3 User-Generated Rewards . 40 5.3 Experiments and Results . 40 5.3.1 Experiment 1: Multiple Grip and Object Setting . 41 5.3.2 Experiment 2: Infinite Object Setting . 43 5.4 Discussion . 45 6 Using Actor-Critic To Learn From Prospective Body Lan- guage 49 6.1 Implementation using an Actor-Critic Method . 50 6.2 Experiments and Results . 52 6.3 Discussion . 54 7 Discussions and Extensions: From Facial Expressions to Body Language 55 7.1 What is Missing in Existing Human- Machine Interaction Approaches? . 55 7.2 How Does Our Approach Differ from Approaches that Learn from User-Generated Rewards? . 57 7.3 Do People Produce Non-Verbal Cues While Interacting With Their Machines? . 58 7.4 How Does Our Approach Relate to Affective Computing? . 60 7.5 Better Feature Representations for Nuanced Body Language . 60 7.6 Integrating Different Modalities . 61 8 Conclusion 63 Bibliography 65 vi List of Figures 2.1 The Reinforcement Learning problem . 7 2.2 Actor-Critic Architecture . 16 2.3 Tile Coding . 18 3.1 Interactive Shaping . 20 4.1 Learning from Prospective Body Language . 26 5.1 State Representation for Face-Valuing Agent . 35 5.2 Grip-Selection Task . 38 5.3 Learning Performance of Sarsa Face-Valuing Agent . 42 5.4 Total User-Generated Rewards Provided to Sarsa Learning Agent 43 5.5 Total Time Steps Taken to Complete the Infinite Objects Set- ting by Sarsa Learning Agents. 44 6.1 Learning Performance of Actor-Critic Face-Valuing Agent . 52 vii Chapter 1 Beyond Clever Hans The overarching goal of this thesis is to introduce an approach to human- machine interaction, where the machine learns from whatever subtle non- verbal cues that are produced by the user during the process of interaction. We begin by introducing the story of Clever Hans, which is a popular example that illustrates the role of body language in communication. Subsequently, we broadly discuss how existing approaches use body language for human- machine interaction. Finally, we introduce our approach and highlight our contributions in this thesis. 1.1 Horse that Answered Questions Wilhelm von Osten was a math teacher and an amateur horse trainer. He owned a horse called Hans. In the year 1891, von Osten displayed his horse in public and claimed that he had taught his horse to perform simple arithmetic calculations, tell time, and keep track of the calendar. Hans answered many of the questions posed by the questioner, and over time, became a public sensation in the late twentieth century. As a result of this popularity, the horse was called \Clever Hans" and was reported by the New York Times in 1904. The significant public interest in Clever Hans motivated the German board of education to appoint a commission to investigate whether the horse pos- sessed intelligence. The commission recruited a German psychologist named Oskar Pfungst, who conducted a substantial number of trials with Hans and 1 discovered that Hans got the right answer only when the questioner knew what the answer was. More importantly, Hans needed to see the questioner while answering their questions. Pfungst then studied the behavior of the questioner while the horse was answering their question and reported his findings (c.f. Pfungst, 1911). He discovered that the horse's behavior was influenced by subtle and unintentional cues. Specifically, as Hans approached the correct answer the questioner's posture and facial expressions changed in ways that were consistent with an increase in tension, which was released when Hans made the final tap with its hoof. This change in body language served as a useful cue for the horse to stop tapping its hoof. Hans learned when to stop tapping its hoof by observing the subtle non- verbal cues of the questioner. This ability of the horse was wrongly attributed to be a consequence of possessing intelligence. While training his horse, von Osten rewarded the horse whenever it answered a question correctly which reinforced this process of answering from the questioner's subtle cues. 1.2 Human-Machine Interaction using Body Language The ideal approaches to human-machine interaction should allow users to com- municate naturally as they would do with their peers. This would make the process of interaction between people and machines to be sustainable and scalable. As an important step towards achieving this ambitious goal of ideal human-machine interaction, we need to design approaches that allow machines to learn from whatever non-verbal cues produced during the interaction, sim- ilar to how the horse picked on unintentional non-verbal cues of its questioner for answering their questions. In human communication, the subtle non-verbal cues that we produce un- intentionally influences the observer's behavior towards us. This is a natural process and is well-studied in psychology (De Gelder, 2006; Pezzulo et al., 2013; Jack & Schyns, 2015) and in affective neuroscience (Adolphs, 2002; Whalen et 2 al., 2013). Specifically, these body language cues have communicative value to their observers and allowing the machines to learn from our subtle non-verbal cues seems to be a natural direction to pursue in human-machine interaction. The existing approaches to human-machine interaction do not use body language as training information. Specifically, these approaches assume that the body language cues and their meaning are given to the machine from some external source, instead of learning them from ongoing interactions.
Load more

Recommended publications
  • Beyond Clever Hans: Learning from People Without Their Really Trying
    Beyond Clever Hans: Learning From People Without Their Really Trying by Vivek Veeriah Jeya Veeraiah A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science University of Alberta c Vivek Veeriah Jeya Veeraiah, 2017 Abstract Facial expressions and other body language are important for human commu- nication. They complement speech and make the process of communication simple and sustainable. However, the process of communication using existing approaches to human-machine interaction is not intuitive as that of human communication. Specifically, the existing approaches to human machine inter- action do not learn from whatever subtle non-verbal cues produced by a user. Many of the existing approaches map body language cues to instructions or re- wards and use them to train a machine. These mappings are not learned from ongoing interactions and are assumed to be defined by some external source. As a consequence, the communicative process through these approaches can cause significant cognitive load on the user. This is an important problem that needs to be addressed if we are to amplify our existing cognitive and physical capabilities through intelligent machines. Towards addressing this, we intro- duce our idea that allows machines to learn from whatever subtle cues people produce during the process of interaction. Particularly, the agent learns a value function that maps the user’s non-verbal cues to later rewards. By using this value function, the user can teach their agent to complete a task according to their preferences, and consequently maximize their satisfaction. We demon- strate this by training an agent using facial expressions.
    [Show full text]
  • Clever Hans, by Oskar Pfungst 1
    Clever Hans, by Oskar Pfungst 1 Clever Hans, by Oskar Pfungst The Project Gutenberg EBook of Clever Hans, by Oskar Pfungst This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org Title: Clever Hans (The horse of Mr. Von Osten): A contribution to experimental animal and human psychology Author: Oskar Pfungst Translator: Carl L. Rahn Release Date: October 11, 2010 [EBook #33936] Language: English Character set encoding: ISO-8859-1 *** START OF THIS PROJECT GUTENBERG EBOOK CLEVER HANS *** Clever Hans, by Oskar Pfungst 2 Produced by Audrey Longhurst, Linda Hamilton and the Online Distributed Proofreading Team at http://www.pgdp.net [Illustration: CLEVER HANS] CLEVER HANS (THE HORSE OF MR. VON OSTEN) A CONTRIBUTION TO EXPERIMENTAL ANIMAL AND HUMAN PSYCHOLOGY BY OSKAR PFUNGST WITH AN INTRODUCTION BY PROF. C. STUMPF, AND ONE ILLUSTRATION AND FIFTEEN FIGURES TRANSLATED FROM THE GERMAN BY CARL L. RAHN Fellow in Psychology in the University of Chicago WITH A PREFATORY NOTE BY JAMES R. ANGELL Professor of Psychology in the University of Chicago [Illustration] NEW YORK HENRY HOLT AND COMPANY 1911 COPYRIGHT, 1911, BY HENRY HOLT AND COMPANY PREFATORY NOTE [BY JAMES R. ANGELL] The University of Chicago It gives me great pleasure to accept the invitation of the publishers to write a word of introduction for Mr. Rahn's excellent translation of "Der Kluge Hans", a book which in the original has been but little known to American readers.
    [Show full text]
  • Doing Away with Morgan 'S Canon
    Doing Away with Morgan’ s Canon SIMON FITZPATRICK Abstract : Morgan ’ s Canon is a very widely endorsed methodological principle in animal psychology, believed to be vital for a rigorous, scientifi c approach to the study of animal cognition. In contrast I argue that Morgan ’ s Canon is unjustifi ed, pernicious and unnecessary. I identify two main versions of the Canon and show that they both suffer from very serious problems. I then suggest an alternative methodological principle that captures all of the genuine methodological benefi ts that Morgan ’ s Canon can bring but suffers from none of its problems. 1. Introduction Morgan ’ s Canon is a very widely endorsed methodological principle in animal psychology. Proposed at the end of the 19th century by the British comparative psychologist Conwy Lloyd Morgan, it states that: In no case may we interpret an action as the outcome of the exercise of a higher psychical faculty, if it can be interpreted as the outcome of the exercise of one which stands lower in the psychological scale (Morgan, 1894 , p. 53). This principle has played a central role in subsequent scientifi c and philosophical work into the nature of non-human animal minds. So central in fact that it has been described as ‘ possibly the most important single sentence in the history of the study of animal behaviour ’ ( Galef, 1996, p. 9; cited in Radick, 2000 , p. 3). Morgan ’ s Canon has attained this eminent status because it has been seen as a necessary component of a suitably rigorous approach to explaining animal behaviour. According to a recent textbook, ‘ Morgan ’ s Canon … has enabled us to approach the analysis of behaviour sensibly and to avoid the superfi cial anthropomorphism which led to many absurdities in the past ’ ( Manning and Dawkins, 1998 , p.
    [Show full text]
  • BROWN, BARNUM (1873–1963) Dinosaur Fossil Collector Amed After the Great Showman P
    BRoWn, BaRnUM (1873–1963) Dinosaur Fossil collector amedafterthegreatshowmanP.T.Barnum,indefatigabledinosaurdiggerBarnum Brownassembledhisownversionof“The Greatest Showon Earth”:aparadeofgiant n dinosaurfossilswrenchedfromthecliffsandarroyosoftheAmericanWest.Brown’s -lastingcontribution—hundredsoftonsofdinosaurfossils—formedthenucleusoftheAmeri .canMuseumofnaturalHistory’sworld-famouscollection during the 1960s, Brown, nearly 90, could still be seen leading visitors around the crammeddinosaurhalls,announcing,“Here’sanotheroneofmychildren,”ashepointed outthebonesofasauriangiant.Butwhenhebeganhiscareerin1897,themuseumhad .notasingledinosaur AsachildinCarbondale, Kansas,Browncollectedfossilsfromfreshlyplowedfields.He -attendedtheUniversityof Kansas,thenmovedto New YorkCity,wherehestudiedpaleontol ogyatColumbiaUniversityandbeganworkingatthemuseumwhilehewasstillagraduate student. Forhisfirstfieldassignment,themuseum’sdirector,HenryFairfield Osborn,sentBrown BONE hUNTER barnum brown, when almost ninety, toComoBluff,Wyoming,toprospectitsrichJurassicdeposits.Brownandhiscolleagues supervised construction of discoverednewbedscontainingenormousquantitiesoffossils,includingtheApatosaurus .sinclair oil’s lifesize dino- (thencalled Brontosaurus)thatstilldominatesoneofthemuseum’shugedinosaurhalls saur models, shown below However, yale paleontologist othniel C. Marsh was furious about his former sites being being barged down the hud- .workedandbeganabitterfeudwithosbornthatlastedtotheendofhislife son river to the 1964 new york World’s fair.
    [Show full text]
  • THE TRUE FAIRYTALE of CLEVER HANS of Course We Don’T Want to Tell Any Fairy Tales Here …
    THE TRUE FAIRYTALE OF CLEVE R HANS THE TRUE FAIRYTALE OF CLEVER HANS Of course we don’t want to tell any fairy tales here … … Even though the story of how a three-man business founded by Hans Grohe in 1901 in the little Black Forest town of Schiltach became a world-wide enterprise with over 3,200 employees might seem to some to be more like a fairy tale… What follows is the story of the firm of Hansgrohe, spanning more than a century of growth and development. And the story of Hans Grohe himself, the handyman, the craftsman, the visionary pioneer in the field of sanitation, without whom the company of Hansgrohe could never have come into being. And the story of his descendants who right up to the present day lead and guide the firm of Hansgrohe SE. It’s not a dry-as-dust story, and doesn’t claim to be complete in itself or to be a scientific account; it is told here sometimes tongue in cheek, with great joy in the telling, giving deep insights into the world of Hansgrohe and into the family firm, founded by Clever Hans. And if the story of “Lucky Hans” (“Hans im Glück”) as once told by the Grimm Brothers in their collection of fairy tales is unfamiliar to you, just read on. Truth is stranger than fiction, as they say … 2 Hans had served his master for seven years and now he had an urgent desire to go to Schiltach. So he asked his master to give him his wages.
    [Show full text]
  • “Clever Hans Effect” You Are Going to Listen to a Scientific Radio Show
    Kammerhofer/Zangerl Going for Finals B2, Audio-CD – Transcripts GOING FOR FINALS B2,-AUDIO-CD – TRANSCRIPTS 1 The “Clever Hans Effect” You are going to listen to a scientific radio show about an experiment with an animal. First you will have 45 seconds to study the task below, then you will hear the recording twice. While listening, complete the sentences (1–9) using a maximum of 4 words. Write your answers in the spaces provided. The first one (0) has been done for you. After the second listening, you will have 45 seconds to check your answers. Hello and good evening, dear listeners. Today I’m talking about the “Clever-Hans-Effect”. This term refers originally to a trained horse, named “Clued Hans”, referred to in the literature as “Clever Hans” … who was taught by his master, William Von Osten, to do math and to report on this math … So William would ask the horse, ask Hans, to add 3 + 2 for example and Hans would tap his hoof five times. This may have seemed as if the horse understood not only the spoken word, but understood math, to mean that it was able to do mathematical calculations on its own, which was unheard of in the ani- mal kingdom. Several scientists reviewed this demonstration and declared it genuine. However, when proper con- trols were put into place it turned out that, if the people who knew the correct answer to the question Hans was being asked were not within sight of Hans, Hans could not come up with the correct answer.
    [Show full text]
  • ST~~YI~~ ?F ~F~F Artifacts and Ethics in Behavioral Research
    ?f ~P~f ST~~YI~~ ?f ~f~f Artifacts and Ethics in Behavioral Research Ralph L. Rosnow Temple University Robert Rosenthal Harvard University II w. H. FREEMAN AND COMPANY NEW YORK ACQUISITIONS EDITOR: Susan Finnemore Brennan We are as sailors who are forced to rebuild their ship on the open sea, PROJECT EDITOR: Christine Hastings without ever being able to start fresh from the bottom up. Wherever COVER AND TEXT DESIGNER: Victoria Tomaselli a beam is taken away, immediately a new one must take its place, ILLUSTRATION COORDINATOR: Bill Page PRODUCTION COORDINATOR: Maura Studley and while this is done, the rest of the ship is used as support. In this COMPOSITION: W. H. Freeman Electronic Publishing Center/Susan Cory way, the ship may be completely rebuilt like new with the help of the MANUFACTURING: RR Donnelley & Sons Company old beams and driftwood-but only through gradual rebuilding. 1 Library of Congress Cataloging-in-Publication Data Rosnow, Ralph L. People studying people: artifacts and ethics in behavioral research/ Ralph L. Rosnow, Robert Rosenthal. p. cm. Includes bibliographical references and indexes. ISBN 0- 7167-3070-7. -ISBN 0-7167- 3071-5 (pbk.) 1. Psychology-Research. 2. Social sciences-Research. I. Rosenthal, Robert, 1933- II. Title BF76.5.R645 1997 150'.7'2-dc21 96-48978 CIP © 1997 by W. H. Freeman and Company No part of this book may be reproduced by any mechanical, photographic, or electronic process, or in the form of a phonographic recording, nor may it be stored in a retrieval system, transmitted, or otherwise copied for public or private use, without written permission from the publisher.
    [Show full text]
  • A History of Modern Psychology, 10Th
    This page intentionally left blank This page intentionally left blank A History of Modern Psychology TENTH EDITION DUANE P. SCHULTZ University of South Florida SYDNEY ELLEN SCHULTZ Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it. For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest. A History of Modern Psychology, © 2011 Wadsworth, Cengage Learning Tenth Edition ALL RIGHTS RESERVED. No part of this work covered by the copyright Duane P. Schultz and Sydney Ellen herein may be reproduced, transmitted, stored or used in any form or by Schultz any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribu- Senior Publisher: Linda Schreiber-Ganster tion, information networks, or information storage and retrieval sys- Executive Editor: Jon-David Hague tems, except as permitted under Section 107 or 108 of the 1976 United Editorial Assistant: Sheli DeNola States Copyright
    [Show full text]
  • People Studying People: Artifacts and Ethics in Behavioral Research
    People studying people: artifacts and ethics in behavioral research The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Rosnow, Ralph L. and Robert Rosenthal. 1997. People studying people: artifacts and ethics in behavioral research. New York: W.H. Freeman Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:32300329 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA ?f ~P~f ST~~YI~~ ?f ~f~f Artifacts and Ethics in Behavioral Research Ralph L. Rosnow Temple University Robert Rosenthal Harvard University II w. H. FREEMAN AND COMPANY NEW YORK ACQUISITIONS EDITOR: Susan Finnemore Brennan We are as sailors who are forced to rebuild their ship on the open sea, PROJECT EDITOR: Christine Hastings without ever being able to start fresh from the bottom up. Wherever COVER AND TEXT DESIGNER: Victoria Tomaselli a beam is taken away, immediately a new one must take its place, ILLUSTRATION COORDINATOR: Bill Page PRODUCTION COORDINATOR: Maura Studley and while this is done, the rest of the ship is used as support. In this COMPOSITION: W. H. Freeman Electronic Publishing Center/Susan Cory way, the ship may be completely rebuilt like new with the help of the MANUFACTURING: RR Donnelley & Sons Company old beams and driftwood-but only through gradual rebuilding. 1 Library of Congress Cataloging-in-Publication Data Rosnow, Ralph L.
    [Show full text]
  • Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
    Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin1, Stephan W¨aldchen2, Alexander Binder3, Gr´egoireMontavon2, Wojciech Samek1y and Klaus-Robert M¨uller2;4;5y∗ 1Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute Einsteinufer 37, 10587 Berlin, Germany 2Department of Electrical Engineering and Computer Science, Technische Universit¨atBerlin Marchstr. 23, 10587 Berlin, Germany 3ISTD Pillar, Singapore University of Technology and Design 8 Somapah Rd, Singapore 487372, Singapore 4Department of Brain and Cognitive Engineering, Korea University Anam-dong, Seongbuk-ku, Seoul 136-713, Republic of Korea 5Max Planck Institut f¨urInformatik Campus E1 4, Stuhlsatzenhausweg, 66123 Saarbr¨ucken, Germany yCorresponding authors. E-mail: [email protected], [email protected]. Abstract Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly \intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well- informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
    [Show full text]
  • From Our Readers
    From Our Readers The letters column is a forum for views multiplied beyond necessity." But when on matters raised in previous issues. Let­ we translate this statement into "the ters are more likely to be published if simplest explanation for an observation they are brief and typed double-spaced. is most likely to be the correct one," then They may be edited for space and clarity. I pause. In the first place, if the simplest explanation is most likely to be correct, then it is not always correct. Then how Occam's Razor do we know when it is correct? A law with exceptions is of no use unless we I agree with Elie Shneour's results in his have a rule defining the exceptions. article on Occam's Razor (SI, Summer As a matter of fact, there is no reason 1986), but disagree with the method he to believe that the simplest explanation uses to arrive at them. If we take the for anything is the correct one. There is meaning of Occam's Razor to be "the no scientific or logical basis for this belief. simplest explanation for an observation It is simply a convenient prejudice. is most likely to be the correct one," what Counterexamples abound. Consider John is the observation that he is trying to Dalton's belief that all elemental gases explain? Is he trying to explain the bib­ are composed of molecules, each with a lical account of the Flood? Where is there single atom. This was the simplest theory an observation of a flood or an ark? The that explained the observations of the only thing we observe is a story about a day.
    [Show full text]
  • The Horse That Won't Go Away. Clever Hans, Facilitated
    The Horse That Won’t Go Away Clever Hans, Facilitated Communication, and the Need for Clear Thinking Thomas E. Heinzen William Paterson University Scott O. Lilienfeld Emory University Susan A. Nolan Seton Hall University heinzen_fm.indd 1 11/24/14 1:20 PM Publisher: Rachel Losh Senior Acquisitions Editor: Daniel DeBonis Development Editor: Elsa Peterson Editorial Assistant: Katie Pachnos Senior Marketing Manager: Lindsay Johnson Marketing Assistant: Allison Greco Director of Editing, Design, and Media Production: Tracey Kuehn Managing Editor: Lisa Kinne Production Editor: Janice Stangel Production Manager: Sarah Segal Photo Editor: Richard Fox Art Director: Diana Blume Interior and Cover Designer: Kevin Kall Art Manager: Matthew McAdams Illustrations: Lachina Composition: Linda Harms Printing and Binding: RR Donnelley Library of Congress Preassigned Control Number: 2014956522 ISBN-10: 1-4641-4574-1 ISBN-13: 978-1-4641-4574-2 © 2015 by Worth Publishers All rights reserved. Printed in the United States of America First printing Worth Publishers 41 Madison Avenue New York, NY 10010 www.worthpublishers.com heinzen_fm.indd 2 11/24/14 1:20 PM For Michael Brown –Tom Heinzen To James Randi, tireless and fearless slayer of myths – Scott Lilienfeld For Ranko Bojanic – Susan Nolan heinzen_fm.indd 3 11/24/14 1:20 PM a b o u t t h e a u t h o r s Thomas E. Heinzen is Professor of Psychology at William Paterson University of New Jersey. A graduate of Rockford College, he earned his Ph.D. in social psychology at the State University of New York at Albany. After publishing his first book on frustration and creativity in government, Heinzen worked as a public policy research associate, consulted for the Johns Hopkins Center for Talented Youth, and then began his teaching career.
    [Show full text]