View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Scholarship@Western Western University Scholarship@Western Electronic Thesis and Dissertation Repository 4-20-2011 12:00 AM Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How The Mind Works Sheldon J. Chow The University of Western Ontario Supervisor Dr. Christopher Viger The University of Western Ontario Graduate Program in Philosophy A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Sheldon J. Chow 2011 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Philosophy of Mind Commons Recommended Citation Chow, Sheldon J., "Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How The Mind Works" (2011). Electronic Thesis and Dissertation Repository. 128. https://ir.lib.uwo.ca/etd/128 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. HEURISTICS, CONCEPTS, AND COGNITIVE ARCHITECTURE: TOWARD UNDERSTANDING HOW THE MIND WORKS (Spine title: Heuristics, Concepts, and Cognitive Architecture) (Thesis format: Monograph) by Sheldon Chow Graduate Program in Philosophy A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy The School of Graduate and Postdoctoral Studies The University of Western Ontario London, Ontario, Canada c Sheldon Joseph Chow 2011 THE UNIVERSITY OF WESTERN ONTARIO School of Graduate and Postdoctoral Studies CERTIFICATE OF EXAMINATION Examiners: Supervisor: ..................... Dr. John Nicholas ..................... Dr. Christopher Viger ..................... Supervisory Committee: Dr. Benjamin Hill ..................... ..................... Dr. Robert Stainton Dr. Bertram Gawronski ..................... Dr. Richard Samuels The thesis by Sheldon Joseph Chow entitled: Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How The Mind Works is accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy ............... .............................. Date Chair of the Thesis Examination Board ii Abstract Heuristics are often invoked in the philosophical, psychological, and cognitive science liter- atures to describe or explain methodological techniques or “shortcut” mental operations that help in inference, decision-making, and problem-solving. Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspec- tion of the way(s) in which “heuristic” is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their role in cognition. This dissertation seeks to remedy this situation by motivating philosophical inquiry into heuristics and heuristic reasoning, and then advancing a theory of how heuristics operate in cognition. I develop a positive working characterization of heuristics that is coherent and robust enough to account for a broad range of phenomena in reasoning and inference, and makes sense of empirical data in a systematic way. I then illustrate the work this characterization does by considering the sorts of problems that many philosophers believe heuristics solve, namely those resulting from the so-called frame problem. Considering the frame problem motivates the need to gain a better understanding of how heuristics work and the cognitive structures over which they oper- ate. I develop a general theory of cognition which I argue underwrites the heuristic operations that concern this dissertation. I argue that heuristics operate over highly organized systems of knowledge, and I offer a cognitive architecture to accommodate this view. I then provide an account of the systems of knowledge that heuristics are supposed to operate over, in which I suggest that such systems of knowledge are concepts. The upshot, then, is that heuristics oper- ate over concepts. I argue, however, that heuristics do not operate over conceptual content, but over metainformational relations between activated and primed concepts and their contents. Fi- nally, to show that my thesis is empirically adequate, I consider empirical evidence on heuristic reasoning and argue that my account of heuristics explains the data. Keywords: Heuristics; concepts; mental representation; cognition; cognitive architecture; relevance; the frame problem; reasoning; inference iii Acknowlegements I would like to thank Chris Viger, my supervisor, for all our discussions and for all his com- ments, suggestions, and general guidance as I wrote and worked through this dissertation. Because of him I was able to improve and sharpen my ideas and writing, and he was a tremen- dous help in turning a rough and vague idea about heuristics into the present work. I would also like to thank Rob Stainton for his help throughout the writing process. Further, although he probably doesn’t know it, John Woods was an influential figure in my undergrad, and he got me interested in heuristics and humans as satisficing agents. So I extend sincere thanks to him for in some sense initiating my work. Barry Hoffmaster was also instrumental in initiating some of my ideas—it was after taking a class of his in my PhD program that I first realized that there wasn’t an agreed-upon account of heuristics. Many thanks also go to my examina- tion committee for their insightful questions, suggestions, and general input: John Nicholas, Ben Hill, Bertram Gawronski, and Richard Samuels. Finally, but not least, I thank my loving partner, Janet, who continues to support me and put up with me, even as I went through grad school and wrote this dissertation, and spent long days in front of my computer instead of by her side. 1 iv Contents Certificate of Examination ii Abstract iii Acknowledgements iv List of Figures 1 1 Introduction 2 1.1 Dissertation outline . 3 1.2 Computational cognition . 8 1.3 Philosophy in cognitive science . 9 2 Characterizing “Heuristic” 13 2.1 Why we need an appropriate characterization of “heuristic” . 16 2.2 Abuses of “heuristic” . 18 2.2.1 Heuristics vs. guaranteed correct outcomes . 19 2.2.2 Heuristics vs. optimization . 28 2.2.3 Inherent normativity? . 32 2.3 Uses of “heuristic” . 34 2.3.1 Processes of discovery: Polya´ . 36 2.3.2 Information processing theory: Simon . 37 2.3.3 Heuristics and biases: Kahneman and Tversky . 40 2.3.4 Fast and frugal heuristics: Gigerenzer . 42 2.3.5 Other uses . 45 2.4 A positive characterization . 47 2.4.1 Some distinctions . 49 2.4.1.1 Stimulus-response vs. heuristics . 49 2.4.1.2 Computational vs. cognitive heuristics . 52 2.4.1.3 Perceptual vs. cognitive heuristics . 52 2.4.1.4 Methodological vs. inferential heuristics . 53 2.4.2 Characterizing cognitive heuristics . 57 2.4.2.1 Heuristics as rules of thumb . 59 2.4.2.2 Beyond rules of thumb: exploiting information . 69 2.5 Concluding remarks . 72 v 3 Beginning to Understand How Heuristics Work 74 3.1 Heuristics and relevance problems . 77 3.1.1 The many guises of the frame problem . 78 3.1.2 The heuristic solution . 84 3.2 Toward a general architecture . 94 3.3 Informationally rich structures: k-systems . 101 3.4 Exploiting informational richness . 108 3.4.1 High information load cognition . 109 3.4.2 Exploiting k-systems, but still frugal . 117 3.5 Concluding remarks . 121 4 Concepts, Heuristics, and Relevance 124 4.1 Concepts and representation . 127 4.1.1 Perceptual symbol systems . 127 4.1.2 The file metaphor . 134 4.2 A theory of richer concepts . 138 4.2.1 Conceptual content and associations . 138 4.2.2 A critique of the PSS theory of concepts . 143 4.2.2.1 Integrating linguistic information . 146 4.2.2.2 Affording connections between concepts . 151 4.2.3 Putting it together . 154 4.3 From k-systems to concepts . 162 4.3.1 What this means for heuristics . 166 4.4 How heuristics work: Informational relations and higher-order operations . 169 4.5 Concluding remarks . 180 5 How Heuristics Work: Considering Empirical Evidence 182 5.1 Heuristics and biases . 183 5.1.1 Availability . 186 5.1.2 Representativeness . 190 5.1.2.1 The Linda problem . 192 5.1.2.2 The Dick problem . 197 5.1.2.3 The cab problem . 199 5.2 Fast and frugal heuristics . 205 5.2.1 The Recognition heuristic . 206 5.2.2 Take the Best . 213 5.3 General remarks . 221 6 Relevance and the epistemological relevance problem 223 6.1 The epistemological relevance problem . 225 6.2 Relevance . 228 6.2.1 Sperber and Wilson’s Relevance Theory . 228 6.2.2 Relevance in light of the foregoing theory of concepts and cognition . 233 6.3 Do heuristics determine relevance? . 239 6.4 Concluding remarks . 246 vi References 247 Curriculum Vitae 256 vii List of Figures 2.1 Classifying heuristics . 58 3.1 The Muller-Lyer¨ Illusion. (a) appears shorter than (b), although the lines are the same length; and (a) continues to appear shorter than (b) despite knowing that the lines are of equal length. The visual system is therefore said to be encapsulated with respect to the belief that the lines are of equal length. 85 4.1 Simplified representation of thin and thick ER-structures. The nodes represent richly structured activated and primed concepts, and the arrows represent in- ward/outward connections. The concepts bearing the inward connections are deemed pertinent to the task at hand. With respect to the thick ER-structure represented, the concepts on the left constitute a set of reference concepts that bear multiple outward connections to the pertinent concepts on the right. 173 5.1 A graphical representation of the cab problem expressed in terms of natural frequencies. 203 5.2 Representation of an ER-structure arising from Goldstein and Gigerenzer’s (1999) task of predicting which of two cities is larger.