DOCSLIB.ORG

Perceptions of Perceptual Symbols Science Evidence Implicates Sensory-Motor Regions of the Brain in Knowledge Representation (Sect

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Perceptions of Perceptual Symbols Science Evidence Implicates Sensory-Motor Regions of the Brain in Knowledge Representation (Sect BEHAVIORAL AND BRAIN SCIENCES (1999) 22, 577–660 Printed in the United States of America Perceptual symbol systems Lawrence W. Barsalou Department of Psychology, Emory University, Atlanta, GA 30322 [email protected] userwww.service.emory.edu/~barsalou/ Abstract: Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. Keywords: analogue processing; categories; concepts; frames; imagery; images; knowledge; perception; representation; sensory-motor representations; simulation; symbol grounding; symbol systems The habit of abstract pursuits makes learned men much infe- As Figure 1 illustrates, this view begins by assuming that rior to the average in the power of visualization, and much more perceptual states arise in sensory-motor systems. As dis- exclusively occupied with words in their “thinking.” cussed in more detail later (sect. 2.1), a perceptual state can Bertrand Russell (1919b) contain two components: an unconscious neural represen- 1. Introduction tation of physical input, and an optional conscious experi- ence. Once a perceptual state arises, a subset of it is ex- For the last several decades, the fields of cognition and per- tracted via selective attention and stored permanently in ception have diverged. Researchers in these two areas know ever less about each other’s work, and their discoveries have had diminishing influence on each other. In many universi- Lawrence Barsalou is Professor ties, researchers in these two areas are in different programs, of Psychology at Emory University, and sometimes in different departments, buildings, and uni- having previously held positions at versity divisions. One might conclude from this lack of con- the Georgia Institute of Technology tact that perception and cognition reflect independent or and the University of Chicago. He is the author of over 50 scientific pub- modular systems in the brain. Perceptual systems pick up in- lications in cognitive psychology and formation from the environment and pass it on to separate cognitive science. His research ad- systems that support the various cognitive functions, such as dresses the nature of human knowl- language, memory, and thought. I will argue that this view is edge and its roles in language, memory, and thought, fundamentally wrong. Instead, cognition is inherently per- focusing specifically on the perceptual bases of knowl- ceptual, sharing systems with perception at both the cogni- edge, the structural properties of concepts, the dynamic tive and the neural levels. I will further suggest that the di- representation of concepts, the construction of cate- vergence between cognition and perception reflects the gories to achieve goals, and the situated character of widespread assumption that cognitive representations are in- conceptualizations. He is a Fellow of the American Psy- herently nonperceptual, or what I will call amodal. chological Society and of the American Psychological Association, he served as Associate Editor for the Jour- 1.1. Grounding cognition in perception nal of Experimental Psychology: Learning, Memory, and Cognition, and he is currently on the governing In contrast to modern views, it is relatively straightforward board of the Cognitive Science Society. to imagine how cognition could be inherently perceptual. © 1999 Cambridge University Press 0140-525X/99 $12.50 577 Barsalou: Perceptual symbol systems of images. The goal of these attacks was not to exclude im- ages from mentalism, however, but to eliminate mentalism altogether. As a result, image-based theories of cognition disappeared with theories of cognition. 1.2. Amodal symbol systems Following the cognitive revolution in the mid-twentieth century, theorists developed radically new approaches to representation. In contrast to pre-twentieth century think- ing, modern cognitive scientists began working with repre- sentational schemes that were inherently nonperceptual. To a large extent, this shift reflected major developments Figure 1. The basic assumption underlying perceptual symbol outside cognitive science in logic, statistics, and computer systems: Subsets of perceptual states in sensory-motor systems are science. Formalisms such as predicate calculus, probability extracted and stored in long-term memory to function as symbols. theory, and programming languages became widely known As a result, the internal structure of these symbols is modal, and and inspired technical developments everywhere. In cog- they are analogically related to the perceptual states that produced nitive science, they inspired many new representational them. languages, most of which are still in widespread use today (e.g., feature lists, frames, schemata, semantic nets, proce- dural semantics, production systems, connectionism). long-term memory. On later retrievals, this perceptual These new representational schemes differed from ear- memory can function symbolically, standing for referents in lier ones in their relation to perception. Whereas earlier the world, and entering into symbol manipulation. As col- schemes assumed that cognitive representations utilize lections of perceptual symbols develop, they constitute the perceptual representations (Fig. 1), the newer schemes as- representations that underlie cognition. sumed that cognitive and perceptual representations con- Perceptual symbols are modal and analogical. They are stitute separate systems that work according to different modal because they are represented in the same systems as principles. Figure 2 illustrates this assumption. As in the the perceptual states that produced them. The neural sys- framework for perceptual symbol systems in Figure 1, per- tems that represent color in perception, for example, also ceptual states arise in sensory-motor systems. However, the represent the colors of objects in perceptual symbols, at next step differs critically. Rather than extracting a subset least to a significant extent. On this view, a common repre- of a perceptual state and storing it for later use as a symbol, sentational system underlies perception and cognition, not an amodal symbol system transduces a subset of a percep- independent systems. Because perceptual symbols are tual state into a completely new representation language modal, they are also analogical. The structure of a percep- that is inherently nonperceptual. tual symbol corresponds, at least somewhat, to the percep- As amodal symbols become transduced from perceptual tual state that produced it.1 states, they enter into larger representational structures, Given how reasonable this perceptually based view of such as feature lists, frames, schemata, semantic networks, cognition might seem, why has it not enjoyed widespread and production systems. These structures in turn constitute acceptance? Why is it not in serious contention as a theory a fully functional symbolic system with a combinatorial syn- of representation? Actually, this view dominated theories tax and semantics, which supports all of the higher cogni- of mind for most of recorded history. For more than tive functions, including memory, knowledge, language, 2,000 years, theorists viewed higher cognition as inherently and thought. For general treatments of this approach, see perceptual.
Load more

Recommended publications
  • Chapter 2 Introduction to Classical Propositional
    CHAPTER 2 INTRODUCTION TO CLASSICAL PROPOSITIONAL LOGIC 1 Motivation and History The origins of the classical propositional logic, classical propositional calculus, as it was, and still often is called, go back to antiquity and are due to Stoic school of philosophy (3rd century B.C.), whose most eminent representative was Chryssipus. But the real development of this calculus began only in the mid-19th century and was initiated by the research done by the English math- ematician G. Boole, who is sometimes regarded as the founder of mathematical logic. The classical propositional calculus was ¯rst formulated as a formal ax- iomatic system by the eminent German logician G. Frege in 1879. The assumption underlying the formalization of classical propositional calculus are the following. Logical sentences We deal only with sentences that can always be evaluated as true or false. Such sentences are called logical sentences or proposi- tions. Hence the name propositional logic. A statement: 2 + 2 = 4 is a proposition as we assume that it is a well known and agreed upon truth. A statement: 2 + 2 = 5 is also a proposition (false. A statement:] I am pretty is modeled, if needed as a logical sentence (proposi- tion). We assume that it is false, or true. A statement: 2 + n = 5 is not a proposition; it might be true for some n, for example n=3, false for other n, for example n= 2, and moreover, we don't know what n is. Sentences of this kind are called propositional functions. We model propositional functions within propositional logic by treating propositional functions as propositions.
    [Show full text]
  • COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
    Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined.
    [Show full text]
  • Thinking Recursively Part Four
    Thinking Recursively Part Four Announcements ● Assignment 2 due right now. ● Assignment 3 out, due next Monday, April 30th at 10:00AM. ● Solve cool problems recursively! ● Sharpen your recursive skillset! A Little Word Puzzle “What nine-letter word can be reduced to a single-letter word one letter at a time by removing letters, leaving it a legal word at each step?” Shrinkable Words ● Let's call a word with this property a shrinkable word. ● Anything that isn't a word isn't a shrinkable word. ● Any single-letter word is shrinkable ● A, I, O ● Any multi-letter word is shrinkable if you can remove a letter to form a word, and that word itself is shrinkable. ● So how many shrinkable words are there? Recursive Backtracking ● The function we wrote last time is an example of recursive backtracking. ● At each step, we try one of many possible options. ● If any option succeeds, that's great! We're done. ● If none of the options succeed, then this particular problem can't be solved. Recursive Backtracking if (problem is sufficiently simple) { return whether or not the problem is solvable } else { for (each choice) { try out that choice. if it succeeds, return success. } return failure } Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T A R I N G Failure in Backtracking ● Returning false in recursive backtracking does not mean that the entire problem is unsolvable! ● Instead, it just means that the current subproblem is unsolvable.
    [Show full text]
  • Chapter 6 Formal Language Theory
    Chapter 6 Formal Language Theory In this chapter, we introduce formal language theory, the computational theories of languages and grammars. The models are actually inspired by formal logic, enriched with insights from the theory of computation. We begin with the definition of a language and then proceed to a rough characterization of the basic Chomsky hierarchy. We then turn to a more de- tailed consideration of the types of languages in the hierarchy and automata theory. 6.1 Languages What is a language? Formally, a language L is defined as as set (possibly infinite) of strings over some finite alphabet. Definition 7 (Language) A language L is a possibly infinite set of strings over a finite alphabet Σ. We define Σ∗ as the set of all possible strings over some alphabet Σ. Thus L ⊆ Σ∗. The set of all possible languages over some alphabet Σ is the set of ∗ all possible subsets of Σ∗, i.e. 2Σ or ℘(Σ∗). This may seem rather simple, but is actually perfectly adequate for our purposes. 6.2 Grammars A grammar is a way to characterize a language L, a way to list out which strings of Σ∗ are in L and which are not. If L is finite, we could simply list 94 CHAPTER 6. FORMAL LANGUAGE THEORY 95 the strings, but languages by definition need not be finite. In fact, all of the languages we are interested in are infinite. This is, as we showed in chapter 2, also true of human language. Relating the material of this chapter to that of the preceding two, we can view a grammar as a logical system by which we can prove things.
    [Show full text]
  • 1 VITA Updated 1/2021 BARBARA LANDAU Department of Cognitive
    VITA Updated 1/2021 BARBARA LANDAU Department of Cognitive Science Johns Hopkins University 241 Krieger Hall Baltimore, MD. 21218 [email protected] I. Education Ph.D. Psychology, University of Pennsylvania Ed.M. Educational Psychology, Rutgers University B.A. Sociology, University of Pennsylvania II. Positions Held 2001- Dick and Lydia Todd Professor of Cognitive Science, Johns Hopkins University 2018- Gilman Scholar, Johns Hopkins University 2013-2018 Director, Science of Learning Institute, Johns Hopkins University 2011- 2014 Vice Provost for Faculty Affairs, Johns Hopkins University 2006-2011 Chair, Department of Cognitive Science, Johns Hopkins University 2003 Acting Chair, Department of Cognitive Science, Johns Hopkins University 1997- 2000 Professor, Psychology and Linguistics Director, Cognitive Science Program, University of Delaware 1995- 1997 Associate - Full Professor, Psychology and Linguistics, University of Delaware 1990- 1996 Associate- Full Professor of Psychology, University of California, Irvine 1992- 1993 Visiting Scientist, University of Pennsylvania Institute for Research in Cognitive Science 1983- 1991 Assistant- Associate Professor of Psychology, Columbia University 1983 Visiting Instructor of Psychology, Princeton University 1982- 1983 Sloan Post-Doctoral Fellow, University of Pennsylvania Special Committees and Appointments 2017-2021 NSF Alan T. Waterman Award Committee (Chair in 2020) 2020 External Review Committee, Cognitive Science, Dartmouth College 2019 National Academy of Sciences, Atkinson Prize Committee 2015-2018 Chair-Elect, Chair, and Retiring-Chair of Section J Psychology, AAAS 2014; 2019 George Bergeron Visiting Professor in Neuroscience, Center for Brain Plasticity & Recovery, Georgetown University Medical Center 2009-2013 Advisory Committee, Social, Behavioral and Economic Sciences Directorate, NSF 2013 Commission, Developmental Cognitive Neuroscience search, University of Trento, Rovereto, Italy 2013 U.S.
    [Show full text]
  • THE TRUTH GAME (Silvan Mühlemann, Geneva 2019)
    THE TRUTH GAME (Silvan Mühlemann, Geneva 2019) 1 I. DEFENDING AXIOMATIC-DEDUCTIVE SCIENCE ( ) 1. CHOOSING ONE’S AXIOMS: THE EPISTEMOLOGY OF HAPPINESS. According to Karl Popper2, science begins with falsifiable hypotheses. These hypotheses are, as explained by Ashish Dalela, chosen as a function of happiness they provide us. It is intuitively clear that happiness, not truth, is the highest goal in life. “To know the truth we must know the good, but to pick the good, we must desire that good. Ultimately, our cognition of truth depends on our desire. If that desire is modified, then the truths are modified. Similarly, happiness is produced only when the desires are fulfilled. Therefore, if you find the thing that you desire, then you have the double satisfaction of finding truth and goodness. Your brain tells you that you have found the truth, and your heart tells you that you have fulfilled your desire. If you tell a happy person that his ideas about the world are false, he will most likely ignore you, because he knows that since he is happy he must be doing something correctly, and consequently his beliefs must also be true. On the other hand, if you tell an unhappy person that he is suffering because of his false beliefs, and that he must change his beliefs in order to find happiness, he is more likely to listen to your arguments. In short, new knowledge doesn’t come when you are happy, because there is complacency whereby one’s current beliefs are accepted as true just because one is already happy and contented.
    [Show full text]
  • Notes on Mathematical Logic David W. Kueker
    Notes on Mathematical Logic David W. Kueker University of Maryland, College Park E-mail address: [email protected] URL: http://www-users.math.umd.edu/~dwk/ Contents Chapter 0. Introduction: What Is Logic? 1 Part 1. Elementary Logic 5 Chapter 1. Sentential Logic 7 0. Introduction 7 1. Sentences of Sentential Logic 8 2. Truth Assignments 11 3. Logical Consequence 13 4. Compactness 17 5. Formal Deductions 19 6. Exercises 20 20 Chapter 2. First-Order Logic 23 0. Introduction 23 1. Formulas of First Order Logic 24 2. Structures for First Order Logic 28 3. Logical Consequence and Validity 33 4. Formal Deductions 37 5. Theories and Their Models 42 6. Exercises 46 46 Chapter 3. The Completeness Theorem 49 0. Introduction 49 1. Henkin Sets and Their Models 49 2. Constructing Henkin Sets 52 3. Consequences of the Completeness Theorem 54 4. Completeness Categoricity, Quantifier Elimination 57 5. Exercises 58 58 Part 2. Model Theory 59 Chapter 4. Some Methods in Model Theory 61 0. Introduction 61 1. Realizing and Omitting Types 61 2. Elementary Extensions and Chains 66 3. The Back-and-Forth Method 69 i ii CONTENTS 4. Exercises 71 71 Chapter 5. Countable Models of Complete Theories 73 0. Introduction 73 1. Prime Models 73 2. Universal and Saturated Models 75 3. Theories with Just Finitely Many Countable Models 77 4. Exercises 79 79 Chapter 6. Further Topics in Model Theory 81 0. Introduction 81 1. Interpolation and Definability 81 2. Saturated Models 84 3. Skolem Functions and Indescernables 87 4. Some Applications 91 5.
    [Show full text]
  • Mathematical Modeling and Anthropology: Its Rationale, Past Successes and Future Directions
    Mathematical Modeling and Anthropology: Its Rationale, Past Successes and Future Directions Dwight Read, Organizer European Meeting on Cybernetics and System Research 2002 (EMCSR 2002) April 2 - 5, 2002, University of Vienna http://www.ai.univie.ac.at/emcsr/ Abstract When anthropologists talk about their discipline as a holistic study of human societies, particularly non-western societies, mathematics and mathematical modeling does not immediately come to mind, either to persons outside of anthropology and even to most anthropologists. What does mathematics have to do with the study of religious beliefs, ideologies, rituals, kinship and the like? Or more generally, What does mathematical modeling have to do with culture? The application of statistical methods usually makes sense to the questioner when it is explained that these methods relate to the study of human societies through examining patterns in empirical data on how people behave. What is less evident, though, is how mathematical thinking can be part of the way anthropologists reason about human societies and attempt to make sense of not just behavioral patterns, but the underlying cultural framework within which these behaviors are embedded. What is not widely recognized is the way theory in cultural anthropology and mathematical theory have been brought together, thereby constructing a dynamic interplay that helps elucidate what is meant by culture, its relationship to behavior and how the notion of culture relates to concepts and theories developed not only in anthropology but in related disciplines. The interplay is complex and its justification stems from the kind of logical inquiry that is the basis of mathematical reasoning.
    [Show full text]
  • Formal Languages
    Formal Languages Discrete Mathematical Structures Formal Languages 1 Strings Alphabet: a finite set of symbols – Normally characters of some character set – E.g., ASCII, Unicode – Σ is used to represent an alphabet String: a finite sequence of symbols from some alphabet – If s is a string, then s is its length ¡ ¡ – The empty string is symbolized by ¢ Discrete Mathematical Structures Formal Languages 2 String Operations Concatenation x = hi, y = bye ¡ xy = hibye s ¢ = s = ¢ s ¤ £ ¨© ¢ ¥ § i 0 i s £ ¥ i 1 ¨© s s § i 0 ¦ Discrete Mathematical Structures Formal Languages 3 Parts of a String Prefix Suffix Substring Proper prefix, suffix, or substring Subsequence Discrete Mathematical Structures Formal Languages 4 Language A language is a set of strings over some alphabet ¡ Σ L Examples: – ¢ is a language – ¢ is a language £ ¤ – The set of all legal Java programs – The set of all correct English sentences Discrete Mathematical Structures Formal Languages 5 Operations on Languages Of most concern for lexical analysis Union Concatenation Closure Discrete Mathematical Structures Formal Languages 6 Union The union of languages L and M £ L M s s £ L or s £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 7 Concatenation The concatenation of languages L and M £ LM st s £ L and t £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 8 Kleene Closure The Kleene closure of language L ∞ ¡ i L £ L i 0 Zero or more concatenations Discrete Mathematical Structures Formal Languages 9 Positive Closure The positive closure of language L ∞ i L £ L
    [Show full text]
  • Propositional Logic: Semantics and an Example
    Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Logic: Semantics and an Example CPSC 322 { Logic 2 Textbook x5.2 Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 1 Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Lecture Overview 1 Recap: Syntax 2 Propositional Definite Clause Logic: Semantics 3 Using Logic to Model the World 4 Proofs Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 2 Definition (body) A body is an atom or is of the form b1 ^ b2 where b1 and b2 are bodies. Definition (definite clause) A definite clause is an atom or is a rule of the form h b where h is an atom and b is a body. (Read this as \h if b.") Definition (knowledge base) A knowledge base is a set of definite clauses Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Definite Clauses: Syntax Definition (atom) An atom is a symbol starting with a lower case letter Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 3 Definition (definite clause) A definite clause is an atom or is a rule of the form h b where h is an atom and b is a body. (Read this as \h if b.") Definition (knowledge base) A knowledge base is a set of definite clauses Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Definite Clauses: Syntax Definition (atom) An atom is a symbol starting with a lower case letter Definition (body) A body is an atom or is of the form b1 ^ b2 where b1 and b2 are bodies.
    [Show full text]
  • Symbol Sense: Informal Sense-Making in Formal Mathematics
    Symbol Sense: Informal Sense-making in Formal Mathematics ABRAHAM ARCA VI Prologue Irineo Funes, the "memorious ", was created from Borges ~s fan­ tions and formulating mathematical arguments (proofs) tastic palette He had an extraordinarity retentive memory, he Instruction does not always provide opportunities not only was able to remember everything, be it the shapes of the southern not to memorize, but also to "forget" rules and details and clouds on the dawn of April 30th, 1882, or all the words in to be able to see through them in order to think, abstract, Engli'>h, French, Portuguese and Latin Each one of his experi­ generalize, and plan solution strategies Therefore, it ences was fully and accurately registered in his infinite memory would seem reasonable to attempt a description of a paral­ On one occasion he thought of "reducing" each past day of his lel notion to that of "number sense" in arithmetic: the idea life to s·eventy thousand remembrances to be referred to by num­ of "symbol sense".[!] bers. Two considerations dissuaded him: the task was inter­ minable and futile. Towards the end of the story, the real tragedy What is symbol sense? A first round of Funes is revealed He was incapable of general, "platonic " Compared to the attention which has been given to "num­ ideas For example, it was hard for him to understand that the ber sense", there is very little in the literature on "'symbol generic name dog included so many individuals of diverse sizes sense". One welcome exception is Fey [1990] He does not and shapes Moreover,
    [Show full text]
  • Sacredness in an Experimental Chamber
    BEHAVIORAL AND BRAIN SCIENCES (2006) 29, 161–209 Printed in the United States of America Moneyastool,moneyasdrug:The biological psychology of a strong incentive Stephen E. G. Lea University of Exeter, School of Psychology, Washington Singer Laboratories, Exeter EX4 4QG, United Kingdom [email protected] http://www.exeter.ac.uk/SEGLea Paul Webley University of Exeter, School of Psychology, Washington Singer Laboratories, Exeter EX4 4QG, United Kingdom [email protected] http://www.exeter.ac.uk/pwebley Abstract: Why are people interested in money? Specifically, what could be the biological basis for the extraordinary incentive and reinforcing power of money, which seems to be unique to the human species? We identify two ways in which a commodity which is of no biological significance in itself can become a strong motivator. The first is if it is used as a tool, and by a metaphorical extension this is often applied to money: it is used instrumentally, in order to obtain biologically relevant incentives. Second, substances can be strong motivators because they imitate the action of natural incentives but do not produce the fitness gains for which those incentives are instinctively sought. The classic examples of this process are psychoactive drugs, but we argue that the drug concept can also be extended metaphorically to provide an account of money motivation. From a review of theoretical and empirical literature about money, we conclude that (i) there are a number of phenomena that cannot be accounted for by a pure Tool Theory of money motivation; (ii) supplementing Tool Theory with a Drug Theory enables the anomalous phenomena to be explained; and (iii) the human instincts that, according to a Drug Theory, money parasitizes include trading (derived from reciprocal altruism) and object play.
    [Show full text]