DOCSLIB.ORG

Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing 32 International Journal of Signs and Semiotic Systems, 2(1), 32-71, January-June 2012 Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing William J. Rapaport, University at Buffalo, The State University of New York, USA ABSTRACT In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, the author argues that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. The author also argues that, if semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, the author suggests that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, the author takes issue with Fetzer’s arguments to the contrary. Keywords: Cognition, Computationalism, Semantics, Semiotic Systems, Syntax 1. INTRODUCTION view that cognition (human or otherwise) is computable (§2). On this view, it follows that This essay is a reply to James H. Fetzer’s essay computationalism can be true even if (human) in this journal, “Minds and Machines: Limits cognition is not the result of computations in to Simulations of Thought and Action” (Fetzer, the brain. Second, I argue that, if semiotic sys- 2011). I treat three topics: computationalism, tems are systems that interpret signs, then both semiotic systems, and cognition (the mind), humans and computers are semiotic systems offering what I feel is the proper treatment of (§5.2). Finally, I suggest that minds should be computationalism. From this, certain views considered as virtual machines implemented about semiotic systems and minds follow in certain semiotic systems: primarily brains, (or, at least, are consistent): First, I argue that but also AI computers (§6.3). In the course of computationalism should not be understood as presenting and arguing for these positions, I the view that (human) cognition is computa- respond to Fetzer’s arguments to the contrary tion, but that it should be understood as the (§§5–8).1 DOI: 10.4018/ijsss.2012010102 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. International Journal of Signs and Semiotic Systems, 2(1), 32-71, January-June 2012 33 2. THE PROPER TREATMENT computation and cognition as processes of OF COMPUTATIONALISM fundamentally the same type is the fact that both are physically realized and both are Computationalism is often characterized as the governed by rules and representations.… thesis that cognition is computation. Its origins (Pylyshyn, 1980, p. 111). can be traced back at least to Thomas Hobbes: • [C]ognition is a type of computation (Py- lyshyn, 1985, p. xiii). “For REASON, in this sense [i.e., “as among • The basic idea of the computer model of the the faculties of the mind”], is nothing but reck- mind is that the mind is the program and oning—that is, adding and subtracting—of the the brain the hardware of a computational consequences of general names agreed upon for system (Searle, 1990, p. 21). the marking and signifying of our thoughts…” • Computationalism is the hypothesis that (Hobbes 1651, Part I, Ch. 5, p. 46)2 cognition is the computation of functions. …The job for the computationalist is to de- It is a view whose popularity, if not its termine…which specific functions explain origins, has been traced back to McCulloch and specific cognitive phenomena (Dietrich, Pitts (1943), Putnam (1960 or 1961), and Fodor 1990, p. 135, emphasis added). (1975) (Horst, 2009; Piccinini, 2010). This is • [T]he Computational Theory of Mind… usually interpreted to mean that the mind, or is…the best theory of cognition that we’ve the brain—whatever it is that exhibits cogni- got…. (Fodor, 2000, p. 1). tion—computes, or is a computer. Consider • Tokens of mental processes are ‘computa- these passages, more or less (but not entirely) tions’; that is, causal chains of (typically randomly chosen:3 inferential) operations on mental represen- tations (Fodor, 2008, pp. 5-6). • The core idea of cognitive science is that • A Plan is any hierarchical process in the our brains are a kind of computer…. Psy- organism that can control the order in which chologists try to find out exactly what kinds a sequence of operations is to be performed. of programs our brains use, and how our A Plan is, for an organism, essentially the brains implement those programs (Gopnik, same as a program for a computer (Miller 2009, p. 43). et al., 1960, p. 16).4 • [A] particular philosophical view that • [H]aving a propositional attitude is being holds that the mind literally is a digital in some computational relation to an in- computer…, and that thought literally is ternal representation. …Mental states are a kind of computation…will be called relations between organisms and internal the “Computational Theory of Mind”…. representations, and causally interrelated (Horst, 2009). mental states succeed one another accord- • Computationalism…is the view that the ing to computational principles which apply functional organization of the brain (or formally to the representations (Fodor, any other functionally equivalent system) 1975, p. 198). is computational, or that neural states are • [C]ognition ought to be viewed as computa- computational states (Piccinini, 2010, p. tion. [This] rests on the fact that computa- 271). tion is the only worked-out view of process • These remarkable capacities of comput- that is both compatible with a materialist ers—to manipulate strings of digits and view of how a process is realized and that to store and execute programs—suggest a attributes the behavior of the process to the bold hypothesis. Perhaps brains are com- operation of rules upon representations. In puters, and perhaps minds are nothing but other words, what makes it possible to view Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 34 International Journal of Signs and Semiotic Systems, 2(1), 32-71, January-June 2012 the programs running on neural computers a (particular kind of) problem, the answer (Piccinini, 2010, pp. 277-278). to a (particular kind of) question, or the ac- complishment of some (particular kind of) That cognition is computation is an inter- task. (See the Appendix for more details.) esting claim, one well worth exploring, and it may even be true. But it is too strong: It is not Various of these features can be relaxed: the kind of claim that is usually made when one One can imagine a procedure that has all these says that a certain behavior can be understood features of algorithms but that has no specific computationally (Rapaport, 1998). There is a goal, e.g., “Compute 2+2; then read Moby related claim that, because it is weaker, is more Dick.”, or one for which there is no executor, or likely to be true and—more importantly—is one that yields output that is only approximately equally relevant to computational theories correct (sometimes called a ‘heuristic’; see of cognition, because it preserves the crucial §6.1), etc. For alternative informal formulations insight that cognition is capable of being ex- of “algorithm”, see the Appendix. plained in terms of the mathematical theory of Several different mathematical, hence computation. precise, formulations of this still vague notion Before stating what I think is the proper have been proposed, the most famous of which version of the thesis of computationalism, let is Alan Turing’s (1936) notion of (what is now me clarify two terms: called in his honor) a ‘Turing machine’. Because all of these precise, mathematical formula- 1. I will use ‘cognition’5 as a synonym for tions are logically equivalent, the claim that such terms as ‘thinking’, ‘intelligence’ the informal notion of “algorithm” is a Turing (as in ‘AI’, not as in ‘IQ’), ‘mentality’, machine is now known as “Turing’s thesis” ‘understanding’, ‘intentionality’, etc. (or as “Church’s thesis” or the “Church-Turing Cognition is whatever cognitive scientists thesis”, after Alonzo Church, whose “lambda study, including (in alphabetical order) calculus” was another one of the mathematical believing (and, perhaps, knowing), con- formulations). sciousness, emotion, language, learning, Importantly, for present purposes, when memory, (perhaps) perception, planning, someone says that a mathematical function problem solving, reasoning, representation (see note 42) or a certain phenomenon or be- (including categories, concepts, and mental havior is “computable”, they mean that there imagery), sensation, thought, etc. Knowing is an algorithm that outputs the values of that might not be part of cognition, insofar as it function when given its legal inputs6 or that depends on the way the world is (knowing produces that phenomenon or behavior—i.e., is often taken to be justified true belief) that one could write a computer program that, and thus would be independent of what when executed on a suitable computer, would goes on in the mind or brain; perception enable that computer to perform (i.e., to output) also depends on the way the world is (see the appropriate behavior. §3.1). Hence, 2. An “algorithm” for an executor E to achieve a goal G is, informally, a procedure (or Computationalism, properly understood, should “method”) for E to achieve G, where (a) be the thesis that cognition is computable, i.e., E is the agent—human or computer—that that there is an algorithm (more likely, a family of carries out the algorithm (or executes, algorithms) that computes cognitive functions. or implements, or “follows” it), (b) the procedure is a set (usually, a sequence) of I take the basic research question of com- statements (or “steps”, usually “rules” or putational cognitive science to ask, “How much instructions), and (c) G is the solution of Copyright © 2012, IGI Global.
Load more

Recommended publications
  • Hypertext Semiotics in the Commercialized Internet
    Hypertext Semiotics in the Commercialized Internet Moritz Neumüller Wien, Oktober 2001 DOKTORAT DER SOZIAL- UND WIRTSCHAFTSWISSENSCHAFTEN 1. Beurteiler: Univ. Prof. Dipl.-Ing. Dr. Wolfgang Panny, Institut für Informationsver- arbeitung und Informationswirtschaft der Wirtschaftsuniversität Wien, Abteilung für Angewandte Informatik. 2. Beurteiler: Univ. Prof. Dr. Herbert Hrachovec, Institut für Philosophie der Universität Wien. Betreuer: Gastprofessor Univ. Doz. Dipl.-Ing. Dr. Veith Risak Eingereicht am: Hypertext Semiotics in the Commercialized Internet Dissertation zur Erlangung des akademischen Grades eines Doktors der Sozial- und Wirtschaftswissenschaften an der Wirtschaftsuniversität Wien eingereicht bei 1. Beurteiler: Univ. Prof. Dr. Wolfgang Panny, Institut für Informationsverarbeitung und Informationswirtschaft der Wirtschaftsuniversität Wien, Abteilung für Angewandte Informatik 2. Beurteiler: Univ. Prof. Dr. Herbert Hrachovec, Institut für Philosophie der Universität Wien Betreuer: Gastprofessor Univ. Doz. Dipl.-Ing. Dr. Veith Risak Fachgebiet: Informationswirtschaft von MMag. Moritz Neumüller Wien, im Oktober 2001 Ich versichere: 1. daß ich die Dissertation selbständig verfaßt, andere als die angegebenen Quellen und Hilfsmittel nicht benutzt und mich auch sonst keiner unerlaubten Hilfe bedient habe. 2. daß ich diese Dissertation bisher weder im In- noch im Ausland (einer Beurteilerin / einem Beurteiler zur Begutachtung) in irgendeiner Form als Prüfungsarbeit vorgelegt habe. 3. daß dieses Exemplar mit der beurteilten Arbeit überein
    [Show full text]
  • Semiotics of Strategy Graeme Carswell Macleod Smith MA. Dipm
    Knowledge Management through Storytelling and Narrative – Semiotics of Strategy Graeme Carswell MacLeod Smith MA. DipM. PGCLTHE. MCIM. FIDM. AFHEA School of Business, Law and Communications Solent University Southampton A thesis submitted in partial fulfilment of the requirements of the Nottingham Trent University and Solent University for the degree of Doctor of Philosophy. A thesis submitted in fulfilment of the requirements of Solent University for the degree of Doctor of Philosophy October 2019 i Copyright Statement This work is the intellectual property of Graeme Smith. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the owner of the Intellectual Property Rights. ii Acknowledgments The author wishes to acknowledge the invaluable assistance of the following people whose contribution, advice and encouragement have done so much to bring this work to fruition. To Professor Steven Henderson, my former Director of Studies, whose advice, interest and expertise was always inspiring. You instilled in me so many research skills, such as critical thinking and helped to guide me away from many a youthful folly. And Professor Deborah Blackman who set me on this road of discovery in the field of knowledge management. To Emeritus Professor Mike Wilkinson, who stepped in as Director of Studies and instilled some ‘management’ rigour to the project and immediately saw merit in this whole endeavour.
    [Show full text]
  • Non-Anthropogenic Mind and Complexes of Cultural Codes
    Semiotica 2016; 213: 63–73 Sergey Kulikov* Non-anthropogenic mind and complexes of cultural codes DOI 10.1515/sem-2015-0034 Abstract: The object of research is to clarify the connections between non- anthropogenic mind and culture as sign systems. Investigation of such an object discloses the perspectives on construction of the generalized model of mind and can help to build the bridge between traditional and digital humanities. The subject of traditional humanities is natural human activity; the subject of digital humanities is computer-based forms of activity and communication. Finding signs created not only by human but also by natural circumstances helps to define the sign system that unites the natural (non-anthropogenic) and artificial kinds of mind. Methodology of research includes the principles of semiotics previously developed by Charles Peirce and Ferdinand de Saussure and expanded by Yuri Lotman and Boris Uspensky. Semiotic interpretation of mind as the object of culture allows the building of a generalized model of mind as one of textual constructions, presenting the history of mankind as the replacement of natural events by secondary models. The author concludes that the revealing of a generalized model of mind opens new opportunities for the construction of the intelligent activity strictly interpreted as special sign systems. Semiotic studies interpret culture as a rationality making machine, and activity of mind is caused by the work of such a machine. Because of that, if traditional meaning sign systems were estimated as human-made kinds of complex of primary signs, then modern statements help to see the absence of an irresistible limit to interpret such complex as a nature-made but non-anthropogenic phenomenon.
    [Show full text]
  • Charles Sanders Peirce - Wikipedia, the Free Encyclopedia 9/2/10 4:55 PM
    Charles Sanders Peirce - Wikipedia, the free encyclopedia 9/2/10 4:55 PM Charles Sanders Peirce From Wikipedia, the free encyclopedia Charles Sanders Peirce (pronounced /ˈpɜrs/ purse[1]) Charles Sanders Peirce (September 10, 1839 – April 19, 1914) was an American philosopher, logician, mathematician, and scientist, born in Cambridge, Massachusetts. Peirce was educated as a chemist and employed as a scientist for 30 years. It is largely his contributions to logic, mathematics, philosophy, and semiotics (and his founding of pragmatism) that are appreciated today. In 1934, the philosopher Paul Weiss called Peirce "the most original and versatile of American philosophers and America's greatest logician".[2] An innovator in many fields (including philosophy of science, epistemology, metaphysics, mathematics, statistics, research methodology, and the design of experiments in astronomy, geophysics, and psychology) Peirce considered himself a logician first and foremost. He made major contributions to logic, but logic for him encompassed much of that which is now called epistemology and philosophy of science. He saw logic as the Charles Sanders Peirce formal branch of semiotics, of which he is a founder. As early as 1886 he saw that logical operations could be carried out by Born September 10, 1839 electrical switching circuits, an idea used decades later to Cambridge, Massachusetts produce digital computers.[3] Died April 19, 1914 (aged 74) Milford, Pennsylvania Contents Nationality American 1 Life Fields Logic, Mathematics, 1.1 United States Coast Survey Statistics, Philosophy, 1.2 Johns Hopkins University Metrology, Chemistry 1.3 Poverty Religious Episcopal but 2 Reception 3 Works stance unconventional 4 Mathematics 4.1 Mathematics of logic C.
    [Show full text]
  • Peirce's Sign Theory As an Open-Source R Package
    Signs vol. 8: pp. 1-24, 2017 ISSN: 1902-8822 Peirce’s sign theory as an open-source R package Alon Friedman1 and Erin Feichtinger2 Abstract: Throughout Peirce’s writing, we witness his developing vision of a machine that scientists will eventually be able to create. Nadin (2010) raised the question: Why do computer scientists continue to ignore Peirce’s sign theory? A review of the literature on Peirce’s theory and the semiotics machine reveals that many authors discussed the machine; however, they do not differentiate between a physical computer machine and its software. This paper discusses the problematic issues involved in converting Peirce’s theory into a programming language, machine and software application. We demonstrate this challenge by introducing Peirce’s sign theory as a software application that runs under an open-source R environment. Keywords: Peirce sign theory, Software, Open source R, package, Fuzzy logic. 1. Introduction Semiotics, the study of signs, arises from philosophical speculation about signification and language (Chandler, 2004). In the 19th century, deliberation about the meaning of the term “signs” developed in two schools of thought that provided different interpretations. One was promulgated by American philosopher Charles Sanders Peirce, who proposed the study of the sign as "semeiosis." Peirce offered a triadic foundation for the term, in which anything can be a sign as long as someone interprets it as signifying something, referring to, or standing for something other than itself (Peirce, 1931-58, 2.302). Another school of thought, developed by Ferdinand de Saussure (1916/1983, 114), introduces the structuralist point of view which emphasizes the synchronic dimension in the study of language.
    [Show full text]
  • Semiotics ­ Wikipedia, the Free Encyclopedia Semiotics from Wikipedia, the Free Encyclopedia
    11/03/2016 Semiotics ­ Wikipedia, the free encyclopedia Semiotics From Wikipedia, the free encyclopedia Semiotics (also called semiotic studies; not to be confused with the Saussurean tradition called semiology which is a part of semiotics) is the study of meaning­making, the study of sign processes and meaningful communication.[1] This includes the study of signs and sign processes (semiosis), indication, designation, likeness, analogy, metaphor, symbolism, signification, and communication. Semiotics is closely related to the field of linguistics, which, for its part, studies the structure and meaning of language more specifically. The semiotic tradition explores the study of signs and symbols as a significant part of communications. As different from linguistics, however, semiotics also studies non­linguistic sign systems. Semiotics is often divided into three branches: Semantics: relation between signs and the things to which they refer; their signified denotata, or meaning Syntactics: relations among or between signs in formal structures Pragmatics: relation between signs and sign­using agents or interpreters Semiotics is frequently seen as having important anthropological dimensions; for example, the late Italian novelist Umberto Eco proposed that every cultural phenomenon may be studied as communication.[2] Some semioticians focus on the logical dimensions of the science, however. They examine areas belonging also to the life sciences—such as how organisms make predictions about, and adapt to, their semiotic niche in the world (see semiosis). In general, semiotic theories take signs or sign systems as their object of study: the communication of information in living organisms is covered in biosemiotics (including zoosemiotics). Syntactics is the branch of semiotics that deals with the formal properties of signs and symbols.[3] More precisely, syntactics deals with the "rules that govern how words are combined to form phrases and sentences".
    [Show full text]
  • A Theory of Computer Semiotics: Semiotic Approaches to Construction and Assessment of Computer Systems
    A Theory of Computer Semiotics: Semiotic Approaches to Construction and Assessment of Computer Systems P. B. Andersen (University of Aarhus) Cambridge, England: Cambridge University Press (Cambridge Series on Human-Computer Interaction 3), 1990, vii + 416 pp. Hardbound, ISBN 0-521-39366-1, $59.50 Reviewed by Robin P. Fawcett University of Wales College of Cardiff 1. Semiotics: Its Relevance to Computational Linguistics Semiotics is, or seeks to be, the science of sign systems, just as linguistics is, or seeks to be, the science of language. Semiotics therefore includes linguistics, as well as the study of all other sign systems. These include: • Those sign systems that are used in parallel with language, such as tone of voice and body posture (including pointing, etc). • Those that operate on a longer time scale such as the 'presentation of self' through our choice of clothing, of car, etc. • Those with a broader conception of 'self,' such as the self presented by civic architecture, etc. Semiotics also includes the general principles that underlie all sign systems. It is thus more comprehensive than linguistics--very much more, because there is a semiotic dimension to practically every human artifact. (Indeed, since we can 'read in' meanings to natural events, e.g., as in 'the lack of rain shows God's displeasure,' semiotics is not even limited to human artifacts.) In recent years there has been a rapid growth of interest in the ways that humans and machines may communicate with each other by the use of modes and codes other than written natural language text. For example, one theme of the recent International Natural Language Generation Workshop at Trento was the 'extension of language generation to multiple media.' Relevant contributions in Dale et al.
    [Show full text]
  • Computational Opposition Analysis Using Word Embeddings: a Method for Strategising Resonant Informal Argument
    Argument & Computation 10 (2019) 301–317 301 DOI 10.3233/AAC-190467 IOS Press Computational opposition analysis using word embeddings: A method for strategising resonant informal argument Cameron Shackell ∗ and Laurianne Sitbon Queensland University of Technology, 2 George St, Brisbane 4000, Australia Abstract. In informal argument, an essential step is to ask what will “resonate” with a particular audience and hence persuade. Marketers, for example, may recommend a certain colour for a new soda can because it “pops” on Instagram; politicians may “fine-tune” diction for different social demographics. This paper engages the need to strategise for such resonance by offering a method for automating opposition analysis (OA), a technique from semiotics used in marketing and literary analysis to plot objects of interest on oppositional axes. Central to our computational approach is a reframing of texts as proxies for thought and opposition as the product of oscillation in thought in response to those proxies, a model to which the contextual similarity information contained in word embeddings is relevant. We illustrate our approach with an analysis of texts on gun control from ProCon.org, implementing a three-step method to: 1) identify relatively prominent signifiers; 2) rank possible opposition pairs on prominence and contextual similarity scores; and 3) derive plot values for proxies on opposition pair axes. The results are discussed in terms of strategies for informal argument that might be derived by those on each side of gun control. Keywords: Informal argumentation, automated persuasion, semiotics, computational opposition analysis 1. Introduction Our fascination with networked computers exposes us daily to hundreds if not thousands of infor- mal arguments that seek to influence our behaviour.
    [Show full text]
  • On Evolution of Thinking About Semiosis: Semiotics Meets Cognitive Science
    On evolution of thinking about semiosis ... AVANT, Vol. VII, No. 2/2016 ISSN: 2082-6710 avant.edu.pl/en DOI: 10.26913/70202016.0112.0005 On evolution of thinking about semiosis: semiotics meets cognitive science Piotr Konderak Department of Logic and Cognitive Science Maria Curie-Sklodowska University in Lublin kondorp@ bacon.umcs.lublin.pl Abstract The aim of the paper is to sketch an idea—seen from the point of view of a cognitive scientist—of cognitive semiotics as a discipline. Consequently, the article presents aspects of the relationship between the two disciplines: semi- otics and cognitive science. The main assumption of the argumentation is that at least some semiotic processes are also cognitive processes. At the methodo- logical level, this claim allows for application of cognitive models as explana- tions of selected semiotic processes. In particular, the processes of embedded interpretation (in contrast to interpretability in principle) are considered: belief revision, dynamic organization of meaning and metaknowledge. The explanations are formulated in terms of artificial cognitive agents of the GLAIR/SNePS cognitive architecture. Finally, it is suggested that even if some- one rejects the idea of artificial cognitive systems as simulations of semiotic processes, they may acknowledge the usefulness of cognitive modeling in analysis of semiotic processes in virtual, simulated worlds and in the area of “new media”. Keywords: cognitive semiotics; cognitive science; computational modeling; semiosis; Peirce 1. Setting the scene Cognitive Semiotics can be defined as an interdisciplinary matrix of disci- plines and methods, focused on the multifaceted phenomenon of meaning or as an emerging field with the ambition of …integrating methods and theories developed in the disciplines of cognitive sci- ence with methods and theories developed in semiotics and the humanities, with the ultimate aim of providing new insights into the realm of human signifi- cation and its manifestation in cultural practices.
    [Show full text]
  • Finite Semiotics: a New Theory of Semiotics with Applications to Information Technology
    FINITE SEMIOTICS: A NEW THEORY OF SEMIOTICS WITH APPLICATIONS TO INFORMATION TECHNOLOGY Cameron Shackell B. Econ., Grad. Dip. Ed., M. Litt. (App. Ling.) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy School of Electrical Engineering and Computer Science Faculty of Science and Engineering Queensland University of Technology 2019 Keywords Attention economy, Automated persuasion, Cognitive externalities, Cognitive rights, Cognitive determination, Cognitive set, Computational semiotics, Finite semiotics, Hyperreality, Informal argument, Metacognition, Natural language processing, Natural semiotic processing, Opposition analysis, Recovery function, Semioformation, Semiosic field, Semiotic valency, Semiotic vector, Semiosic oscillation, Semiotics, Semiotic theory, Technological influencing, Trans-metacognition Finite Semiotics: A New Theory Of Semiotics With Applications To Information Technology i Abstract Semiotics, often defined as the study of signs and sign processes, has not played a central role in the development of information technology. Nor has information technology been much employed to create computational tools for semiotics. This lack of engagement is surprising given that terms such as “global village” and “hyperreality” have their origins in the semiotic tradition; “encoding” and “decoding” are a shared paradigm; information and communication are fundamentally semiotic; and semiotics’ closest relative, linguistics, has helped spawn the field of Natural Language Processing. This research seeks to address this disconnect. It proposes that the arrival of the information age and its attention economy have made obvious a new theoretical possibility: that semiosis can be analysed as a function of the finiteness of human cognition and the allocation of that resource by human artefacts of the type information technology reproduces in abundance. Part One of the thesis comprises three theoretical papers accepted by Semiotica that build the proposition that cognition is finite into a new theory of finite semiotics.
    [Show full text]
  • Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing
    1 Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing William J. Rapaport Department of Computer Science and Engineering, Department of Philosophy, Department of Linguistics, and Center for Cognitive Science University at Buffalo, The State University of New York Buffalo, NY 14260-2000 [email protected] http://www.cse.buffalo.edu/~rapaport Keywords: computationalism, semiotic systems, cognition, syntax, semantics 2 Abstract In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. 1. Introduction This essay is a reply to James H. Fetzer’s essay in this journal, “Minds and Machines: Limits to Simulations of Thought and Action” (Fetzer 2011). I treat three topics: computationalism, semiotic systems, and cognition (the mind), offering what I feel is the proper treatment of computationalism. From this, certain views about semiotic systems and minds follow (or, at least, are consistent): First, I argue that computationalism should not be understood as the view that (human) cognition is computation, but that it should be understood as the view that cognition (human or otherwise) is computable (§2).
    [Show full text]
  • Information in Formation: a Peircean Approach Informação Em Formação: Uma Perspectiva Peirciana
    Information in Formation: A Peircean Approach Informação em Formação: Uma Perspectiva Peirciana André De Tienne Indiana University at Indianapolis - USA [email protected] Abstract: Two conceptions of information weave their way through Peirce’s writings. The first one emerges in 1865 in the famous formula “com- prehension x extension = information,” and tightly connects information with the newborn interpretant. The second conception emanates from Peirce’s late definition of a sign as a medium that “communicates forms,” which entails a more dynamic and processual conception of information. After discussing the potential relevance of Peirce’s contribution to information theory, I show how Peirce’s early recalculation of the two logical quantities of breadth and depth led him to define the third quantity as information. The meta-role of the interpretant is then clarified to explain what makes that third quantity special. Mihai Nadin’s conception of anticipation is then introduced and a case is made for its pertinence in understanding the telic nature of information. Once this is accomplished, I turn to Peirce’s later writings and discuss his conception of “genuine information,” before proposing an analysis of the three types of “influence” or determination that constitute information. Keywords: Information. Anticipation. Breadth. Depth. Interpretant. Proposition. Symbol. Final causation. Resumo: Duas concepções de informação entrelaçam seus caminhos pelos escritos de Peirce. A primeira surge em 1865, na famosa fórmula “compre- ensão x extensão = informação” e liga fortemente informação com o recém- nascido interpretante. A segunda concepção emana da definição peirciana tardia de signo como um meio que “comunica formas”, que acarreta uma concepção de informação mais dinâmica e processual.
    [Show full text]