There is today much interest in research of neuronal substrata in metaphor processing. It has been suggested that the right hemisphere yields a key role in the comprehension of figurative language and, particularly, in metaphors. Figurative language is included in pragmatics, a branch of linguistics that researches the use of language, in opposition to the study of the system of language. There lingers, though, an open debate in respect to the identification of the specific aspects concerning semantics, as opposed to (...) those dominated by pragmatics. Can studies from neuronal correlates clarify questions that relate to semantics/pragmatics representation? I shall analyze neuroscientific developments about implicit language to attempt to understand, in section 2, scientific techniques available and more suitable to the phenomenology of the act of understanding an implicit, figurative or implicated message in a certain language game. To do so, I shall start by reviewing the studies in philosophy of language, and accommodate the development of the research in pragmatics underlying metaphor, particularly, in Philosophical Investigations by Wittgenstein. Finally, I discuss the possibility of interpretative capabilities being socioculturally grounded. I expect this methodological analysis to contribute to the enlightenment of the problem of phenomenology of intersubjective pragmatics, and to its future experimental paradigms. (shrink)

Keywords: climate change, integrated assessment, knowledge integration, transdisciplinary research, vulnerability, vulnerability assessment. This thesis explores how transdisciplinary knowledge integration can be facilitated in the context of integrated assessments and vulnerability assessments of climate change. Even though knowledge integration is fundamental in such transdisciplinary assessments, the actual process of integrating knowledge is rarely addressed explicitly and methodically. Here, knowledge integration is conceptualised into the subsequent phases of the elaboration of a shared language and the design of a methodology. Three devices for (...) facilitating knowledge integration are put forward: 1. semantic ascent or the shift from speaking in a language to speaking in a meta-language about the former, 2. formalisation or the translation of statements made in ordinary or technical language into a formal language, and 3. knowledge integration methods, which are methods that provide a meta-language for speaking about the knowledge to be integrated and organise the process of integration. Four cases of knowledge integration are presented. First, the general problem of methodology design is addressed and a graphical framework for representing methodologies is presented. Second, the problem of developing a shared language for speaking about vulnerability to climate change is addressed. A formal mathematical framework of vulnerability and related concepts is presented. Third, a special case of methodology design, the integration of computer models in the context of modular integrated assessment modelling is addressed. A modular approach developed in the PIAM project is presented. Fourth, the integration of computer models, this time in the context of a global assessment of coastal vulnerability to sea-level rise, is addressed. A knowledge integration method, which was developed and applied in the DINAS-COAST project, is presented. These cases show that semantic ascent is a useful device in those cases in which it is difficult to directly elaborate a shared language at the beginning of the assessment. Formalisation can contribute to the elaboration of a shared language in those cases in which concepts overlap non trivially in their meanings. More emphasis should be placed on the development and application of iterative knowledge integration methods as iteration is crucial in order to benefit from the mutual learning during the course of the assessment. (shrink)

This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements (...) that are met by different sorts of minds, i.e. the niches that they occupy, (b) understanding the space of possible designs, and (c) understanding complex and varied relationships between requirements and designs. Attempts to model or explain any particular phenomenon, such as vision, emotion, learning, language use, or consciousness lead to muddle and confusion unless they are placed in that broader context. A methodology for making progress is summarised and a novel requirement proposed for a theory of how human minds work: the theory should support a single generic design for a learning, developing system that, in addition to meeting familiar requirements, should be capable of developing different and opposed philosophical viewpoints about consciousness, and the so-called hard problem. In other words, we need a common explanation for the mental machinations of mysterians, materialists, functionalists, identity theorists, and those who regard all such theories as attempting to answer incoherent questions. No designs proposed so far come close. (shrink)

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

Each of us is uniquely subject to certain kinds of stimulation from a small part of the universe within our skins. Mentalistic psychologies insist that other kinds of events, lacking the physical dimensions of stimuli, are accessible to the owner of the skin within which they occur. One solution often regarded as behavioristic, granting the distinction between public and private events and ruling the latter out of consideration, has not been successful. A science of behavior must face the problem of (...) privacy by dealing with events within the skin in their relation to behavior, without assuming they have a special nature or must be known in a special way. (shrink)

The information age can be dated to the work of Norbert Wiener and Claude Shannon in the 1940s. Their work on cybernetics and information theory, and many subsequent developments, had a profound influence on reshaping the field of psychology from what it was prior to the 1950s. Contemporaneously, advances also occurred in experimental design and inferential statistical testing stemming from the work of Ronald Fisher, Jerzy Neyman, and Egon Pearson. These interdisciplinary advances from outside of psychology provided the conceptual and (...) methodological tools for what is often called the cognitive revolution but is more accurately described as the information-processing revolution. Cybernetics set the stage with the idea that everything ranging from neurophysiological mechanisms to societal activities can be modeled as structured control systems with feedforward and feedback loops. Information theory offered a way to quantify entropy and information, and promoted theorizing in terms of information flow. Statistical theory provided means for making scientific inferences from the results of controlled experiments and for conceptualizing human decision making. With those three pillars, a cognitive psychology adapted to the information age evolved. The growth of technology in the information age has resulted in human lives being increasingly interweaved with the cyber environment, making cognitive psychology an essential part of interdisciplinary research on such interweaving. Continued engagement in interdisciplinary research at the forefront of technology development provides a chance for psychologists not only to refine their theories but also to play a major role in the advent of a new age of science. (shrink)

This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems for natural-language representation and reasoning systems, because--unlike pure indicators--they cannot be replaced by coreferential NPs without changing the meaning of the (...) embedding sentence. Therefore, the referent of the quasi-indicator must be represented in such a way that no invalid coreferential claims are entailed. The importance of quasi-indicators is discussed, and it is shown that all four of the above categories of belief reports can be handled by a single representational technique using belief spaces containing intensional entities. Inference rules and belief-revision techniques for the system are also examined. (shrink)

SummaryJ.R. Lucas has argued that it follows from Godel's Theorem that the mind cannot be a machine or represented by any formal system. Although this notorious argument against the mechanism thesis has received considerable attention in the literature, it has not been decisively rebutted, even though mechanism is generally thought to be the only plausible view of the mind. In this paper I offer an analysis of Lucas's argument which shows that it derives its persuasiveness from a subtle confusion. In (...) particular, I examine Benacerraf's reconstruction of Lucas's argument, thereby permitting a precise re‐statement of my own analysis and reinforcement of my conclusions regarding Lucas's mistake. This examination of Benacerraf's reconstruction permits us to evaluate certain radical and highly paradoxical conclusions concerning empirical psychology which Benacerraf draws, and this, in turn, permits us to consider certain suggestive implications for the mind which may, after all, follow from Godel's Theorem.RésuméJ.R. Lucas a dérivé du théorème de Gödel la conclusion que ľesprit ne peut pas être une machine ou être représenté par un systeme formel. Bien que cet argument célèbre contre la thése du mécanisme ait éveillé un interêt considérable dans la littérature, il ňa pas été réfuté de façon décisive, même si ľopinion générate prévaut que le mécanisme est la seule conception possible de ľesprit. Dans cet article, je propose une analyse du raisonnement de Lucas qui montre que sa force persuasive provient ?on;une subtile confusion. En particulier, j'examine la reconstruction, par Benacerraf, de la démonstration de Lucas, ce qui me pérmet de préciser ma propre analyse et de confirmer mes conclusions quant àľerreur de Lucas. Cet examen de la reconstruction de Benacerraf nous fournit ľoccasion de discuter certaines conclusions radicales et hautement paradoxals qu'il en tire concernant la psychologie expérimentale et nous conduit à certaines implications suggestives concernant ľesprit et qui pourraient finalement decouler du théorème de Gödel.ZusammenfassungJ.R. Lucas hat argumentiert, aus Gödels Theorem mUsse folgen, dass der menschliche Geist keine Maschine sein oder durch ein formales System dargestellt werden kann. Obschon dieses bekannte Argument gegen die mechanistische These in der Literatur beträchtliche Beachtung gefunden hat, ist es bisher noch nie eigentlich widerlegt worden, obgleich eine mechanistische Betrachtungsweise allgemein als die einzig plausible gilt. In dieser Arbeit biete ich eine Analyse von Lucas Argument an, die zeigen soil, dass seine Überzeugungskraft auf einer subtilen Konfusion beruht. Im besonderen untersuche ich Benacerrafs Rekonstruktion des Arguments, was rnirerlaubt, meine eigene Analyse in genauer Weise zu formulieren und meine Schlussfolgerung bezüglich des von Lucas gemachten Fehlers zu erharten. Diese Untersuchung von Benacerrafs Rekonstruktion ermöglicht es, gewisse radikale und im höchsten Grad paradoxe Schlüsse, die Benacerraf hinsichtlich der empirischen Psychologie zieht, zu beurteilen. Das wiederum erlaubt uns, gewisse den Geist betreffende Implikationen zu erörtern, die tatsächlich aus Gödels Theorem folgen kUnnten. (shrink)

Central to contemporary cognitive science is the notion that mental processes involve computations defined over internal representations. This view stands in sharp contrast to the to visual perception and cognition, whose most prominent proponent has been J.J. Gibson. In the direct theory, perception does not involve computations of any sort; it is the result of the direct pickup of available information. The publication of Gibson's recent book (Gibson 1979) offers an opportunity to examine his approach, and, more generally, to contrast (...) the theory of direct perception with the computational/representational view. In the first part of the present article (Sections 2direct perceptioncase study”: the problem of perceiving the three-dimensional shape of moving objects is examined. This problem, which has been extensively studied within the immediate perception framework, serves to illustrate some of the inherent shortcomings of that approach. Finally, in Section 5, an attempt is made to place the theory of direct perception in perspective by embedding it in a more comprehensive framework. (shrink)

This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory (...) and, with the help of evolutionary biology and neurophysiology, to attempt a characterization of the adaptive processes involved in visual perception. Visual representations are explained as patterns of cortical activity that are enabled to focus on objects in the changing visual environment by constantly adjusting to maintain levels of mutual information between pattern and object that are adequate for continuing perceptual control. In these terms, the answer proposed to (1) is that the intentional functions of vision are those involved in the establishment and maintenance of such representations, and to (2) that semantic features are added to the information processes of vision with the focus on objects that these representations accomplish. The article concludes with proposals for extending this account of intentionality to the higher domains of conceptualization and reason, and with speculation about how semantic information-processing might be achieved in mechanical systems. (shrink)

Artificially intelligent machines are different in kind from all previous machines and tools. While many are used for relatively benign purposes, the types of artificially intelligent machines that we should care about, the ones that are worth focusing on, are the machines that purport to replace humans entirely and thereby engage in what Brian Cantwell Smith calls “judgment.” As impressive as artificially intelligent machines are, their abilities are still derived from humans and as such lack the sort of normative commitments (...) that humans have. So while artificially intelligent machines possess a great capacity for “reckoning,” to use Smith’s terminology, i.e., a calculative prowess of extraordinary utility and importance, they still lack the kind of considered human judgment that accompanies the ethical commitment and responsible action we humans must ultimately aspire toward. But there is a perfect technological storm brewing. Artificially intelligent machines are analogous to a perfect storm in that such machines involve the convergence of a number of factors that threaten our ability to behave ethically and maintain meaningful human control over the outcomes of processes involving artificial intelligence. I argue that the storm in the context of artificially intelligent machines makes us vulnerable to moral complacency. That is, this perfect technological storm is capable of lulling people into a state in which they abdicate responsibility for decision-making and behaviour precipitated by the use of artificially intelligent machines, a state that I am calling “moral complacency.” I focus on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control. The first problem is that of transparency/opacity. The second problem is that of overtrust in machines, often referred to as the automation bias. The third problem is that of ascribing responsibility. I examine each of these problems and how together they threaten to render us morally complacent. (shrink)

This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO. The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment. Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents. CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases. The computational modelhelps students test legal hypotheses againsta database of (...) legal cases, draws analogiesto problem scenarios from the database, andcomposes arguments by analogy with a setof argument moves. The CATO model accountsfor a number of the important featuresof the jurisprudential accounts, includingimplementing a kind of reflective adjustment.It also avoids some of the problems identifiedin the critique; for instance, it deals withweights in a non-numeric, context-sensitivemanner. The article concludes by describingthe contributions AI research can make tojurisprudential investigations of complexcognitive phenomena of legal reasoning. Forinstance, unlike the jurisprudential models,CATO provides a detailed account of how togenerate multiple interpretations of a citedcase, downplaying or emphasizing the legalsignificance of distinctions in terms of thepurposes of the law as the argument contextdemands. (shrink)

Within the strong branch of artificial intelligence, which is aimed at creating thinking machines with intellectual powers like those of man, the most explored research program is symbolic aI, defined as the attempt to use electronic computers to replicate the human mind, either assuming a structural and functional similarity between them, or trying to replicate the behavior produced by the human mind through computational processes that also have an intentional structure but are only instrumentally equivalent. In this paper we show (...) that strong symbolic aI, in either of its two variants, is impossible, since formal systems, that is what all computer programs ultimately are, are not sufficient to replicate in an intentional way four intellectual faculties of man that are essential to intelligent behavior: sense of the situation, common sense, procedural skills and theory abduction. (shrink)

The term ‘the artificial’ can only be given a precise meaning in the context of the evolution of computational technology and this in turn can only be fully understood within a cultural setting that includes an epistemological perspective. The argument is illustrated in two case studies from the history of computational machinery: the first calculating machines and the first programmable computers. In the early years of electronic computers, the dominant form of computing was data processing which was a reflection of (...) the dominant philosophy of logical positivism. By contrast, artificial intelligence (AI) adopted an anti-positivist position which left it marginalised until the 1980s when two camps emerged: technical AI which reverted to positivism, and strong AI which reified intelligence. Strong AI's commitment to the computer as a symbol processing machine and its use of models links it to late-modernism. The more directly experiential Virtual Reality (VR) more closely reflects the contemporary cultural climate of postmodernism. It is VR, rather than AI, that is more likely to form the basis of a culture of the artificial. (shrink)

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote (...) the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)

Summary The idea of man imitating by his own inventions capacities, considered genuinely human, like talking, making music or thinking, has fascinated man's phantasy at all times.