When Ken Malone investigates a case of something causing mental static across the United States, he is teleported to a world that doesn't exist.

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

[Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of generalizations, (...) not just generalize correctly to individual cases; (2) The need to be able to match two or more complex short-term information structures, to enable rapid generalization from recent examples rather than from long-term memories; (3) The need to represent and reason with anomalous combinations of concepts; (4) The need to perform embedded reasoning. This presents special problems for systems using non-concatenative representations (as in mainstream connectionist approaches). We also touched on vague quantification in attitude report complements. Neither this topic nor that of analogies between short-term structures (point 2) has been adequately addressed in the symbolic framework, let alone in connectionism. -/- The opportunities and problems covered are put forward as things worth being optimistic about or pessimistic about, respectively. They are not put forward as decisive arguments for or against connectionism. The hope is that this chapter contributes to a greater understanding of the connectionist/symbolist gap by presenting some unusual issues and by throwing new light on some well known ones. (shrink)

Philosophers since the ancient Greeks have investigated the nature of different kinds of inference. Although deductive inference in the form of Aristotelian syllogisms and Fregean formal logic has predominated, much attention has also been paid to induction, inference where the conclusion does not follow necessarily from the premises.

The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science. As such, it is a basic concept of the Computational Theory of Mind, according to which cognitive states and processes are constituted by the occurrence, transformation and storage (in the mind/brain) of information-bearing structures (representations) of one kind or another.

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind with computation, (...) including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

My main concern in this work is answering the question: does parallel distributed processing as a model of the mind offer a genuine alternative to traditionalism? There has been vigorous debate within the last eight years on the subject of the relative merits of the one model over the other; however, a detailed examination of the nature of their respective differences has not been attempted. ;The mental realm is that realm in which causal interaction is governed by laws quantifying over (...) representational states. Traditionalism is the thesis that the law-governed transitions between mental states are transitions between computational states. PDP is the thesis that the transitions between mental states are transitions between distributed representational states in a PDP-type system. The representational content of a distributed state is determined by the causal history of the system as a whole, and results from the changing of system parameters via learning so as to insert this state in the causal chain between the perception of some external state-of-affairs and behavior. ;Traditionalism and PDP are best considered not as providing a detailed picture of the causal processes involved in mental activity, but rather as providing a general framework that sets broad constraints on how such law-governed transitions proceed. I describe two aspects of qualitative distinctness that can be used even when comparing such non-specific models. The first involves examining the ontological commitment of each: assuming a realist interpretation, what must exist if traditionalism is a true model of the mind? If the two models make the same commitments, one may ask the further question: do the constraints imposed on the form that mental causal transitions take allow the possibility of an isomorphism between causal sequences permitted by the one model with those permitted by the other? An examination of the manner in which representational content is determined within PDP systems shows that there is no possible isomorphism. Therefore, the two models are qualitatively distinct. (shrink)

John Searle and Roger Penrose are two staunch critics of computationalism who nonetheIess believe that with the right framework the mind can be naturalized. while they may be successful in showing the shortcomings of computationalism, I argue that their alternative noncomputational frameworks equally fail to carry out the project to naturalize the mind. The main reason is their failure to resolve some fundamental incompatibilities between mind and science. Searle tries to resolve the incompatibility between the subjectivity of consciousness and the (...) objectivity of silence by means of conceptual clarification. He, howeve4 fails to deal with the concepts crucial to this incompatibility, namely, the publicness of scientific knowledge and the privacy of psychological knowledge. Penrose tries to resolve the incompatibility between the non-computationality of psychological process and the computationality of scientific process by expanding the scope of science through some radical changes in quantum physics. His strategy, howeve4 has the danger of trivializing the distinction between science and non-science thereby putting into question the very value of the project to naturalize the mind. In addition, the feasibility of this strategy remains dubious in light of the mysteries that still surround quantum physics. (shrink)

In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.

In this thesis, I defend the explanatory force of algorithmic information processing models in cognitive neuroscience. I describe the algorithmic approach to cognitive explanation, its relation to Shea’s theory of cognitive representation, and challenges stemming from neuronal population analysis and dimensionality reduction. I then consider competing interpretations of some neuroscientific data that have been central to the debate. I argue in favor of a sequenced computational explanation of the phenomenon, contra Burnston. Finally, I argue that insights from theoretical neuroscience allow (...) us to understand why dimensionality reduction does not militate against localizing distinct contents to distinct components of functioning brain systems. (shrink)

Recent work in the methodology of connectionist explanation has I'ocrrsccl on the notion of levels of explanation. Specific issucs in conncctionisrn hcrc intersect with rvider areas of debate in the philosophy of psychology and thc philosophy of science generally. The issues I raise in this chapter, then, are not unique to cognitive science; but they arise in new and important contexts when connectionism is taken seriously as a model of cognition. The general questions are the relation between levels and the (...) status of levels which have no obvious relation to others. In speaking of levels, what is the connection, if there is one, between explanation and ontology? Which, if any, conccpt of reduction is applicable to connectionist systems? What kind of legitinrtcy can the constructs of common sense psychology, or of that vclsion ol intentional realism represented by classical symbol-systems n I, hirvc irr ir full-scale connectionist theory of mind? (shrink)

Explicatures are not cancellable. Theoretical considerations.

Variable binding has long been a challenge to connectionists. Attempts to perform variable binding using localist and distributed connectionist representations are discussed, and problems inherent in each type of representation are outlined.

Persons are biological beings who participate in social environments. Is human sociality different from that of insects? Is human sociality different from that of a computer or robot with elaborate rules for social interaction in its program memory? What is the relationship between the biology of humans and the sociality of persons? I argue that persons constitute an emergent ontological level that develops out of the biological and psychological realm, but that is largely social in its own constitution. This requires (...) a characterization of the relationships between the bio/psychological and the social, and of the developmental process of emergence. It also requires a framework for modeling the bio/psychological level that makes any such emergence possible. Neither attachment theory nor information processing frameworks, for example, will do — the major orientations toward human sociality today make understanding that sociality ultimately impossible. Only an action framework, such as that of Peirce or Piaget2, suffices. (shrink)

Riassunto: in queste note presentouna breve panoramica della scienza cognitiva, che costituisce a tutt'oggi, a più di vent'anni dalla sua nascita, un coacervo di novità rilevanti nell'ambito della ricerca interdisciplinare. Dopo una prima presentazione generale (§1), traccio una breve storia della disciplina (§2) per passare poi a descrivere nel § 3 uno dei nuclei di fondo della scienza cognitiva: il funzionalismo e l'idea di mente come sistema di rappresentazioni o mappe cognitive. Nel § 4. accenno ad alcune tensioni interne alla (...) scienza cognitiva. Essa è tuttora un campo disciplinare con forti contrasti sui paradigmi da seguire nella descrizione dell'architettura della mente. Una sfida recente è venuta dalla robotica "situata" e dalla tesi che occorre fare a meno delle rappresentazioni mentali nella costruzione di robot intelligenti. Nel § 5 discuto questa sfida mostrando che, nonostante l'entusiasmo suscitato dai primi successi della nuova robotica, il cardine della scienza cognitiva resta solido, anche se bisognoso di completamento. Nella conclusione, al § 6, tocco di sfuggita il tema della coscienza dei sistemi intelligenti (umani e artificiali) e invito a cercare strade per affrontare i problemi che si porranno alle nuove generazioni di studiosi, ormai al di là della frattura tra cultura umanistica e scientifica, frattura che forse trova in Italia uno degli ultimi ambienti in cui sopravvivere. (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)

This work explores the importance of similarity-based processes in human everyday reasoning, beyond purely rule-based processes prevalent in AI and cognitive science. A unified framework encompassing both rulebased and similarity-based reasoning may provide explanations for a variety of human reasoning data.

Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance (...) of the organization. It is suggested that the two disciplines, cognitive modeling and social simulation, which have so far been pursued in relative isolation from each other, can be profitably integrated. (shrink)

The paper critically reviews Jerry Fodor's book In Critical Condition: Polemic Essays on Cognitive Science and the Philosophy of Mind. It focuses on Fodor's compositionality arguments and their relevance to the following questions: How should concepts be individuated? What has semantics to do with epistemology? Who is right in the debate over classical and connectionist theories of cognition? How can the semantic properties of a mental state be inherited from the semantic properties of the state's constituents? The paper finally argues (...) that Fodor's opposition to functional role semantics might jeopardize his view that semantic compositionality requires appropriate constituent relations between complex and less complex concepts. (shrink)

from non-conscious components by positing that consciousness is a universal primitive. For example, the double aspect theory of information holds that infor- mation has a phenomenal aspect. How then do you get from phenomenal infor- mation to human consciousness? This paper proposes that an entity is conscious to the extent it amplifies information, first by trapping and integrating it through closure, and second by maintaining dynamics at the edge of chaos through simul- taneous processes of divergence and convergence. The origin (...) of life through autocatalytic closure, and the origin of an interconnected worldview through conceptual closure, induced phase transitions in the degree to which informa- tion, and thus consciousness, is locally amplified. Divergence and convergence of cognitive information may involve phenomena observed in light e.g. focusing, interference, and resonance. By making information flow inward- biased, clo- sure shields us from external consciousness; thus the paucity of consciousness may be an illusion. (shrink)

Selection theory requires multiple, distinct, simultaneously-actualized states. In cognition, each thought or cognitive state changes the 'selection pressure' against which the next is evaluated; they are not simultaneously selected amongst. Creative thought is more a matter of honing in a vague idea through redescribing successive iterations of it from different real or imagined perspectives; in other words, actualizing potential through exposure to different contexts. It has been proven that the mathematical description of contextual change of state introduces a non-Kolmogorovian probability (...) distribution, and a classical formalism such as selection theory cannot be used. This paper argues that creative thought evolves not through a Darwinian process, but a process of context-driven actualization of potential. (shrink)

We outline the rationale and preliminary results of using the State Context Property (SCOP) formalism, originally developed as a generalization of quantum mechanics, to describe the contextual manner in which concepts are evoked, used, and combined to generate meaning. The quantum formalism was developed to cope with problems arising in the description of (1) the measurement process, and (2) the generation of new states with new properties when particles become entangled. Similar problems arising with concepts motivated the formal treatment introduced (...) here. Concepts are viewed not as fixed representations, but entities existing in states of potentiality that require interaction with a context---a stimulus or another concept---to `collapse' to observable form as an exemplar, prototype, or other (possibly imaginary) instance. The stimulus situation plays the role of the measurement in physics, acting as context that induces a change of the cognitive state from superposition state to collapsed state. The collapsed state is more likely to consist of a conjunction of concepts for associative than analytic thought because more stimulus or concept properties take part in the collapse. We provide two contextual measures of conceptual distance---one using collapse probabilities and the other weighted properties---and show how they can be applied to conjunctions using the pet fish problem. (shrink)

The improbability of a spontaneously generated self-assembling molecule has suggested that life began with a set of simpler, collectively replicating elements, such as an enclosed autocatalytic set of polymers (or autocell). Since replication occurs without a self-assembly code, acquired characteristics are inherited. Moreover, there is no strict distinction between alive and dead; one can only infer that an autocell was alive if it replicates. These features of early life render natural selection inapplicable to the description of its change-of-state because they (...) defy its underlying assumptions. Moreover, natural selection describes only randomly generated novelty; it cannot describe the emergence of form at the interface between organism and environment. Self-organization is also inadequate because it is restricted to interactions amongst parts; it too cannot account for context-driven change. A modified version of selection theory or self-organization would not work because the description of change-of-state through interaction with an incompletely specified context has a completely different mathematical structure, i.e. entails a non-Kolmogorovian probability model. It is proposed that the evolution of early life is appropriately described as lineage transformation through context-driven actualization of potential, with self-organized change-of-state being a special case of no contextual influence, and competitive exclusion of less fit individuals through a selection-like process possibly (but not necessarily) playing a secondary role. It is argued that natural selection played an important role in evolution only after genetically mediated replication was established. (shrink)

This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the (...) functional role of consciousness on the basis of the aforementioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional role of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model and various cognitive data that it captures. © 2001 Elsevier Science B.V. All rights reserved. (shrink)

_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...) existing models of conscious-_. (shrink)

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably (...) be persons. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...) human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)

A myth has arisen concerning Turing's paper of 1936, namely that Turing set forth a fundamental principle concerning the limits of what can be computed by machine - a myth that has passed into cognitive science and the philosophy of mind, to wide and pernicious effect. This supposed principle, sometimes incorrectly termed the 'Church-Turing thesis', is the claim that the class of functions that can be computed by machines is identical to the class of functions that can be computed by (...) Turing machines. In point of fact Turing himself nowhere endorses, nor even states, this claim (nor does Church). I describe a number of notional machines, both analogue and digital, that can compute more than a universal Turing machine. These machines are exemplars of the class of _nonclassical_ computing machines. Nothing known at present rules out the possibility that machines in this class will one day be built, nor that the brain itself is such a machine. These theoretical considerations undercut a number of foundational arguments that are commonly rehearsed in cognitive science, and gesture towards a new class of cognitive models. (shrink)

In his book The Sensory Order, Hayek anticipates many of the central ideas behind what we now call the connectionist paradigm, and develops on this basis a theory of the workings of the human mind that extends the thinking of Hume and Mach. He shows that the idea of neural networks is can be applied not only in psychology and neurology but also in the sphere of economics. For the mind, from the perspective of The Sensory Order, is a dynamic, (...) relational affair that is in many respects analogous to a market process. The mind, as Hayek puts it, is a 'continuous stream of impulses, the significance of each and every contribution of which is determined by the place in the pattern of channels through which they flow' , so that the flow of representative neural impulses can be compared 'to a stock of capital being nourished by inputs and giving a continuous stream of outputs'. (shrink)

The standard approach to model how human beings understand natural languages is the symbolic, compositional approach according to which the meaning of a complex expression is a function of the meanings of its constituents. In other words, meaning plays a fundamental role in the model. In this work, because of the polysemous, flexible, dynamic, and contextual structure of natural languages, this approach is rejected. Instead, a connectionist model which eliminates the concept of meaning is proposed.

This chapter reviews some interesting research trends in the philosophy of information (PI). First, PI is defined; then, a series of open problems in PI on which philosophers are currently working is considered. The conclusion highlights the innovative character of this new area of research.

This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...) the semantics, such as inductive skepticism, the logic of discovery, Duhem’s problem, the articulation of theories by auxiliary hypotheses, the role of serendipity in scientific knowledge, Fitch’s paradox, deductive closure of knowability, whether one can know inductively that one knows inductively, whether one can know inductively that one does not know inductively, and whether expert instruction can spread common inductive knowledge—as opposed to mere, true belief—through a community of gullible pupils. (shrink)

In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms of (...) theories and their logical relations is entirely inadequate with respect to what is going on in actual biological research practice. (shrink)

The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...) suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in.

This chapter outlines a new argument for the view that language has a cognitive role. I suggest that humans exhibit two distinct kinds of belief state, one passively formed, the other actively formed. I argue that actively formed beliefs (_virtual beliefs_, as I call them) can be identified with _premising policies_, and that forming them typically involves certain linguistic operations. I conclude that natural language has at least a limited cognitive role in the formation and manipulation of virtual beliefs.

Though researchers often refer to memory as if it were a unitary phenomenon, a natural kind, the apparent heterogeneity of the various "kinds" of memory casts doubt on this default view. This paper argues, first, that kinds of memory are individuated by memory systems. It argues, second, for a view of the nature of kinds of memory informed by the tri-level hypothesis. If this approach to kinds of memory is right, then memory is not in fact a natural kind.

This paper is informed by Charles Sanders Peirce’s philosophy as semiotics or the doctrine of signs. The paper’s purpose is to explore Peirce’s category of abduction as not being limited to the inference to the best explanation. In the context of the logic of discovery, abduction is posited as a necessary although not sufficient condition for the production of meanings. The structure of a genuine sign is triadic and represents a synthesis between precognitive ideas and conceptual representations. The novel model (...) of reasoning is offered, based on the mathematical formalism borrowed from Gauss’ interpretation of the complex number. It is suggested that this model in a form of a diagram not only represents a semiotic process-structure but also overcomes the long-standing paradox of new knowledge. For Peirce, it is a diagram as a visual representation that may yield solutions to the otherwise unsolvable logical problems. What appears to us as a paradox is the very presence of abductive, or hypothetical, inference, as Peircean generic category of Firstness within the Thirdness of the total thought-process. Firstness , Secondness , and Thirdness together constitute a dynamic structure of experience. (shrink)