This article discusses work on implementing emotional and cultural models into synthetic graphical characters. An architecture, FAtiMA, implemented first in the antibullying application FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is discussed. We discuss the modelling relationships between culture, social interaction, and cognitive appraisal. Integrating a lower level homeostatically based model is also considered as a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is summarised and future directions (...) discussed. (shrink)

Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an `abstract' discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in (...) a theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process. (shrink)

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling (...) linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling. (shrink)

Rosen's modelling relations constitute a conceptual schema for the understanding of the bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems used in this study refers to information structures constructed as algebraic rings of observable attributes of natural systems, in which the notion of observable signifies a physical attribute that, in principle, can be measured. Due to the fact that modelling relations are bidirectional by construction, they admit a precise categorical formulation in (...) terms of the category-theoretic syntactic language of adjoint functors, representing the inverse processes of information encoding/decoding via adjunctions. As an application, we construct a topological modelling schema of complex systems. The crucial distinguishing requirement between simple and complex systems in this schema is reflected with respect to their rings of observables by the property of global commutativity. The global information structure representing the behaviour of a complex system is modelled functorially in terms of its spectrum functor. An exact modelling relation is obtained by means of a complex encoding/decoding adjunction restricted to an equivalence between the category of complex information structures and the category of sheaves over a base category of partial or local information carriers equipped with an appropriate topology. (shrink)

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) (...) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. (shrink)

I will present the rationale followed for the conceptualization and the following development the Dual PECCS system that relies on the cognitively grounded heterogeneous proxytypes representational hypothesis. Such hypothesis allows integrating exemplars and prototype theories of categorization and has provided useful insights in the context of cognitive modelling for what concerns the typicality effects in categorization. As argued in [Chella et al., 2017] [Lieto et al., 2018b] [Lieto et al., 2018a] a pivotal role in this respect is played by the (...) use of the conceptual spaces framework and by its integration with a symbolic knowledge representation layer. (shrink)

In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an alternative (...) account to the deflationary view – the Hybrid Account; and we defend our account in contrast with deflationism. (shrink)

The target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.

Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and (...) is not a symbol system. A hybrid nonsymbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system's capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analog sensory projection on which successful categorization is based. "Categorical perception" (Harnad 1987a), in which similarity space is "warped" in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analog world of objects on the formal world of symbols. (shrink)

As is well known, some paradoxes arise through inadequate analysis of the meanings of terms in a language, an adequate analysis showing that the paradoxes arise through a lack of separation of an object theory and a metatheory. Under such an adequate analysis in which parts of the metatheory are modelled in the object theory, the paradoxes give way to remarkable theorems establishing limitations of the object theory.Such a modelling is often accomplished by a Gödel numbering. Here we shall use (...) a somewhat different technique in axiomatic set theory, from which we shall reap a few results having the effect of comparing the strength of various axiom schema of comprehension for sets and classes. Similar results were obtained by A. Mostowski [7] using Gödel numbering. (shrink)

In the view of my philosophical position “nominal conceptualism”, cognitive/knowledge agents, who are in some way aware of expressing the world based on their mental concepts, deal with their linguistic and/or symbolic expressions. In this paper I rely on nominal conceptualism to logically characterise agents’ concept-based descriptions of the world and analyse a fundamental logical system for conception representation.

The organization of the complex mixed-mode oscillations generated, in a three-dimensional variable space, by an autocatalytic process formalized as a cubic monomial is analyzed. The generation of the temporal patterns is elucidated by complementary approaches dealing with the three-variable differential continuous system itself and with successive discrete applications modelling its first return map. The extent to which the underlying bifurcation structures could constitute a fingerprint of autocatalytic processes is discussed in connection with the modelling of biological systems.

In silico medicine is still forging a road for itself in the current biomedical landscape. Discursively and rhetorically, it is using a three-way positioning, first, deploying discourses of personalised medicine, second, extending the 3Rs from animal to clinical research, and third, aligning its methods with experimental methods. The discursive and rhetorical positioning in promotions and statements of the programme gives us insight into the sociability of the scientific labour of advancing the programme. Its progress depends on complex social, institutional and (...) technological conditions which are not external to its epistemology, but intricately interwoven with it. This article sets out to show that this is the case through an analysis of the process of computational modelling that is at the core of its epistemology. In this paper I show that the very notion of ‘model’ needs to be re-thought for in silico medicine, and propose a replacement, in the form of the ‘Model-Simulation-Experiment-System’ or MSE-system, which is simultaneously an epistemological, social and technological system. I argue that the MSE-system is radically mediated by social relations, technologies and symbolic systems. We need now to understand how such mediations operate effectively in the construction of robust MSE-systems. (shrink)

The contribution of data use in schools has been proven via visible changes in policy and practice in schools, changes in practitioners learning or cognition and changes in opinions or attitudes regarding teaching or policy-making. Nevertheless, limited research is available on the extent to which data use in schools results in the aforementioned effects and how they can be explained by data use expectations and collaboration. This paper addresses both issues by describing and explaining data use effects via a large-scale (...) study in Flanders. Data collected from 1472 teachers indicate that, although teachers are moderately positive about the extent to which data use results in different types of effects, data use effects cannot be taken for granted. Structural Equation Modelling shows that explicating data use expectations and data use collaboration are essential in order to facilitate data use effects in schools. (shrink)

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in (...) dynamic normative contexts. (shrink)

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in (...) dynamic normative contexts. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, (...) be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

In which respects do modeling and experimenting resemble or differ from each other? We explore this question through studying in detail the combinatorial strategy in synthetic biology whereby scientists triangulate experimentation on model organisms, mathematical modeling, and synthetic modeling. We argue that this combinatorial strategy is due to the characteristic constraints of the three epistemic activities. Moreover, our case study shows that in some cases materiality clearly matters, in fact it provides the very rationale of synthetic (...) class='Hi'>modeling. We will show how the materialities of the different kinds of models – biological components versus mathematical symbols – in combination with their different structures – the complexity of biological organisms versus the isolated network structure and its mathematical dynamics - define the spectrum of epistemic possibilities in synthetic biology. Furthermore, our case shows that from the perspective of scientific practice the question of whether or not simulations are like or unlike experiments is often beside the point, since they are used to accomplish different kinds of things. (shrink)

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...) for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O); Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; Ubiquitous predicates model the varying degrees to which items may suggest alignment; Structural evaluation of analogical inferences models aspects of plausibility judgments; Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction (...) with their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification (...) has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns and (...) transfers its learning across problems, it also accounts for the faster problem‐solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition‐based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem‐solving episodes. (shrink)

The well-known game of chess has traditionally been modeled in artificial intelligence studies by search engines with advanced pruning techniques. The models were thus centered on an inference engine manipulating passive symbols in the form of tokens. It is beyond doubt, however, that human players do not carry out such processes. Instead, chess masters instead carry out perceptual processes, carefully categorizing the chunks perceived in a position and gradually building complex dynamic structures to represent the subtle pressures embedded in the (...) positions. In this paper we will consider two hypotheses concerning the underlying subcognitive processes and architecture. In the first hypothesis, a multiple-leveled chess representational structure is presented, which includes distance graphs (with varying levels of quality) between pieces, piece mobilities, and abstract roles. These representational schemes seem to account for numerous characteristics of human player’s psychology. The second hypothesis concerns the extension of the architecture proposed in the Copycat project as central for modeling the emergent intuitive perception of a chess position. We provide a synthesis on how the postulated architecture models chess intuition as an emergent mixture of simultaneous distance estimations, chunk perceptions, abstract role awareness, and intention activations. This is an alternative model to the traditional AI approaches, focusing on the philosophy of active symbols. (shrink)

Two decades of attempts to model the emergence of language as a collective cognitive activity have demonstrated a number of principles that might have been part of the historical process that led to language. Several models have demonstrated the emergence of structure in a symbolic medium, but none has demonstrated the emergence of the capacity for symbolic representation. The current shift in cognitive science toward theoretical frameworks based on embodiment is already furnishing computational models with additional mechanisms relevant (...) to the emergence of symbolic language. An analysis of embodied interaction among captive, but not human‐enculturated, bonobo chimpanzees reveals a number of additional features of embodiment that are relevant to the emergence of symbolic language, but that have not yet been explored in computational simulation models; for example, complementarity of action in addition to imitation, iconic in addition to indexical gesture, coordination among multiple sensory and perceptual modalities, and the orchestration of intra‐ and inter‐individual motor coordination. The bonobos provide an evolutionarily plausible intermediate stage in the development of symbolic expression that can inform efforts to model the emergence of symbolic language. (shrink)

Symbols can be used to mask or embellish firms’ exploitative labor practices. The present study defines exploitative firms’ abuse of symbolic management using legitimate symbolic terminologies to embellish their demanding working conditions as symbolic framing and examines it in the Japanese context. Because of strong social criticism for exploitative practices, firms are under pressure to avoid giving an exploitative impression to stakeholders, particularly job seekers in recruitment. This study argues that exploitative firms respond to these pressures by (...) embellishing their descriptions of the hard-working conditions at the firm, using certain symbolic expressions. To test this argument, this study examined recruitment advertising of 1238 Japanese firms between 2006 and 2017 and analyzed their ideal candidate profile (ICP) statement—a description of personal attributes that align with the firms’ working conditions—based on topic modeling. The results showed that exploitative firms tend to frame their working conditions in the ICP statement as ‘challenging work,’ which acknowledges the demanding work environment but frames it positively. This tendency was strengthened for firms with younger employees. The results provide the current literature with new insights regarding the dark side of symbolic management. (shrink)

To analyze the task of mental arithmetic with external representations in different number systems we model algorithms for addition and multiplication with Arabic and Roman numerals. This demonstrates that Roman numerals are not only informationally equivalent to Arabic ones but also computationally similar—a claim that is widely disputed. An analysis of our models' elementary processing steps reveals intricate tradeoffs between problem representation, algorithm, and interactive resources. Our simulations allow for a more nuanced view of the received wisdom on Roman numerals. (...) While symbolic computation with Roman numerals requires fewer internal resources than with Arabic ones, the large number of needed symbols inflates the number of external processing steps. (shrink)

We develop a general covariant categorical modeling theory of natural systems’ behavior based on the fundamental functorial processes of representation and localization-globalization. In the first part of this study we analyze the process of representation. Representation constitutes a categorical modeling relation that signifies the semantic bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems is substantiated by algebraic rings of observable attributes of natural systems. In this perspective, the distinction between (...) simple and complex systems is reflected in the appropriate qualification of their corresponding rings of observables. The crucial distinguishing requirement with respect to the coordinatizing rings of observables has to do with the property of global commutativity. The global information structure representing the behavior of a complex system is modeled functorially in terms of its Spectrum functor. Due to the fact that, the fundamental process of representation is bidirectional by construction, it admits a precise categorical formulation in terms of the syntactic language of adjoint functors, constituting thus, a categorical adjunction. The left adjoint functor of this adjunction signifies the process of encoding the information related with phenomena of natural systems in terms of coordinatizing rings of observables, whereas, the right adjoint functor signifies the inverse process of information decoding, which, can be used for making predictions about the behavior of natural systems. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

1.1 The predominant approach to cognitive modeling is still what has come to be called "computationalism" (Dietrich 1990, Harnad 1990b), the hypothesis that cognition is computation. The more recent rival approach is "connectionism" (Hanson & Burr 1990, McClelland & Rumelhart 1986), the hypothesis that cognition is a dynamic pattern of connections and activations in a "neural net." Are computationalism and connectionism really deeply different from one another, and if so, should they compete for cognitive hegemony, or should they collaborate? (...) These questions will be addressed here, in the context of an obstacle that is faced by computationalism (as well as by connectionism if it is either computational or seeks cognitive hegemony on its own): The symbol grounding problem (Harnad 1990). (shrink)

We study the logical structure of relations, and in particular the notion of occurrences of objects in a state. We start with formulating a number of principles for occurrences and defining corresponding mathematical models. These models are analyzed to get more insight in the formal properties of occurrences. In particular, we prove uniqueness results that tell us more about the possible logical structures relations might have.

Ample research documents the health benefits of community gardens, but our understanding of the factors shaping gardener participation is limited. Neighborhood demographics and garden frames have each been theorized to play a role in shaping who participates in community gardens. Yet, our understanding of the interplay between these factors is underdeveloped and this body of work lacks consideration of the racial and class makeup of gardeners on a large scale. With a nation-wide survey that includes measures of gardener demographics (N (...) = 162), the present study considers the extent to which community garden frames and locations simultaneously shape participant demographics. I combine these factors into a conceptual model explaining community garden participation as an iterative process of framing, accessibility, and representation, all situated within a garden’s surrounding community. Results show some base correlations between gardens focusing on healthy food access or symbolic food labels and gardener demographics, but ordered logistic and negative binomial regressions show stronger evidence of community demographics shaping gardener demographics. At the same time, _t_-tests comparing mean neighborhood and gardener demographics shows a consistent under-representation of Latinx community members among gardeners. As theorized in the model presented, community garden locations are important for shaping what demographics are represented among gardeners, but how community garden benefits are framed can limit garden accessibility, and subsequently neighborhood representation, especially for Latinx residents. This model helps illustrate the mechanisms through which garden organizers and advocates can develop more inclusive community gardens through fostering representation from people of color and the working-class. (shrink)

This paper presents and evaluates a model of how the abstractness of source and target concepts influences metaphoricity, the property of how metaphoric a linguistic metaphoric expression is. The purpose of this is to investigate the long-standing claim that metaphoric mappings are from less abstract concepts to more abstract concepts. First, abstractness is modeled using Searle’s social ontology and this model of abstractness evaluated using a participant-based measure of abstractness. Second, this model of abstractness is used to determine the direction (...) and distance in abstractness of metaphoric mappings. Direction of abstractness in these mappings is whether the source or target concept is the more abstract. Distance is the difference in abstractness between source and target concepts. Both factors influence metaphoricity, as measured using introspection and a participant-based study of metaphoricity. The results give empirical support for a long-standing claim about abstractness in mappings, but also.. (shrink)

In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by analyzing from a (...) semiotic point of view the representational relation which is characteristic of models. We then corroborate our hypothesis (2) by discussing the conceptual differences between icons, i.e. models, and indexical and symbolic signs and (3) by putting forward a general classification of all icons into three functional subclasses (images, diagrams, and metaphors). Subsequently, we (4) integratively refine our results by resorting to two influential and, as can be shown, complementary philosophy of science approaches to models. This yields the following result: models are determined by a semiotic structure in which a subject intentionally uses an object, i.e. the model, as a sign for another object, i.e. the original, in the context of a chosen theory or language in order to attain a specific end by instituting a representational relation in which the syntactic structure of the model, i.e. its attributes and relations, represents by way of a mapping the properties of the original, which hence are regarded as similar in a relevant manner. (shrink)

In previous work, I have looked in detail at the capacity and the limits of the linguistics model as applied to gene expression. The recent use of a primitive applied linguistic model in Apple's SIRI system allows further analysis. In particular, the failings of this system resemble those of the HGP; the model used also helps point out the shortcomings of the concept of the "gene". This is particularly urgent as we are entering an era of applied biology in the (...) absence of theory, and indeed an era with a near-epidemic of retracted papers. There are a few workarounds proposed. One is to add to the nascent field of biosemiotics a more explicit concern with syntax. At the time of writing, Apple is being sued for false advertising of its iphone 4s, with the associated claim that apple had solved many of the problems of natural language processing by computer . The system was bought by Apple from a company called SIRI, and in turn was based on the notion, trumpeted by the prior art in a company called Dejima, that nlpbc could be done by keywords alone. Yet the hype resembles nothing so much as the misrepresentation of the Human Genome Project fed to the media in the glory days at the beginning of this millennium, and it says a lot for the status of scientists in society that they have avoided Apple's fate. In this paper, a short review of several current themes in theoretical and applied biology is first proposed. Then the tensions implicit in the notion that the "gene" is simultaneously to be identified as a unit of inheritance and spatially located over spatially well-defined nucleotides is explored and the notion is found to be incoherent. An expanded notion of inheritance is proposed in the context of a focus on inheritance as necessarily involving species, population and organism over time. While it is premature to talk about a paradigm shift, it is certainly arguable that biology urgently needs a sophisticated theory of how symbols work substantially more sophisticated than that implicit in the HGP; Biosemiotics affords a framework in which this might be tried. Indeed, as this paper concludes, there may yet be room for a "Bionoetics", a perspective in which biological explanation can be extended to include cognition in all its forms. Finally, a working sketch of a modeling environment written in LISP, one that shows promise in reflecting the complexities discussed in the paper, is included. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}. (shrink)

The thesis deals with the concept of truth and the paradoxes of truth. Philosophical theories usually consider the concept of truth from a wider perspective. They are concerned with questions such as - Is there any connection between the truth and the world? And, if there is - What is the nature of the connection? Contrary to these theories, this analysis is of a logical nature. It deals with the internal semantic structure of language, the mutual semantic connection of sentences, (...) above all the connection of sentences that speak about the truth of other sentences and sentences whose truth they speak about. Truth paradoxes show that there is a problem in our basic understanding of the language meaning and they are a test for any proposed solution. It is important to make a distinction between the normative and analytical aspect of the solution. The former tries to ensure that paradoxes will not emerge. The latter tries to explain paradoxes. Of course, the practical aspect of the solution is also important. It tries to ensure a good framework for logical foundations of knowledge, for related problems in Artificial Intelligence and for the analysis of the natural language. Tarski’s analysis emphasized the T-scheme as the basic intuitive principle for the concept of truth, but it also showed its inconsistency with the classical logic. Tarski’s solution is to preserve the classical logic and to restrict the scheme: we can talk about the truth of sentences of a language only inside another essentially richer metalanguage. This solution is in harmony with the idea of reflexivity of thinking and it has become very fertile for mathematics and science in general. But it has normative nature | truth paradoxes are avoided in a way that in such frame we cannot even express paradoxical sentences. It is also too restrictive because, for the same reason we cannot express a situation in which there is a circular reference of some sentences to other sentences, no matter how common and harmless such a situation may be. Kripke showed that there is no natural restriction to the T-scheme and we have to accept it. But then we must also accept the riskiness of sentences | the possibility that under some circumstances a sentence does not have the classical truth value but it is undetermined. This leads to languages with three-valued semantics. Kripke did not give any definite model, but he gave a theoretical frame for investigations of various models | each fixed point in each three-valued semantics can be a model for the concept of truth. The solutions also have normative nature | we can express the paradoxical sentences, but we escape a contradiction by declaring them undetermined. Such a solution could become an analytical solution only if we provide the analysis that would show in a substantial way that it is the solution that models the concept of truth. Kripke took some steps in the direction of finding an analytical solution. He preferred the strong Kleene three-valued semantics for which he wrote it was "appropriate" but did not explain why it was appropriate. One reason for such a choice is probably that Kripke finds paradoxical sentences meaningful. This eliminates the weak Kleene three valued semantics which corresponds to the idea that paradoxical sentences are meaningless, and thus indeterminate. Another reason could be that the strong Kleene three valued semantics has the so-called investigative interpretation. According to this interpretation, this semantics corresponds to the classical determination of truth, whereby all sentences that do not have an already determined value are temporarily considered indeterminate. When we determine the truth value of these sentences, then we can also determine the truth value of the sentences that are composed of them. Kripke supplemented this investigative interpretation with an intuition about learning the concept of truth. That intuition deals with how we can teach someone who is a competent user of an initial language (without the predicate of truth T) to use sentences that contain the predicate T. That person knows which sentences of the initial language are true and which are not. We give her the rule to assign the T attribute to the former and deny that attribute to the latter. In that way, some new sentences that contain the predicate of truth, and which were indeterminate until then, become determinate. So the person gets a new set of true and false sentences with which he continues the procedure. This intuition leads directly to the smallest fixed point of strong Kleene semantics as an analytically acceptable model for the logical notion of truth. However, since this process is usually saturated only on some transfinite ordinal, this intuition, by climbing on ordinals, increasingly becomes a metaphor. This thesis is an attempt to give an analytical solution to truth paradoxes. It gives an analysis of why and how some sentences lack the classical truth value. The starting point is basic intuition according to which paradoxical sentences are meaningful (because we understand what they are talking about well, moreover we use it for determining their truth values), but they witness the failure of the classical procedure of determining their truth value in some "extreme" circumstances. Paradoxes emerge because the classical procedure of the truth value determination does not always give a classically supposed (and expected) answer. The analysis shows that such an assumption is an unjustified generalization from common situations to all situations. We can accept the classical procedure of the truth value determination and consequently the internal semantic structure of the language, but we must reject the universality of the exterior assumption of a successful ending of the procedure. The consciousness of this transforms paradoxes to normal situations inherent to the classical procedure. Some sentences, although meaningful, when we evaluate them according to the classical truth conditions, the classical conditions do not assign them a unique value. We can assign to them the third value, \undetermined", as a sign of definitive failure of the classical procedure. An analysis of the propagation of the failure in the structure of sentences gives exactly the strong Kleene three-valued semantics, not as an investigative procedure, as it occurs in Kripke, but as the classical truth determination procedure accompanied by the propagation of its own failure. An analysis of the circularities in the determination of the classical truth value gives the criterion of when the classical procedure succeeds and when it fails, when the sentences will have the classical truth value and when they will not. It turns out that the truth values of sentences thus obtained give exactly the largest intrinsic fixed point of the strong Kleene three-valued semantics. In that way, the argumentation is given for that choice among all fixed points of all monotone three-valued semantics for the model of the logical concept of truth. An immediate mathematical description of the fixed point is given, too. It has also been shown how this language can be semantically completed to the classical language which in many respects appears a natural completion of the process of thinking about the truth values of the sentences of a given language. Thus the final model is a language that has one interpretation and two systems of sentence truth evaluation, primary and final evaluation. The language through the symbol T speaks of its primary truth valuation, which is precisely the largest intrinsic fixed point of the strong Kleene three valued semantics. Its final truth valuation is the semantic completion of the first in such a way that all sentences that are not true in the primary valuation are false in the final valuation. (shrink)

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features (...) from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)

. Fuzzy systems and artificial neural networks, as important components of soft-computation, can be applied together to model uncertainty. A composite block of the fuzzy system and the ANN shares a mutually beneficial association resulting in enhanced performance with smaller networks. It makes them suitable for application with time-varying multi-input multi-output channel modeling enabling such a system to track minute variations in propagation conditions. Here we propose a fuzzy neural system using a fuzzy time delay fully recurrent neural network (...) that has the capability to tackle time-varying inputs in fuzzified form and is used to model MIMO channels. The inference engine is constituted by novel FTDFRNN blocks which determine the decision boundaries and tracks in-phase and quadrature components of input signals encompassing stochastic behavior of the MIMO channel. The system shows significant improvement in performance compared to statistical and ANN approaches in terms of faster processing time, lower bit error rate margins and better precision while carrying out symbol recovery of transmitted data through severely faded MIMO channels. (shrink)

This book constitutes the thoroughly refereed post-proceedings of the International Conference on Artificial Intelligence and Symbolic Computation, AISC 2000, held in Madrid, Spain in July 2000. The 17 revised full papers presented together with three invited papers were carefully reviewed and revised for inclusion in the book. Among the topics addressed are automated theorem proving, logical reasoning, mathematical modeling of multi-agent systems, expert systems and machine learning, computational mathematics, engineering, and industrial applications.