Cognitive dissonance has been studied for more than sixty years and many insightful findings have come from this research. However, some important theoretical and methodological issues are yet to be resolved, particularly regarding dissonance reduction. In this paper, we place dissonance theory in the larger framework of appraisal theories of emotion, emotion regulation, and coping. The basic premise of dissonance theory is that people experience negative affect (to varying degrees) following the detection of cognitive conflict. The individual will be motivated (...) to alleviate these emotional reactions and could do so by reducing dissonance in some manner. We argue that detection of dissonance will follow the same principles as when people interpret any other stimuli as emotionally significant. Thus, appraisal theory of emotion, which argues that emotions are generated via the cognitive evaluation of surrounding stimuli, should be applicable to the dissonance-detection process. In short, we argue that dissonance-reduction strategies (attitude change, trivialization, denial of responsibility, etc.) can be understood as emotion-regulation strategies. We further argue that this perspective contributes to reconciling fragmented (and sometimes contrary) viewpoints present in the literature on dissonance reduction. In addition to proposing the general model of dissonance reduction, we illustrate at the hand of empirical data how research on dissonance reduction can be performed without relying on experimental paradigms that focus on a specific reduction strategy. (shrink)

More general form of -neutrosophic ideal is introduced, and their properties are investigated. Relations between -neutrosophic ideal and )-neutrosophic ideal are discussed. Characterizations of )-neutrosophic ideal are discussed, and conditions for a neutrosophic set to be an )-neutrosophic ideal are displayed.

We approach the subject of o-minimality from the point of view of tame systems, following the work of Charbonnel and Wilkie. This gives some general sufficient conditions for a system to be model complete and o-minimal. We are then able to obtain the following generalisation of a recent result of Gabrielov : A polynomially bounded o-minimal expansion of the real ordered field by a collection of restricted C∝ functions, which is closed under partial differentiation, is model complete.

The standard epistemic justification for inclusiveness in political decision making is the Condorcet Jury Theorem, which states that the probability of a correct decision using majority rule increases in group size (given certain assumptions). Informally, majority rule acts as a mechanism to pool the information contained in the judgements of individual agents. I aim to extend the explanation of how groups of political agents track the truth. Before agents can pool the information, they first need to find truth-conducive information. Increasing (...) group size is also important in the initial search for truth-conducive information. (shrink)

We develop a semantics for independence logic with respect to what we will call general models. We then introduce a simpler entailment semantics for the same logic, and we reduce the validity problem in the former to the validity problem in the latter. Then we build a proof system for independence logic and prove its soundness and completeness with respect to entailment semantics.

A postcompletion error is a type of procedural error that occurs after the main goal of a task has been accomplished. There is a strong theoretical foundation accounting for postcompletion errors (Altmann & Trafton, 2002; Byrne & Bovair, 1997). This theoretical foundation has been leveraged to develop a logistic regression model of postcompletion errors based on reaction time and eye movement measures (Ratwani, McCurry, & Trafton, 2008). This study further develops and extends this predictive model by (a) validating the model (...) and the general set of predictors on a new task to test the robustness of the model, and (b) determining which specific theoretical components are most important to postcompletion error prediction. (shrink)

"The term 'model' has become fashionable". What Mary Hesse characterized in the mid-1960s as a trend in logic, mathematics, and the natural sciences, applies today in general for a broad spectrum of disciplines. Today models appear to be of "extraordinary importance" compared to the first half of the twentieth century, when models were neither mentioned nor contemplated, either generally in scientific publications or specifically in the philosophy of science. It is even assumed that models are "the (...) key to science" and that today's science is largely... (shrink)

This article offers a general definition of self-knowledge that embraces all forms and levels of self-knowledge in animals and humans. It is hypothesized that various levels of self-knowledge constitute an ordinal scale such that each species in a lineage displays the forms of self-knowledge found in related species as well as new forms it and its sister species may have evolved. Likewise, it is hypothesized that these various forms of levels of self-knowledge develop in the sequence in which they (...) evolved. Finally, a general hypothesis for the functional significance of self-knowledge is proposed along with subhypotheses regarding the adaptive significance of various levels of self-knowledge in mammals including human and nonhuman primates. The general hypothesis is that self-knowledge serves as a standard for assessing the qualities of conspecifics compared to those of the self. Such assessment is crucial to deciding among alternative reproductive and subsistence strategies. The qualities that are assessed, which vary across taxa, range from the size and strength of the self to its mathematical or musical abilities. This so-called assessment model of self-knowledge is based on evolutionary biological models for social selection and the role of assessment in animal communication. (shrink)

How is scientific knowledge used, adapted, and extended in deriving phenomena and real-world systems? This paper aims at developing a general account of 'applying science' within the exemplar-based framework of Data-Oriented Processing (DOP), which is also known as Exemplar-Based Explanation (EBE). According to the exemplar-based paradigm, phenomena are explained not by deriving them all the way down from theoretical laws and boundary conditions but by modelling them on previously derived phenomena that function as exemplars. To accomplish this, DOP proposes (...) to maintain a corpus of derivation trees of previous phenomena together with a matching algorithm that combines subtrees from the corpus to derive new phenomena. By using a notion of derivational similarity, a new phenomenon can be modelled as closely as possible on previously explained phenomena. I will propose an instantiation of DOP which integrates theoretical and phenomenological modelling and which generalises over various disciplines, from fluid mechanics to language technology. I argue that DOP provides a solution for what I call Kuhn's problem and that it redresses Kitcher's account of explanation. (shrink)

Though formal metascience has made rapid advances over the past few decades, it has seldom been seen to contribute much to the rational reconstruction of scientic development; for the most part, logical concepts have found application in the synchronic analysis of scientic theories. It should be important, therefore, to consider to what extent diachronic or dynamic aspects of scientic theorizing may also be captured within the connes of a formal metascientic framework, and what tools are best suited for constructing a (...) general model of theory dynamics. (shrink)

Models provide scientists with knowledge about target systems. An important group of models are those that are called general. However, what exactly is meant by generality in this context is somewhat unclear. The aim of this paper is to draw out a distinction between two notions of generality that has implications for scientific practice. Some models are general in the sense that they apply to many systems in the world and have many particular targets. Another (...) sense is captured by models that are aimed at understanding the fundamental or underlying dynamics of a phenomenon, as opposed to how it manifests in each particular case. They have non-specific, i.e. generic targets. While both notions of generality and genericness are legitimate and correspond to different aspects of scientific practice, they must be distinguished. Failing to do so obscures the danger of overgeneralisation faced by general models and facilitates the illegitimate use of generic models as general models. This can lead to a reduction of the explanatory and predictive power of both. (shrink)

This chapter discusses the role of general models for language change and considers four such classes of model. The farming/language dispersal model is a frequent case for language replacement. But problems of chronology frequently obscure the relationships between archaeological and linguistic data.

Measurement is a process aimed at acquiring and codifying information about properties of empirical entities. In this paper we provide an interpretation of such a process comparing it with what is nowadays considered the standard measurement theory, i.e., representational theory of measurement. It is maintained here that this theory has its own merits but it is incomplete and too abstract, its main weakness being the scant attention reserved to the empirical side of measurement, i.e., to measurement systems and to the (...) ways in which the interactions of such systems with the entities under measurement provide a structure to an empirical domain. In particular it is claimed that (1) it is on the ground of the interaction with a measurement system that a partition can be induced on the domain of entities under measurement and that relations among such entities can be established, and that (2) it is the usage of measurement systems that guarantees a degree of objectivity and intersubjectivity to measurement results. As modeled in this paper, measurement systems link the abstract theory of measuring, as developed in representational terms, and the practice of measuring, as coded in standard documents such as the International Vocabulary of Metrology. (shrink)

In this article I present HYPER-REF, a model to determine the referent of any given expression in First-Order Logic. I also explain how this model can be used to determine the referent of a first-order theory such as First-Order Arithmetic. By reference or referent I mean the non-empty set of objects that the syntactical terms of a well-formed formula pick out given a particular interpretation of the language. To do so, I will first draw on previous work to make explicit (...) the notion of reference and its hyperintensional features. Then I present HYPER-REF and offer a heuristic method for determining the reference of any formula. Then I discuss some of the benefits and most salient features of HYPER-REF, including some remarks on the nature of self-reference in formal languages. (shrink)

Abstract.Since the gene splicing debates of the 1980s, the public has been exposed to an ongoing sequence of genetic and reproductive technologies. Many issue areas have outcomes that lose track of people's inner values or engender opposing religious viewpoints defying final resolution. This essay relocates the discussion of what is an acceptable application from the individual to the societal level, examining technologies that stand to address large numbers of people and thus call for policy resolution, rather than individual fiat, in (...) their application. A major source of guidance is the “Genetic Frontiers” series of professional dialogues and conferences held by the National Conference for Community and Justice from 2002 to 2004. Genetic testing, human gene therapy, genetic engineering of plants and animals, and stem cell technology are examined. While differences in perspective on the beginning of life persist, a stepwise approach to the examination of genetic testing reveals areas of general agreement. Stewardship of life, human co‐creativity with the divine, and social justice help define the bounds of application of genetic engineering and therapy; compassionate care plays a major role in establishing stem cell policy. Active, sustained dialogue is a useful resource for enabling sharing of religious values and crystallization of policies. (shrink)

The generic account of selection proposed by Hull et al. readily fits operant learning where, by comparison with natural selection, the process is well understood but little is known about the mechanism. Objections within psychology, that operant learning ignores internal processes, fail to recognise the general significance of behaviour-environment interactions. Variation within operant response classes requires further investigation.

book reveiw van boek met gelijknamige titel van Judea Pearl.

The prevalence of optimality models in the literature of evolutionary biology is testimony to their popularity and importance. Evolutionary biologist R. C. Lewontin, whose criticisms of optimality models are considered here, reflects that "optimality arguments have become extremely popular in the last fifteen years, and at present represent the dominant mode of thought." Although optimality models have received little attention in the philosophical literature, these models are very interesting from a philosophical point of view. As will (...) be argued, optimality models are central to evolutionary thought, yet they are not readily accomodated by the traditional view of scientific theories. According to the traditional view, we would expect optimality models to employ general, empirical laws of nature, but they do not. Fortunately, the semantic view of scientific theories, a recent alternative to the traditional view, more readily accomodates optimality models. As we would expect on the semantic view, optimality models can be construed as specifications of ideal systems. These specifications may be used to describe empirical systems--that is, the specifications may have empirical instances. But the specifications are not empirical claims, much less general, empirical laws. Although philosophers have yet to discuss the general features and uses of optimality models, these topics have stimulated much recent discussion among evolutionary biologists. Their discussions raise a number of precautions concerning the proper use of optimality models. Moreover, many of their caveats can be interpreted as general reminders that 1) optimality models specify ideal systems whose empirical instantiations may be quite restricted, and that 2) optimality models should not be construed as general, empirical laws. As G. F. Oster and E. O. Wilson caution, "the prudent course is to regard optimality models as provisional guides to further empirical research and not necessarily as the key to deeper laws of nature." It seems, then, that the semantic view of theories is more sensitive to the nature and limitations of optimality models than is the more traditional view of theories. (shrink)

This book explores a new approach to understanding the human mind - rational analysis - that regards thinking as a facility adapted to the structure of the world. This approach is most closely associated with the work of John R Anderson, who published the original book on rational analysis in 1990. Since then, a great deal of work has been carried out in a number of laboratories around the world, and the aim of this book is to bring this work (...) together for the benefit of the general psychological audience. The book contains chapters by some of the world's leading researchers in memory, categorisation, reasoning, and search, who show how the power of rational analysis can be applied to the central question of how humans think. It will be of interest to students and researchers in cognitive psychology, cognitive science, and animal behaviour. (shrink)

In this paper, I examined whether evidence from the neuroscience of morality supports the standard models of phronesis, i.e., Jubilee and Aretai Centre Models. The standard models explain phronesis as a multifaceted construct based on interaction and coordination among functional components. I reviewed recent neuroscience studies focusing on brain networks associated with morality and their connectivity to examine the validity of the models. Simultaneously, I discussed whether the evidence helps the models address challenges, particularly those (...) from the phronesis eliminativism. Neuroscientific evidence supported the importance of brain networks, i.e., the default mode, salience, and central executive functioning networks, in moral functioning in general. The findings favorably supported the multifaceted and integrative nature of phronesis proposed by the standard models. Finally, I considered how the two models could explain the mechanisms of phronesis more integratively based on neuroscientific findings. At the end of this paper, with the evidence, I proposed several practical ideas to promote the cultivation of phronesis, e.g., the consideration of coordination among components for moral functioning and the use of moral exemplars. (shrink)

This paper introduces three model-theoretic constructions for generalized Epstein semantics: reducts, ultramodels and $\textsf {S}$ -sets. We apply these notions to obtain metatheoretical results. We prove connective inexpressibility by means of a reduct, compactness by an ultramodel and definability theorem which states that a set of generalized Epstein models is definable iff it is closed under ultramodels and $\textsf {S}$ -sets. Furthermore, a corollary concerning definability of a set of models by a single formula is given on the (...) basis of the main theorem and the compactness theorem. We also provide an example of a natural set of generalized Epstein models which is undefinable. Its undefinability is proven by means of an $\textsf {S}$ -set. (shrink)

In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.

A generalized Bayesian inference nets model is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian fuzzy inference nets to diagnose five important types of cardiovascular diseases. The patients' medical records with doctors' confirmed diagnostic results obtained from two hospitals in China are used to design and verify HBFIN. Bayesian theorem is used to calculate the propagation of probability and address (...) the uncertainties involved in each sequential stage of inference nets to deduce the disease. The validity and effectiveness of proposed approach is witnessed clearly from testing results obtained. (shrink)

Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces (...) of possibility. The generality of models can be understood as the measure of possibilities to which the models apply. In order to illustrate our view, we consider the example of structural genomics, the ongoing project of building three-dimensional models of biological macromolecules like proteins. Using measure theory, we interpret the practice of homology modeling, where such models are understood to apply widely but imperfectly to the space of possible proteins. (shrink)

This paper explores the intersection of AI hallucinations and the question of AI consciousness, examining whether the erroneous outputs generated by large language models (LLMs) could be mistaken for signs of emergent intelligence. AI hallucinations, which are false or unverifiable statements produced by LLMs, raise significant philosophical and ethical concerns. While these hallucinations may appear as data anomalies, they challenge our ability to discern whether LLMs are merely sophisticated simulators of intelligence or could develop genuine cognitive processes. By analyzing (...) the causes of AI hallucinations, their impact on the perception of AI cognition, and the potential implications for AI consciousness, this paper contributes to the ongoing discourse on the nature of artificial intelligence and its future evolution. (shrink)

Intuitive-automatic processes are crucial for making judgements and decisions. The fascinating complexity of these processes has attracted many decision researchers, prompting them to start investigating intuition empirically and to develop numerous models. Dual-process models assume a clear distinction between intuitive and deliberate processes but provide no further differentiation within both categories. We go beyond these models and argue that intuition is not a homogeneous concept, but a label used for different cognitive mechanisms. We suggest that these mechanisms (...) have to be distinguished to allow for fruitful investigations of intuition. Specifically, we argue that researchers should concentrate on investigating the processes underlying intuition before making strong claims about its performance. We summarise current models for intuition and propose a categorisation according to the underlying cognitive processes: (a) associative intuition based on simple learning-retrieval processes, (b) matching intuition based on comparisons with prototypes/exemplars, (c) accumulative intuition based on automatic evidence accumulation, and (d) constructive intuition based on construction of mental representations. We discuss how this differentiation might help to clarify the relationship between affect and intuition and we derive a very general hypothesis as to when intuition will lead to good decisions and when it will go astray. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection (...) mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (shrink)