I argue here for a number of ways that modern computational science requires a change in the way we represent the relationship between theory and applications. It requires a switch away from logical reconstruction of theories in order to take surface mathematical syntax seriously. In addition, syntactically different versions of the same theory have important differences for applications, and this shows that the semantic account of theories is inappropriate for some purposes. I also argue against formalist approaches in the (...) philosophy of science and for a greater role for perceptual knowledge rather than propositional knowledge in scientific empiricism. (shrink)

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access (...) to the empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from (...) both computer science and philosophy, we conclude that the current state of deep learning does not offer strong encouragement to the empiricist side despite some arguments to the contrary. (shrink)

This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics, psychology, computer science, and cognitive science, the book sheds light on the central problems of learnability and language, and traces their implications (...) for key questions of theoretical linguistics and the study of language acquisition. (shrink)

This paper explores connections between Radical Empiricism (RE), a philosophic attitude developed by William James at the beginning of the 20th century, and Empirical Modelling (EM), an approach to computer-based modelling that has been developed by the author and his collaborators over a number of years. It focuses in particular on how both RE and EM promote a perspective on the nature of knowing that is radically different from that typically invoked in contemporary approaches to knowledge representation in computing. (...) This is illustrated in detail with reference to the modelling of several scenarios of lift use. Some potential implications for knowledge management are briefly reviewed. (shrink)

This paper explores connections between Radical Empiricism, a philosophic attitude developed by William James at the beginning of the 20th century, and Empirical Modelling, an approach to computer-based modelling that has been developed by the author and his collaborators over a number of years. It focuses in particular on how both RE and EM promote a perspective on the nature of knowing that is radically different from that typically invoked in contemporary approaches to knowledge representation in computing. This is (...) illustrated in detail with reference to the modelling of several scenarios of lift use. Some potential implications for knowledge management are briefly reviewed. (shrink)

This article presents the evolution of philosophical and methodological considerations concerning empiricism in computer/computing science. In this study, we trace the most important current events in the history of reflection on computing. The forerunners of Artificial Intelligence H.A. Simon and A. Newell in their paper Computer Science As Empirical Inquiry started these considerations. Later the concept of empirical computer science was developed by S.S. Shapiro, P. Wegner, A.H. Eden and P.J. Denning. They showed various empirical aspects of computing. This (...) led to a view of the science of computing - the science of general scope. Some interesting contemporary ways towards a generalized perspective on computations were also shown. (shrink)

Historically, the dispute between empiricists and nativists in philosophy and cognitive science has concerned human and animal minds (Margolis and Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, Colombo in Synthese 195: 4817–4838, 2018). But recent progress has highlighted how empiricist and nativist concerns arise in the construction of artificial systems (Buckner in From deep learning to rational machines: What the history of philosophy can teach us about the (...) future of artificial intelligence. Oxford University Press.). This paper uses nativism and empiricism to address questions about the nature of artificial intelligence and its trajectory. It begins by defining the nativism/empiricism debate in terms of the generality of a system. Nativist systems have initial states with domain-specific features; empiricist systems have initial states with only domain-general features. With the debate framed in this way, it then explores a variety of arguments for nativism and empiricism in AI. These arguments revolve around two different questions which must be distinguished: whether nativism the only possible approach to developing human-level AI (HLAI); and whether nativism is the most practical approach to developing HLAI. On the first question, it argues that nativism is quite clearly not the only possible approach to developing HLAI, as is sometimes suggested. It argues that existing arguments for the necessity of nativism are unconvincing, because they analogize from poverty of the stimulus arguments about humans, while AIs often have access to much more data than humans. Then it argues that the case of evolution gives us a compelling argument against nativism. On the second, practical question, the paper argues that there is a tradeoff between the advantages of encoding innate machinery directly, and the advantages of evolving or learning it. However, as the past decade has shown, empiricism is a much more viable path to greater capability levels, given the ‘bitter lesson’ (Sutton in _Reinforcement Learning: An Introduction_. MIT press.) that encoding the ‘correct’ knowledge in AI systems is perennially outperformed by more empiricist methods that leverage large-scale data and computation. (shrink)

Climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an understudied science to illustrate two distinct philosophical approaches to the relations among data, scientist, measurement, models, (...) and theory. I argue that distinguishing between “direct” empiricist and “complex” empiricist approaches helps us understand and analyze this important scientific episode. I introduce a complex empiricist account of testing and evaluation, and contrast it with the basic hypothetico-deductive approach to the climate models used by the direct empiricists. This more developed complex empiricist approach will serve philosophy of science well, as computational models become more widespread in the sciences. (shrink)

climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among data, scientists, measurement, models, (...) and theory. I argue that distinguishing between 'direct' empiricist and 'complex' empiricist approaches helps us understand and analyze this important scientific episode. I also introduce a complex empiricist account of testing and evaluation, and contrast it with the basic Hypothetico-Deductive approach to the climate models used by the direct empiricists. This more developed complex empiricist apporach will serve philosophy of science well, as computational models become more wide-spread in the sciences. (shrink)

The radical empiricist theory of the Quartz & Sejnowski target article would result in a brain that could not act. The attempt to bolster this position with computational arguments is misleading and often just wrong. Fortunately, other efforts are making progress in linking neural and cognitive development.

The topic of this paper is our knowledge of the natural numbers, and in particular, our knowledge of the basic axioms for the natural numbers, namely the Peano axioms. The thesis defended in this paper is that knowledge of these axioms may be gained by recourse to judgements of probability. While considerations of probability have come to the forefront in recent epistemology, it seems safe to say that the thesis defended here is heterodox from the vantage point of traditional philosophy (...) of mathematics. So this paper focuses on providing a preliminary defense of this thesis, in that it focuses on responding to several objections. Some of these objections are from the classical literature, such as Frege's concern about indiscernibility and circularity, while other are more recent, such as Baker's concern about the unreliability of small samplings in the setting of arithmetic. Another family of objections suggests that we simply do not have access to probability assignments in the setting of arithmetic, either due to issues related to the~$\omega$-rule or to the non-computability and non-continuity of probability assignments. Articulating these objections and the responses to them involves developing some non-trivial results on probability assignments, such as a forcing argument to establish the existence of continuous probability assignments that may be computably approximated. In the concluding section, two problems for future work are discussed: developing the source of arithmetical confirmation and responding to the probabilistic liar. (shrink)

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and (...) cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. (shrink)

The topic of this dissertation is what thought must be like in order for the laws and generalizations of psychology to be true. I address a number of contemporary problems in the philosophy of mind concerning the nature and structure of concepts and the ontological status of mental content. Drawing on empirical work in psychology, I develop a number of new conceptual tools for theorizing about concepts, including a counterpart model of concepts' role in linguistic communication, and a deflationary theory (...) of concepts' formal features. I also suggest some new answers to old problems, arguing, for example, that content realism is not hostage to a naturalized semantics. This dissertation can, as a whole, be read as a sympathetic re-evaluation of the language of thought hypothesis (LOT). LOT claims that thoughts are sentences in a mental language of computation, and are composed of meaningful, atomic symbols—concepts—which are individuated entirely by their formal features. Each chapter either defends various components of LOT from recent criticism, or fills in gaps in the theory. I begin the dissertation by introducing the language of thought project, and motivating and explaining each of its central components. In chapter 2, I discuss a recent competitor to atomism—neo-empiricism—and argue that it fails to meet several key desiderata on theories of concepts, and defend atomism from similar charges. In chapter 3, I argue against a view common to both philosophy and psychology: that concepts must be shareable. If true, atomism is in jeopardy. I find shareability to be unmotivated, and suggest an alternative counterpart model of concepts that does a better job of explaining the things shareability was supposed to explain. Chapter 4 takes up the question of what formal features are and how mental symbols are individuated. I develop a reductive account, arguing that formal features are certain sorts of physical properties and symbol types are sets of these properties. I turn to the topic of content in chapter 5, and defend a very strong version of content realism against recent criticism. (shrink)

For the past three decades linguistic theory has been based on the assumption that sentences are interpreted and constructed by the brain by means of computational processes analogous to those of a serial-digital computer. The recent interest in devices based on the neural network or parallel distributed processor (PDP) principle raises the possibility ("eliminative connectionism") that such devices may ultimately replace the S-D computer as the model for the interpretation and generation of language by the brain. An analysis of (...) the differences between the two models suggests that the effect of such a development would be to steer linguistic theory towards a return to the empiricism and behaviorism which prevailed before it was driven by Chomsky towards nativism and mentalism. Linguists, however, will not be persuaded to return to such a theory unless and until it can deal with the phenomenon of novel sentence construction as effectively as its nativist/mentalist rival. (shrink)

Herbert Simon’s work on scientific discovery deserves serious attention by philosophers of science for several reasons. First, Simon was an early advocate of rational scientific discovery, contra Popper and logical empiricist philosophers of science (Simon 1966). This proposal spurred on investigation of scientific discovery in philosophy of science, as philosophers used and developed Simon’s notions of “problem solving” and “heuristics” in attempts to provide rational accounts of scientific discovery (See Nickles 1980a, Wimsatt 1980). Second, Simon promoted and developed many of (...) the crucial techniques and methods used in cognitive science. One is the use of computers to model internal psychological processes, a technique central to his account of scientific discovery. Another is protocol analysis, the use of the verbal reports of experimental subjects in psychology to construct accounts of their psychological processes. Protocol analysis is given a detailed formulation by Simon (Simon and Ericsson 1984), and is modified for use in the study of scientific cognition in the paper on Krebs (Kulkarni and Simon 1988). (shrink)

Part I presents a model of interactive computation and a metric for expressiveness, Part II relates interactive models of computation to physics, and Part III considers empirical models from a philosophical perspective. Interaction machines, which extend Turing Machines to interaction, are shown in Part I to be more expressive than Turing Machines by a direct proof, by adapting Gödel's incompleteness result, and by observability metrics. Observation equivalence provides a tool for measuring expressiveness according to which interactive systems are more expressive (...) than algorithms. Refinement of function equivalence by observation of outer interactive behavior and inner computation steps is examined. The change of focus from algorithms specified by computable functions to interaction specified by observation equivalence captures the essence of empirical computer science. Part II relates interaction in models of computation to observation in the natural sciences. Explanatory power in physics is specified by the same observability metric as expressiveness in interactive systems. Realist models of inner structure are characterized by induction, abduction, and Occam's Razor. Interactive realism extends the hidden-variable model of Einstein to hidden interfaces that provide extra degrees of freedom to formulate hypotheses with testable predictions conforming with quantum theory. Greater expressiveness of collaborative computational observers (writers) than single observers implies that hidden-interface models are more expressive than hidden-variable models. By providing a common foundation for empirical computational and physical models we can use precise results about computational models to establish properties of physical models. Part III shows that the evolution in computing from algorithms to interaction parallels that in physics from rationalism to empiricism. Plato's cave metaphor is interactively extended from Platonic rationalism to empiricism. The Turing test is extended to TMs with hidden interfaces that express interactive thinking richer than the traditional Turing test. Interactive (nonmonotonic) extensions of logic such as the closed-world assumption suggest that interactiveness is incompatible with monotonic logical inference. Procedure call, atomicity of transactions, and taking a fixed point are techniques for closing open systems similar to "preparation" followed by "observation" of a physical system. Pragmatics is introduced as a framework for extending logical models with a fixed syntax and semantics to multiple-interface models that support collaboration among clients sharing common resources. (shrink)

This work locates the historical and conceptual foundations of cognitive science in the "commonsense" psychology of the philosopher Thomas Reid. I begin with Reid's attack on his rationalist and empiricist competitors of the 17th and 18th centuries. I then present his positive theory as a sophisticated faculty psychology appealing to innateness of mental structure. Reidian psychological faculties are equally trustworthy, causally independent mental powers, and I argue that they share nine distinct properties. This distinguishes Reidian 'intentionalism' from idealist 'representationalism,' which (...) derive cognitive content either from the inherited structure of faculties of from the occurrent structure of sensory activity. ;Next, I turn to consciousness and reflection for a contemporary Reidian response to traditional phenomenology. Unlike reflection, faculties of reason and remembrance are not causally mediated by consciousness. My interpretation of Reid is that 'Humean causation' of individual faculty structure accounts only for 'natural intentionality,' while 'efficient causation' of faculty interrelations accounts for cognitive 'personal intentionality.' ;I then proceed by adopting a form of computational description for reconstructing this view as a computational theory of mind. I contrast functional analyses in explanations of some capacities with computational and componential analyses in explanations of other, intentional capacities, in which some processes must be taken to semantically encode and govern the roles of others. This step reconstructs the Reidian notion of intentional operations as requiring an explanation of component faculties and their representation-governed interactions. I argue that properties of faculties delimiting these interactions under Reid's theory parallel those in Fodor's essay on the "modularity of mind," although the reasons given for individual criteria are often very different. Fodor also proposes a trichotomous mental structure, but I find that a third level of "central systems" is a myth engendered by causal information theory. Such an analysis cannot capture generalizations over the internal representation of semantic roles that determines the character of faculty relations. This requirement for any computational account of cognition is precisely the motivation for the reconstructed Reidian theory. Thus, it comports more favorably with the explanatory program constitutive of a computational cognitive science. (shrink)

In this paper I link two hitherto disconnected sets of results in the philosophy of emotions and explore their implications for the computational theory of mind. The argument of the paper is that, for just the same reasons that some computationalists have thought that cognition may be a natural kind, so the same can plausibly be argued of emotion. The core of the argument is that emotions are a representation-governed phenomenon and that the explanation of how they figure in (...) behaviour must as such be undertaken in those terms. I conclude with some interdisciplinary reflections in defence of the hypothesis that emotions might be more fundamental in the organization of behaviour than cognition; that, in effect, we may be emoters before we are cognizers . The aim of the paper is: (1) to introduce a number of promising results in philosophical and empirical emotion theory to a wider audience; and (2) to begin the task of organizing those results into a computational theoretical framework. (shrink)

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

The ancient dualism of a sensible and an intelligible world important in Neoplatonic and medieval philosophy, down to Descartes and Kant, would seem to be supplanted today by a scientific view of mind-in-nature. Here, we revive the old dualism in a modified form, and describe mind as a symbolic language, founded in linguistic recursive computation according to the Church-Turing thesis, constituting a world L that serves the human organism as a map of the Universe U. This methodological distinction of L (...) vs. U helps to understand how and why structures of phenomena come to be opposed to their nature in human thought, a central topic in Heideggerian philosophy. U is uncountable according to Georg Cantor’s set theory but Language L, based on the recursive function system, is countable, and anchored in a Gray Area within U of observable phenomena, typically symbols, prelinguistic structures, genetic-historical records of their origins. Symbols, the phenomena most familiar to mathematicians, are capable of being addressed in L-processing. The Gray Area is the human Environment E, where we can live comfortably, that we manipulate to create our niche within hostile U, with L offering overall competence of the species to survive. The human being is seen in the light of his or her linguistic recursively computational mind. Nature U, by contrast, is the unfathomable abyss of being, infinite labyrinth of darkness, impenetrable and hostile to man. The U-man, biological organism, is a stranger in L-man, the mind-controlled rational person, as expounded by Saint Paul. Noumena can now be seen to reside in L, and are not fully supported by phenomena. Kant’s noumenal cause is the mental L-image of only partly phenomenal causation. Mathematics occurs naturally in pre-linguistic phenomena, including natural laws, which give rise to pure mathematical structures in the world of L. Mathematical foundation within philosophy is reversed to where natural mathematics in the Gray Area of pre-linguistic phenomena can be seen to be a prerequisite for intellectual discourse. Lesser, nonverbal versions of L based on images are shared with animals. (shrink)

Proč by se měl filozof vědy zajímat o počítačové simulace? Autorka ve své práci argumentuje tezi, podle níž je počítačová simulace nejen samozřejmou součástí vědeckých metod, ale navíc má potenciál rozvíjet na poli filozofie vědy zcela nové metodologické koncepty a kategorie. Na příkladu tzv. numerického experimentu, který představuje jednu z nejčastějších podob počítačových simulací ve vědě, je ukázán jejich nejasný metodologický status. Na jedné straně lze u simulací (konkrétně numerického experimentování) sledovat vlastnosti, které jsou typické pro teoretický přístup ve vědě, (...) na druhé straně však rovněž vystupují v roli velmi blízké empirické evidenci, čímž se naopak blíží experimentu. V textu autorka představuje zejména Humphreysovu interpretaci počítačové simulace jako hybridní kategorie, která vzbuzuje diskuzi nad založením zcela nové, simulace lépe postihující teorie vědy. (shrink)

Proč by se měl filozof vědy zajímat o počítačové simulace? Autorka ve své práci argumentuje tezi, podle níž je počítačová simulace nejen samozřejmou součástí vědeckých metod, ale navíc má potenciál rozvíjet na poli filozofie vědy zcela nové metodologické koncepty a kategorie. Na příkladu tzv. numerického experimentu, který představuje jednu z nejčastějších podob počítačových simulací ve vědě, je ukázán jejich nejasný metodologický status. Na jedné straně lze u simulací (konkrétně numerického experimentování) sledovat vlastnosti, které jsou typické pro teoretický přístup ve vědě, (...) na druhé straně však rovněž vystupují v roli velmi blízké empirické evidenci, čímž se naopak blíží experimentu. V textu autorka představuje zejména Humphreysovu interpretaci počítačové simulace jako hybridní kategorie, která vzbuzuje diskuzi nad založením zcela nové, simulace lépe postihující teorie vědy. (shrink)

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules (...) are formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

Philosophers have often claimed that general ideas or representations have their origin in abstraction, but it remains unclear exactly what abstraction as a psychological process consists in. We argue that the Lockean aspiration of using abstraction to explain the origins of all general representations cannot work and that at least some general representations have to be innate. We then offer an explicit framework for understanding abstraction, one that treats abstraction as a computational process that operates over an innate quality (...) space of fine-grained general representations. We argue that this framework has important philosophical implications for the nativism-empiricism dispute, for questions about the acquisition of unstructured representations, and for questions about the relation between human and animal minds. (shrink)

Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of (...) neurons is indeed one with precedents, although the implications of this for psychological issues have been interpreted variously — not all versions of connectionism are alike. (shrink)

This powerfully iconoclastic book reconsiders the influential nativist position toward the mind. Nativists assert that some concepts, beliefs, or capacities are innate or inborn: "native" to the mind rather than acquired. Fiona Cowie argues that this view is mistaken, demonstrating that nativism is an unstable amalgam of two quite different--and probably inconsistent--theses about the mind. Unlike empiricists, who postulate domain-neutral learning strategies, nativists insist that some learning tasks require special kinds of skills, and that these skills are hard-wired into our (...) brains at birth. This "faculties hypothesis" finds its modern expression in the views of Noam Chomsky. Cowie, marshaling recent empirical evidence from developmental psychology, psycholinguistics, computer science, and linguistics, provides a crisp and timely critique of Chomsky's nativism and defends in its place a moderately nativist approach to language acquisition. Also in contrast to empiricists, who view the mind as simply another natural phenomenon susceptible of scientific explanation, nativists suspect that the mental is inelectably mysterious. Cowie addresses this second strand in nativist thought, taking on the view articulated by Jerry Fodor and other nativists that learning, particularly concept acquisition, is a fundamentally inexplicable process. Cowie challenges this explanatory pessimism, and argues convincingly that concept acquisition is psychologically explicable. What's Within? is a clear and provocative achievement in the study of the human mind. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense (...) of culture, history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

Thought experiments are a means of imaginative reasoning that lie at the heart of philosophy, from the pre-Socratics to the modern era, and they also play central roles in a range of fields, from physics to politics. The Routledge Companion to Thought Experiments is an invaluable guide and reference source to this multifaceted subject. Comprising over 30 chapters by a team of international contributors, the Companion covers the following important areas: -/- · the history of thought experiments, from antiquity to (...) the trolley problem and quantum non-locality; -/- · thought experiments in the humanities, arts, and sciences, including ethics, physics, theology, biology, mathematics, economics, and politics; -/- · theories about the nature of thought experiments; -/- · new discussions concerning the impact of experimental philosophy, cross-cultural comparison studies, metaphilosophy, computer simulations, idealization, dialectics, cognitive science, the artistic nature of thought experiments, and metaphysical issues. -/- This broad ranging Companion goes backwards through history and sideways across disciplines. It also engages with philosophical perspectives from empiricism, rationalism, naturalism, skepticism, pluralism, contextualism, and neo-Kantianism to phenomenology. This volume will be valuable for anyone studying the methods of philosophy or any discipline that employs thought experiments, as well as anyone interested in the power and limits of the mind. (shrink)

Scientific Philosophy: Origins and Development is the first Yearbook of the Vienna Circle Institute, which was founded in October 1991. The book contains original contributions to an international symposium which was the first public event to be organised by the Institute: `Vienna--Berlin--Prague: The Rise of Scientific Philosophy: The Centenaries of Rudolf Carnap, Hans Reichenbach and Edgar Zilsel.' The first section of the book - `Scientific Philosophy - Origins and Developments' reveals the extent of scientific communication in the inter-War years between (...) these great metropolitan centres, as well as presenting systematic investigations into the relevance of the heritage of the Vienna Circle to contemporary research and philosophy. This section offers a new paradigm for scientific philosophy, one which contrasts with the historiographical received view of logical empiricism. Support for this re-evaluation is offered in the second section, which contains, for the first time in English translation, Gustav Bergmann's recollections of the Vienna Circle, and an historical study of political economist Wilhelm Neurath, Otto Neurath's father. The third section gives a report on current computer-based research which documents the relevance of Otto Neurath's `Vienna method of pictorial statistics', or `Isotypes'. A review section describes new publications on Neurath and the Vienna Circle, as well anthologies relevant to Viennese philosophy and its history, setting them in their wider cultural and political perspective. Finally, a description is given of the Vienna Circle Institute and its activities since its foundation, as well as of its plans for the future. (shrink)

This handbook provides both an overview of state-of-the-art scholarship in philosophy of science, as well as a guide to new directions in the discipline. Section I contains broad overviews of the main lines of research and the state of established knowledge in six principal areas of the discipline, including computational, physical, biological, psychological and social sciences, as well as general philosophy of science. Section II covers what are considered to be the traditional topics in the philosophy of science, such (...) as causation, probability, models, ethics and values, and explanation. Section III identifies new areas of investigation that show promise of becoming important areas of research, including the philosophy of astronomy and astrophysics, data, complexity theory, neuroscience, simulations, post-Kuhnian philosophy, post-empiricist epistemology, and emergence. Most chapters are accessible to scientifically educated non-philosophers as well as to professional philosophers, and the contributors - all leading researchers in their field -- bring diverse perspectives from the North American, European, and Australasian research communities. This volume is an essential resource for scholars and students. (shrink)

PREFACE PART I METAPHYSICS Review of John McDowell’s Mind and World Special Sciences: Still Autonomous after All These Years Conclusion Acknowledgment Notes PART II CONCEPTS Review of Christopher Peacocke’s A Study of Concepts Notes There Are No Recognitional Concepts--Not Even RED Introduction Compositionality Why Premise P is Plausible Objections Conclusion Afterword Acknowledgment Notes There Are No Recognitional Concepts--Not Even RED, Part 2: The Plot Thickens Introduction: The Story ’til Now Compositonality and Learnability Notes Do We Think in Mentalese? Remarks on (...) Some Arguments of Peter Carruthers Appendix: Higher-Order Thoughts Notes Review of A. W. Moore’s Points of View PART III COGNITIVE ARCHITECTURE Review of Paul Churchland’s The Engine of Reason, The Seat of the Soul Connectionism and the Problem of Systematicity: Why Smolensky’s Solution Doesn’t Work Introduction I The Systematicity Problem and Its Classical Solution II Weak Compositionality III Strong Compositional Structure Conclusion Notes Connectionism and the Problem of Systematicity : Why Smolensky’s Solution Still Doesn’t Work Stage 1: Classical Theories The Connection with The Connection with Stage 2: Smolenksy Architectures Stage 3: Why Smolensky’s Solution Still Doesn’t Work Digression on singing and sailing Acknowledgment Notes There and Back Again: A Review of Annette Karmiloff-Smith’s Beyond Modularity 1. Encapsulation 2. Inaccessibility 3. Domain specificity 4. Innateness Conclusion Notes Review of Jeff Elman et al., Rethinking Innateness Brainlikeness Interactions Representational Nativism Empiricism Review of Steven Mithen’s The Prehistory of the Mind PART IV PHILOSOPHICAL DARWINISM Review of Richard Dawkins’s Climbing Mount Improbable Deconstructing Dennett’s Darwin Introduction Adaptation Adaptation and Teleology Deconstruction Notes Is Science Biologically Possible? Comments on Some Arguments of Patricia Churchland and of Alvin Plantinga Acknowledgment Notes Review of Steven Pinker’s How the Mind Works and Henry Plotkin’s Evolution in Mind Computation Massive modularity Innateness Psychological Darwinism. (shrink)

This article critically examines the views that psychology first came into existence as a discipline ca. 1879, that philosophy and psychology were estranged in the ensuing decades, that psychology finally became scientific through the influence of logical empiricism, and that it should now disappear in favor of cognitive science and neuroscience. It argues that psychology had a natural philosophical phase (from antiquity) that waxed in the seventeenth and eighteenth centuries, that this psychology transformed into experimental psychology ca. 1900, that (...) philosophers and psychologists collaboratively discussed the subject matter and methods of psychology in the first two decades of the twentieth century, that the neobehaviorists were not substantively influenced by the Vienna Circle, that the study of perception and cognition in psychology did not disappear in the behaviorist period and so did not reemerge as a result of artificial intelligence, linguistics, and the computer analogy, that although some psychologists adopted the language-of-thought approach of traditional cognitive science, many did not, and that psychology will not go away because it contributes independently of cognitive science and neuroscience. (shrink)

The philosophy of science has lost its self-confidence, witness the lack of advanced textbooks in contrast to the abundance of elementary textbooks. Structures in Science is an advanced textbook that explicates, updates, accommodates, and integrates the best insights of logical-empiricism and its main critics. This `neo-classical approach' aims at providing heuristic patterns for research. The book introduces four ideal types of research programs and reanimates the distinction between observational laws and proper theories. It explicates various patterns of explanation by (...) subsumption and specification as well as structures in reductive and other types of interlevel research. Its analysis of theory evaluation leads to new characterizations of confirmation, empirical progress, and pseudoscience. Partial analogies between progress in nomological research and progress in explicative and design research emerge. Finally, special chapters are devoted to design research programs, computational philosophy of science, the structuralist approach to theories, and research ethics. (shrink)

In a time of big data, thinking about how we are seen and how that affects our lives means changing our idea about who does the seeing. Data produced by machines is most often ‘seen’ by other machines; the eye is in question is algorithmic. Algorithmic seeing does not produce a computational panopticon but a mechanism of prediction. The authority of its predictions rests on a slippage of the scientific method in to the world of data. Data science inherits (...) some of the problems of science, especially the disembodied ‘view from above’, and adds new ones of its own. As its core methods like machine learning are based on seeing correlations not understanding causation, it reproduces the prejudices of its input. Rising in to the apparatuses of governance, it reinforces the problematic sides of ‘seeing like a state’ and links to the recursive production of paranoia. It forces us to ask the question ‘what counts as rational seeing?’. Answering this from a position of feminist empiricism reveals different possibilities latent in seeing with machines. Grounded in the idea of conviviality, machine learning may reveal forgotten non-market patterns and enable free and critical learning. It is proposed that a programme to challenge the production of irrational pre-emption is also a search for the possibility of algorithmic conviviality. (shrink)

Mark Wilson argues that the standard categorizations of "Theory T thinking"— logic-centered conceptions of scientific organization (canonized via logical empiricists in the mid-twentieth century)—dampens the understanding and appreciation of those strategic subtleties working within science. By "Theory T thinking," we mean to describe the simplistic methodology in which mathematical science allegedly supplies ‘processes’ that parallel nature's own in a tidily isomorphic fashion, wherein "Theory T’s" feigned rigor and methodological dogmas advance inadequate discrimination that fails to distinguish between explanatory structures that (...) are architecturally distinct. One of Wilson's main goals is to reverse such premature exclusions and, thus, early on Wilson returns to John Locke's original physical concerns regarding material science and the congeries of descriptive concern insofar as capturing varied phenomena (i.e., cohesion, elasticity, fracture, and the transmission of coherent work) encountered amongst ordinary solids like wood and steel are concerned. Of course, Wilson methodologically updates such a purview by appealing to multiscalar techniques of modern computing, drawing from Robert Batterman's work on the greediness of scales and Jim Woodward's insights on causation. (shrink)

This paper forges links between early analytic philosophy and the posits of semiotics. I show that there are some striking and potentially quite important, but perhaps unrecognized, connections between three key concepts in Wittgenstein’s middle and later philosophy, namely, complex, rule-following, and language games. This reveals the existence of a conceptual continuity between Wittgenstein’s “early” and “later” philosophy that can be applied to the analysis of the iterability of representation in computer-generated images. Methodologically, this paper clarifies to at least some (...) degree, the nature, progress and promise of an approach to doing philosophy and semiotics from a modally modest perspective that sees in the intellectual products of humanities, and not in unreflective empiricism, the future of scientific development. This hybrid, non-reductionist approach shows, among other things, that semiotic processes are encoded by specific types of complexes in computer-generated images that display iterability in time and space. (shrink)

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive (...) science. Clearly, defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

An in-depth history of the linguistic turn in analytic philosophy, from a leading philosopher of language This is the second of five volumes of a definitive history of analytic philosophy from the invention of modern logic in 1879 to the end of the twentieth century. Scott Soames, a leading philosopher of language and historian of analytic philosophy, provides the fullest and most detailed account of the analytic tradition yet published, one that is unmatched in its chronological range, topics covered, and (...) depth of treatment. Focusing on the major milestones and distinguishing them from detours, Soames gives a seminal account of where the analytic tradition has been and where it appears to be heading. Volume 2 provides an intensive account of the new vision in analytical philosophy initiated by Ludwig Wittgenstein’s Tractatus Logico-Philosophicus, its assimilation by the Vienna Circle of Moritz Schlick and Rudolf Carnap, and the subsequent flowering of logical empiricism. With this “linguistic turn,” philosophical analysis became philosophy itself, and the discipline’s stated aim was transformed from advancing philosophical theories to formalizing, systematizing, and unifying science. In addition to exploring the successes and failures of philosophers who pursued this vision, the book describes how the philosophically minded logicians Kurt Gödel, Alfred Tarski, Alonzo Church, and Alan Turing discovered the scope and limits of logic and developed the mathematical theory of computation that ushered in the digital era. The book’s account of this pivotal period closes with a searching examination of the struggle to preserve ethical normativity in a scientific age. (shrink)

Functionalist theories of mind sometimes have viewed consciousness as emerging simply from the computational activity of extremely complex information-processing systems. Empirical evidence suggests strongly, however, that experiences without content ("pure consciousness" events, or "core mystical experience") and devoid of subjectivity (no sense of agency or ownership) do happen. The occurrence of such consciousness, lacking all informational content, counts against any theory that equates consciousness with the mere "flow of information," no matter how intricate.

Yoshimi has attempted to defuse my argument concerning the identification of network abstraction with empiricist abstraction - thus entailing psychologism - by claiming that the argument does not generalize from the example of simple feed-forward networks. I show that the particular details of networks are logically irrelevant to the nature of the abstractive process they employ. This is ultimately because deep artificial neural networks (ANNs) and dynamical systems theory applied to the mind (DST) are both associationisms - that is, empiricist (...) theories that derive the principles of thought from the causal history of the organism/system. On this basis, I put forward a new aspect of the old argument by noting that ANNs & DST are the causal bases of the phenomena of passive synthesis, whereas the language of thought hypothesis (LOT) and the symbolic computational theory of mind (CTM) are the causal bases of the phenomena of active synthesis. If the phenomena of active synthesis are not distinct in kind from and are thus reducible to those of passive synthesis, psychologism results. Yoshimi’s program, insofar as it denies this fundamental phenomenological distinction, is revealed to be the true anti-pluralist program, by essentially denying the causal efficacy of the mechanistic foundations of active synthesis by referring phenomenology exclusively to associationism for its causal foundation. (shrink)

This paper argues, contrary to Fodor’s well-known position, that the iconic and discursive modes of representation are not mutually exclusive categories. It is argued that there exists at least a third kind of representation which blends the semantic properties of icons and the syntactic properties of discourses. We reason that this iconic-discursive genus behaves differently from other representational formats, such as distributed representations or maps, previously put forward as challenging Fodor’s basic distinction. A reflection follows about how this kind of (...) representation impacts on the Fodorian issues for which the original dual distinction was argumentatively instrumental, namely, the kinds of codes and possible inter-code relations accessible to the representational mind. The suggestion is put forward that iconic-discursive representations may facilitate trade-offs between the world and the representational mind, as well as between the differently complex levels of representation that mediate between percepts and concepts. We conclude that such aspects of the computational mind, which until now appeared to be stubbornly resistant with respect to a conciliation of Hume’s empiricism and Fodor’s computationalism, may be more easily accessed and understood taking advantage of the biosemiotics perspective and acknowledging the richness of the biosemiotics codes accessible to cognition. (shrink)

One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial (...) deep learning networks, namely the convolutional neural network and the generative adversarial network have been found to possess the capability to build internal states that are interpreted by humans as complex visual categories, without any specific hints or any grammatical processing. This emergent ability suggests that those categories do not depend on human knowledge or the syntactic structure of language, while they do rely on their visual context. This supports a mild form of empiricism, while it does not assume that computational functionalism is true. Some consequences are extracted regarding the debate about amodal and grounded representations in the human brain. Furthermore, new avenues for research on cognitive science are open. (shrink)

A form of context-appropriate verificationism is proposed that distinguishes between scientific theories as evolving systems of ideas and operationally-specified, testable formal-empirical models. Theories undergo three stages : a formative, exploratory, heuristic phase of theory conception, a developmental phase of theory-pruning and refinement, and a mature, rigorous phase of testing specific, explicit models. The first phase depends on Feyerabendian open possibility, the second on theoretical plausibility and internal coherence, and the third on testability. Multiple perspectives produce variety necessary for theory formation, (...) whereas explicit agreement on evaluative criteria is essential for testing. Hertzian observer-mechanics of empirical-deductive scientific models are outlined that use semiotic operations of measurement/evaluation, computation, and physical action/construction. If models can be fully operationalized, then they can be intersubjectively verified irrespective of metaphysical, theoretical, value-, or culture-based disagreements. Verificationism can be expanded beyond simple predictive efficacy to incorporate testing for pragmatic, functional efficacy in engineering, medicine, and design contexts. Such a more open, pragmatist, operationalist, epistemically-constructivist perspective is suggested in which verification is contingent on the type of assertion, its intended purpose, degree and reliability of model-based evidence, and existence of alternate, competing predictive models. Suggestions for epistemological hygiene amidst the world-wide pandemic of misinformation and propaganda are offered. (shrink)