Much work on legal knowledge systems treats legal reasoning as arguments that lead from a description of the law and the facts of a case, to the legal conclusion for the case. The reasoning steps of the inference engine parallel the logical steps by means of which the legal conclusion is derived from the factual and legal premises. In short, the relation between the input and the output of a legal inference engine is a logical one. The truth of the (...) conclusion only depends on the premises, and is independent of the argument that leads to the conclusion.This paper opposes the logical approach, and defends a procedural approach to legal reasoning. Legal conclusions are not true or false independent of the reasoning process that ended in these conclusions. In critical cases this reasoning process consists of an adversarial procedure in which several parties are involved. The course of the argument determines whether the conclusion is true or false. The phenomenon of hard cases is used to demonstrate this essential procedural nature of legal reasoning. (shrink)

Chomsky's current Biolinguistic methodology is shown to comport with what might be called 'established' aspects of biological method, thereby raising, in the biolinguistic domain, issues concerning biological autonomy from the physical sciences. At least current irreducibility of biology, including biolinguistics, stems in at least some cases from the very nature of what I will claim is physiological, or inter-organ/inter-component, macro-levels of explanation which play a new and central explanatory role in Chomsky's inter-componential explanation of certain properties of the syntactic component (...) of Universal Grammar. Under this new mode of explanation, certain physiological functions of cognitive mental organs are hypothesized, in an attempt to explain aspects of their internal anatomy. Thus, the internal anatomy of the syntactic component exhibits features that enable it to effectively interface with other 'adjacent' organs, such as the Conceptual-Intensional system and the Sensory- Motor system. These two interface systems take as their inputs the assembled outputs of the syntactic component and, as a result of the very syntactic structure imposed by the syntax are then able to assign their sound and meaning interpretations. If this is an accurate characterization, Chomsky's long-standing postulation of mental organs, and I will argue, the advancement of new hypotheses concerning physiological inter-organ functions, has attained in current biolinguistic Minimalist method a significant unification with foundational aspects of physiological explanation in other areas of biology. (shrink)

Cognitive psychologists, like biologists, frequently describe mechanisms when explaining phenomena. Unlike biologists, who can often trace material transformations to identify operations, psychologists face a more daunting task in identifying operations that transform information. Behavior provides little guidance as to the nature of the operations involved. While not itself revealing the operations, identification of brain areas involved in psychological mechanisms can help constrain attempts to characterize the operations. In current memory research, evidence that the same brain areas are involved in what (...) are often taken to be different memory phenomena or in other cognitive phenomena is playing such a heuristic function. †To contact the author, please write to: Department of Philosophy, 0119, University of California, San Diego, La Jolla, CA 92093‐0119; e‐mail: [email protected]. (shrink)

This paper argues that AI follows classical versions of epistemology in assuming that the identity of the knowing subject is not important. In other words this serves to `delete the subject''. This disguises an implicit hierarchy of knowers involved in the representation of knowledge in AI which privileges the perspective of those who design and build the systems over alternative perspectives. The privileged position reflects Western, professional masculinity. Alternative perspectives, denied a voice, belong to less powerful groups including women. Feminist (...) epistemology can be used to approach this from new directions, in particular, to show how women''s knowledge may be left out of consideration by AI''s focus on masculine subjects. The paper uncovers the tacitly assumed Western professional male subjects in two flagship AI systems, Cyc and Soar. (shrink)

Complete URL to this document: http://srsc.ulb.ac.be/axcWWW/93-Principles.html.

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking (...) into full consideration these desiderata in developing future architectures that are more cognitively and ecologically realistic. A brief and preliminary evaluation of existing cognitive architectures is attempted on the basis of these ideas. (shrink)

Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he failed to demonstrate (...) that the development of Soar, his own candidate architecture, adhered to Lakatosian principles. This paper presents detailed case studies of the development of two cognitive architectures, Soar and ACT-R, from a Lakatosian perspective. It is demonstrated that both are broadly Lakatosian, but that in both cases there have been theoretical progressions that, according to Lakatosian criteria, are pseudo-scientific. Thus, Newell's defense of Soar as a scientific rather than pseudo-scientific theory is not supported in practice. The ACT series of architectures has fewer pseudo-scientific progressions than Soar, but it too is vulnerable to accusations of pseudo-science. From this analysis, it is argued that successive versions of theories of the human cognitive architecture must explicitly address five questions to maintain scientific credibility. (shrink)

In this essay I enter into a recently published debate between Stephen Schiffer and Jerry Fodor concerning whether adequate sense can be made of the ceteris paribus conditions in special science laws, much of their focus being on the case of putative psychological laws. Schiffer argues that adequate sense cannot be made of ceteris paribus clauses, while Fodor attempts to overcome Schiffer's arguments, in defense of special science laws. More recently, Peter Mott has attempted to show that Fodor's response to (...) Schiffer fails, and furthermore that further study shows that the logical framework in which Schiffer and Fodor address their issue is susceptible to inconsistency.In this essay I argue that adequate sense can be made of ceteris paribus conditions. Against Mott, I argue that recent work in the model theory of non-monotonic logic indicates how his problem involving logical inconsistencies can be overcome. Against Schiffer, I argue that the claims that he makes against ceteris paribus clauses would lead to a fatal skepticism concerning indefinitely many of the claims we make about the world (and indeed that his claims would be destructive of the view of the special sciences that Schiffer himself presents in his paper), and that the semantical considerations from non-monotonic logic that I present provide a suitable framework for dealing with his complaints. Thus I come out on the whole on Fodor's side of this debate, although for my own reasons, as I argue against much of Fodor's own argumentation. (shrink)

This chapter investigates the extent to which claims of massive modular organization of the mind (espoused by some members of the evolutionary psychology research program) are consistent with the main elements of the simple heuristics research program. A number of potential sources of conflict between the two programs are investigated and defused. However, the simple heuristics program turns out to undermine one of the main arguments offered in support of massive modularity, at least as the latter is generally understood by (...) philosophers. So one result of the argument will be to force us to re-examine the way in which the notion of modularity in cognitive science should best be characterized, if the thesis of massive modularity isn’t to be abandoned altogether. What is at stake in this discussion, is whether there is a well-motivated notion of ‘module’ such that we have good reason to think that the human mind must be massively modular in its organization. I shall be arguing (in the end) that there is. (shrink)

The central mission of cognitive science is to reveal the real nature of the mind, however familiar or foreign that nature may be to naive preconceptions. The existence of naive conceptions is also important, however. Prescientific thought and language contain concepts of the mental, and these concepts deserve attention from cognitive science. Just as scientific psychology studies folk physics (McCloskey 1983, Hayes 1985), viz., the common understanding (or misunderstanding) of physical phenomena, so it must study folk psychology, the common understanding (...) of mental states. This subfield of scientific psychology is what I mean by the phrase 'the psychology of folk psychology'. (shrink)

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

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...) to be caused by the presence of picturelike representations (mental images) in the mind, soul, or brain, but this is no longer universally accepted. (shrink)

It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and (...) ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons – for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol-structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the “null hypothesis”; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look. (shrink)

Almost all computational models of the mind and brain ignore details about neurotransmitters, hormones, and other molecules. The neglect of neurochemistry in cognitive science would be appropriate if the computational properties of brains relevant to explaining mental functioning were in fact electrical rather than chemical. But there is considerable evidence that chemical complexity really does matter to brain computation, including the role of proteins in intracellular computation, the operations of synapses and neurotransmitters, and the effects of neuromodulators such as hormones. (...) Neurochemical computation has implications for understanding emotions, cognition, and artificial intelligence. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

We present data and argument to show that in Tetris - a real-time interactive video game - certain cognitive and perceptual problems are more quickly, easily, and reliably solved by performing actions in the world rather than by performing computational actions in the head alone. We have found that some translations and rotations are best understood as using the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to (...) change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic actions ñ actions performed to bring one physically closer to a goal - from epistemic actions - actions performed to uncover information that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris-cognition, and show that it cannot explain performance data from human players of the game - even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. However, we describe many such actions that are actually taken by players that are far from superfluous, and that play valuable roles in improving human performance. We argue that traditional accounts are limited because they regard action as having a single function: to change the world. By recognizing a second function of action - an epistemic function - we can explain many of the actions that a traditional model cannot. Although, our argument is supported by numerous examples specifically from Tetris, we outline how the one category of epistemic action can be incorporated into theories of action more generally. (shrink)

The intentional stance is the strategy of prediction and explanation that attributes beliefs, desires, and other states to systems and predicts future behavior from what it would be rational for an agent to do, given those beliefs and desires. Any system whose performance can be thus predicted and explained is an intentional system, whatever its innards. The strategy of treating parts of the world as intentional systems is the foundation of but is also exploited in artificial intelligence and cognitive science (...) more generally, as well as in evolutionary theory. An analysis of the role of the intentional stance and its presuppositions supports a naturalistic theory of mental states and events, their content or intentionality, and the relation between levels of explanation and neurophysiological or mechanistic levels of explanation. As such, the analysis of the intentional stance grounds a theory of the mind and its relation to the body. (shrink)

In Section 5 of his interesting article, Goldman suggests that the consideration of imaginary cases can be valuable in the analysis of our psychological concepts. In particular, he argues that we can imagine a system that is isomorphic to us under any functional description, but which lacks qualitative mental states, such as pains and color sensations. Whether or not such a being is empirically possible, it certainly seems to be logically possible, or conceptually coherent. Goldman argues from this possibility to (...) the conclusion that our concepts of qualitative mental states cannot be analyzed entirely in functional terms. (shrink)

To make progress on the problem of consciousness, we have to confront it directly. In this paper, I first isolate the truly hard part of the problem, separating it from more tractable parts and giving an account of why it is so difficult to explain. I critique some recent work that uses reductive methods to address consciousness, and argue that such methods inevitably fail to come to grips with the hardest part of the problem. Once this failure is recognized, the (...) door to further progress is opened. In the second half of the paper, I argue that if we move to a new kind of nonreductive explanation, a naturalistic account of consciousness can be given. I put forward my own candidate for such an account: a nonreductive theory based on principles of structural coherence and organizational invariance, and a double-aspect theory of information. (shrink)

Starting in the 1950s, computer programs for simulating cognitive processes and intelligent behaviour were the hallmark of Good Old-Fashioned Artificial Intelligence and ‘cognitivist’ cognitive science. This article examines a somewhat neglected case of simulation pursued by one of the founding fathers of simulation methodology, Herbert A. Simon. In the 1970s and 1980s, Simon had repeated contacts with Marxist countries and scientists, in the context of which he advanced the idea that cognitivism could be used as a framework for simulating dialectical (...) materialism. Simon's idea was, in particular, to represent dialectical processes through a ‘symbolic’ version of dialectical logic. This article explores the context of Simon's interaction with Marxist countries—China and the USSR—and also assesses the outcome of the simulation. The difficulty with simulating distinctive features of dialectical materialism is read in light of the underlying assumptions of cognitivism and, ultimately, in light of the attempt to tame a rival world view. (shrink)

In this paper, we propose a new mathematical model of retrieval of intentions from long-term memory. We model retrieval as a stochastic race between a plurality of potentially relevant intentions stored in long-term memory. Psychological theories are dominated by two opposing conceptions of the role of memory in temporally extended agency – as when a person has to remember to make a phone call in the afternoon because, in the morning, she promised she would do so. According to the Working (...) Memory conception, remembering to make the phone call is explained in terms of the construction and maintenance of intentions in working-memory. According to the Long-Term Memory conception, we should explain the episode in terms of an ability to store intentions in long-term memory. The two conceptions predict different processing profiles. The aim of this paper is to present a new mathematical model of the type of memory mechanism that could realise the long-term memory representations of intentions necessary for the Long-Term Memory conception. We present and illustrate the formal model and propose a new type of experimental paradigm that could allow us to test which of the two conceptions provides the best explanation of the role of memory in temporally extended agency. (shrink)

Each day people are presented with circumstances that may require speculation. Scientists may ponder questions such as why a star is born or how rainbows are made, psychologists may ask social questions such as why people are prejudiced, and military strategists may imagine what the consequences of their actions might be. Speculations may lead to the generation of putative explanations called hypotheses. But it is by checking if hypotheses accurately reflect the encountered facts that lead to sensible behaviour demonstrating a (...) true understanding. If evidence shows a hypothesis to be false, then people should rationally abandon it, especially if there are negative consequences. The aim of this thesis is to examine how effectively people search for evidence in their hypothesis testing to test whether or not their hypotheses are true or false in competitive games. -/- Research findings from six studies of hypothesis testing behaviour in competitive deductive tasks are explored. Chapter by chapter the thesis tests how everyday people, and master chess players, tackle hypothesis testing in mathematical tasks, such as how to solve sequential number sequence puzzles when thinking about an opponent, or how to solve chess problems in a variety of contexts. The implications of the results are discussed in light of aspects of general cognition: such as reasoning, social hypothesis testing and planning. (shrink)

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach (...) to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach. (shrink)

In recent years, the growing scientific interest in design has led to great advances in our knowledge of authentic design processes. However, as these findings go counter to the existing theories in both design research and cognitive science, they pose a serious challenge for both disciplines: there is a wide gap between what the existing theories predict and what designers actually do. At the same time, there is a growing movement of research on authentic cognitive activities, which has among other (...) things documented the central roles of action and the physical environment in these activities, something that existing cognitive theories have overlooked and cannot properly account for. This creates an explanatory gap analogous to the one found in design. This book aims to fill both of these gaps with a cognitive theory of how designers work. It revolves around the roles of physical activities and working materials in design, and the theory explains at length how these have functions that are essential to cognition. The two threads of design and cognition run in parallel throughout the book: the cognitive theory is applied to design, but is also consistently related to cognition in general. The result is, in back cover text parlance, a 'provocative' account of cognition and human performance, which should be of interest to cognitive scientists as well as to design researchers. (shrink)

The present study describes the mechanism of need regulation, which accompanies the so-called “biased” decisions. We hypothesized an unconscious urge for psychological need satisfaction as the trigger for cognitive biases. In an experimental study (N = 106), participants had the opportunity to win money in a functionality test. In the test, they could either use the solution they had developed (sunk cost) or an alternative solution that offered a higher probability of winning. The selection of the sunk-cost option was the (...) most chosen option, supporting the hypothesis of this study. The reason behind the majority of participants choosing the sunk-cost option seemed to be the satisfaction of psychological needs, despite a reduced chance of winning money. An intervention, which aimed at triggering self-reflection, had no impact on the decision. The findings of this study contribute to the discussion on the reasons for cognitive biases and their formation in the human mind. Moreover, it discusses the application of the label “irrational” for biased decisions and proposes reasons for instrumental rationality, which exist at an unconscious, need-regulative level. (shrink)

The question “What is the nature of experience?” is of perennial philosophical concern. It deals not only with the nature of experience qua experience, but additionally with related questions about the experiencing subject and that which is experienced. In other words, to speak of the philosophical problem of experience, one must also address questions about mind, world, and the various relations that link them together. Both William James and Kitarō Nishida were deeply concerned with these issues. Their shared notion of (...) “pure experience” is the conceptual cornerstone of their attempt to deal with the philosophical problem of experience. This dissertation is an analysis of “pure experience” and its relevance to several issues in contemporary hilosophy of mind. Drawing upon James’s and Nishida’s “pure experience”, I argue both for a sensorimotor-based, “extended” conception of consciousness and a bodily skills-based account of moral psychology. (shrink)