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Profile: Shimon Edelman (Cornell University)
  1.  22
    Shimon Edelman (2008). Computing the Mind: How the Mind Really Works. Oxford University Press.
    The account that Edelman gives in this book is accessible, yet unified and rigorous, and the big picture he presents is supported by evidence ranging from ...
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  2.  44
    Shimon Edelman (1995). Representation, Similarity, and the Chorus of Prototypes. Minds and Machines 5 (1):45-68.
    It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined concept of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in (...)
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  3.  4
    Luca Onnis, Heidi R. Waterfall & Shimon Edelman (2008). Learn Locally, Act Globally: Learning Language From Variation Set Cues. Cognition 109 (3):423.
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  4.  8
    Michael H. Goldstein, Heidi R. Waterfall, Arnon Lotem, Joseph Y. Halpern, Jennifer A. Schwade, Luca Onnis & Shimon Edelman (2010). General Cognitive Principles for Learning Structure in Time and Space. Trends in Cognitive Sciences 14 (6):249-258.
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  5.  45
    Shimon Edelman (1998). Representation is Representation of Similarities. Behavioral and Brain Sciences 21 (4):449-467.
    Intelligent systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identification of specific instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus in a (...)
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  6.  25
    Shimon Edelman (2008). On the Nature of Minds, Or: Truth and Consequences. Journal of Experimental and Theoretical Ai 20:181-196.
    Are minds really dynamical or are they really symbolic? Because minds are bundles of computations, and because computation is always a matter of interpretation of one system by another, minds are necessarily symbolic. Because minds, along with everything else in the universe, are physical, and insofar as the laws of physics are dynamical, minds are necessarily dynamical systems. Thus, the short answer to the opening question is “yes.” It makes sense to ask further whether some of the computations that constitute (...)
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  7.  15
    Shimon Edelman & Morten H. Christiansen (2003). How Seriously Should We Take Minimalist Syntax? Trends in Cognitive Sciences 7 (2):60-61.
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  8.  24
    Tomer Fekete & Shimon Edelman (2011). Towards a Computational Theory of Experience. Consciousness and Cognition 20 (3):807-827.
    A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must (...)
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  9.  17
    Shimon Edelman & Nathan Intrator (2003). Towards Structural Systematicity in Distributed, Statically Bound Visual Representations. Cognitive Science 23 (1):73-110.
    The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships. We argue that the main desiderata of a representational system — productivity and systematicity — can (indeed, for a number of reasons, should) be achieved without recourse to (...)
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  10.  27
    Shimon Edelman, Tomer Fekete & Neta Zach (eds.) (2012). Being in Time: Dynamical Models of Phenomenal Experience. John Benjamins Pub. Co..
    The chapters comprising this book represent a collective attempt on the part of their authors to redress this aberration.
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  11.  12
    Shimon Edelman (2002). Constraining the Neural Representation of the Visual World. Trends in Cognitive Sciences 6 (3):125-131.
  12.  7
    Shimon Edelman, A Productive, Systematic Framework for the Representation of Visual Structure.
    We describe a unified framework for the understanding of structure representation in primate vision. A model derived from this framework is shown to be effectively systematic in that it has the ability to interpret and associate together objects that are related through a rearrangement of common “middle-scale” parts, represented as image fragments. The model addresses the same concerns as previous work on compositional representation through the use of what+where receptive fields and attentional gain modulation. It does not require prior exposure (...)
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  13.  21
    Shimon Edelman, Unsupervised Learning of Visual Structure.
    To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation. Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the “what+where” receptive fields found in the ventral visual stream in primates. We describe a (...)
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  14.  26
    Shimon Edelman, Similarity-Based Word Sense Disambiguation.
    We describe a method for automatic word sense disambiguation using a text corpus and a machine- readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous (...)
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  15.  34
    Shimon Edelman & Nathan Intrator (2002). Visual Processing of Object Structure. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press
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  16.  23
    Shimon Edelman, On Look-Ahead in Language: Navigating a Multitude of Familiar Paths.
    Language is a rewarding field if you are in the prediction business. A reader who is fluent in English and who knows how academic papers are typically structured will readily come up with several possible guesses as to where the title of this section could have gone, had it not been cut short by the ellipsis. Indeed, in the more natural setting of spoken language, anticipatory processing is a must: performance of machine systems for speech interpretation depends critically on the (...)
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  17.  19
    Shimon Edelman, Representing 3D Ob Jects by Sets of Activities of Receptiv E Elds.
    Idealized mo dels of receptive elds (RFs) can be used as building blocks for the creation of p owerful distributed computation systems. The present rep ort concentrates on inv estigating the utility of collections of RFs in representing 3D objects under changing viewing conditions. The main requirement in this task is that the pattern of activity of RFs vary as little as p ossible when the object and the camera move relative to each other. I propose a method for representing (...)
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  18.  24
    Shimon Edelman (1997). Computational Theories of Object Recognition. Trends in Cognitive Sciences 1 (8):296-304.
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  19.  26
    Shimon Edelman & Erich D. Jarvis, Evolution of Dynamic Coordination.
    What insights does comparative biology provide for furthering scienti¿ c understanding of the evolution of dynamic coordination? Our discussions covered three major themes: (a) the fundamental unity in functional aspects of neurons, neural circuits, and neural computations across the animal kingdom; (b) brain organization –behavior relationships across animal taxa; and (c) the need for broadly comparative studies of the relationship of neural structures, neural functions, and behavioral coordination. Below we present an overview of neural machinery and computations that are shared (...)
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  20.  8
    Shimon Edelman (2001). Neural Spaces: A General Framework for the Understanding of Cognition? Behavioral and Brain Sciences 24 (4):664-665.
    A view is put forward, according to which various aspects of the structure of the world as internalized by the brain take the form of “neural spaces,” a concrete counterpart for Shepard's “abstract” ones. Neural spaces may help us understand better both the representational substrate of cognition and the processes that operate on it. [Shepard].
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  21.  15
    Shimon Edelman, Learning Syntactic Constructions From Raw Corpora.
    Construction-based approaches to syntax (Croft, 2001; Goldberg, 2003) posit a lexicon populated by units of various sizes, as envisaged by (Langacker, 1987). Constructions may be specified completely, as in the case of simple morphemes or idioms such as take it to the bank, or partially, as in the expression what’s X doing Y?, where X and Y are slots that admit fillers of particular types (Kay and Fillmore, 1999). Constructions offer an intriguing alternative to traditional rule-based syntax by hinting at (...)
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  22.  12
    Shimon Edelman (2003). But Will It Scale Up? Not Without Representations. Adaptive Behavior 11:273-275.
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  23.  14
    Shimon Edelman (1995). How Representation Works is More Important Than What Representations Are. Behavioral and Brain Sciences 18 (4):630.
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  24.  6
    Shimon Edelman & Nathan Intrator (2003). Better Limited Systematicity in Hand Than Structural Descriptions in the Bush: A Reply to Hummel. Cognitive Science 27 (2):331-332.
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  25. Shimon Edelman (2008). On What It Means to See, and What We Can Do About It. In S. Dickinson, A. Leonardis, B. Schiele & M. J. Tarr (eds.), Object Categorization: Computer and Human Vision Perspectives. Cambridge University Press
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  26.  37
    Shimon Edelman, Complex Cells and Object Recognition.
    Nearest-neighbor correlation-based similarity computation in the space of outputs of complex-type receptive elds can support robust recognition of 3D objects. Our experiments with four collections of objects resulted in mean recognition rates between 84% and 94%, over a 40 40 range of viewpoints, centered on a stored canonical view and related to it by rotations in depth. This result has interesting implications for the design of a front end to an arti cial object recognition system, and for the understanding of (...)
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  27.  13
    Oren Kolodny, Arnon Lotem & Shimon Edelman (2014). Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition. Cognitive Science 38 (4):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  28.  33
    Shimon Edelman, (Object Recognition/Multidimensional Scaling/Computational Model).
    differentiaily rated pairwise similarity when confronted with two pairs of objects, each revolving in a separate window on a computer screen. Subject data were pooled using individually weighted MDS (ref. 11; in all the experiments, the solutions were consistent among subjects). In each trial, the subject had to select among two pairs of shapes the one consisting of the most similar shapes. The subjects were allowed to respond at will; most responded within 10 sec. Proximity (that is, perceived similarity) tables (...)
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  29.  2
    Robert L. Goldstone, John R. Anderson, Nick Chater, Andy Clark, Shimon Edelman, Kenneth Forbus, Dedre Gentner, Raymond W. Gibbs Jr, James Greeno & Robert A. Jacobs (2004). Journal of The Cognitive Science Society. Cognitive Science 28 (3).
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  30.  10
    Shimon Edelman & Tomer Fekete (2012). Being in Time. In Shimon Edelman, Tomer Fekete & Neta Zach (eds.), Being in Time: Dynamical Models of Phenomenal Experience. John Benjamins 88--81.
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  31.  22
    Shimon Edelman, Vision, Reanimated and Reimagined.
    The publication in 1982 of David Marr’s Vision has delivered a singular boost and a course correction to the science of vision. Thirty years later, cognitive science is being transformed by the new ways of thinking about what it is that the brain computes, how it does that, and, most importantly, why cognition requires these computations and not others. This ongoing process still owes much of its impetus and direction to the sound methodology, engaging style, and unique voice of Marr’s (...)
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  32.  2
    Michael Ramscar, Daniel Yarlett, Shimon Edelman, Nathan Intrator, Gergely Csibra, Szilvia Bıró, Orsolya Koós, György Gergely, Holk Cruse & Michael D. Lee (2003). Regular Articles Learning to Divide the Labor: An Account of Deficits in Light and Heavy Verb Production 1 Jean K. Gordon, Gary S. Dell Semantic Grounding in Models of Analogy: An Environmental Approach 41. Cognitive Science 27:945-948.
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  33.  14
    Shimon Edelman, Some Tests of an Unsupervised Model of Language Acquisition.
    We outline an unsupervised language acquisition algorithm and offer some psycholinguistic support for a model based on it. Our approach resembles the Construction Grammar in its general philosophy, and the Tree Adjoining Grammar in its computational characteristics. The model is trained on a corpus of transcribed child-directed speech (CHILDES). The model’s ability to process novel inputs makes it capable of taking various standard tests of English that rely on forced-choice judgment and on magnitude estimation of linguistic acceptability. We report encouraging (...)
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  34.  29
    Shimon Edelman, Unsupervised Statistical Learning in Vision: Computational Principles, Biological Evidence.
    Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey (...)
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  35.  27
    Shimon Edelman (2011). The Metaphysics of Embodiment. International Journal of Machine Consciousness 3 (02):321-.
    Shanahan’s eloquently argued version of the global workspace theory fits well into the emerging understanding of consciousness as a computational phenomenon. His disinclination toward metaphysics notwithstanding, Shanahan’s book can also be seen as supportive of a particular metaphysical stance on consciousness — the computational identity theory.
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  36.  26
    Shimon Edelman, Bridging Computational, Formal and Psycholinguistic Approaches to Language.
    We compare our model of unsupervised learning of linguistic structures, ADIOS [1, 2, 3], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction Grammar in its general philosophy (e.g., in its reliance on structural generalizations rather than on syntax projected by the lexicon, as in the current generative theories), and the Tree Adjoining Grammar in its computational characteristics (e.g., in its apparent affinity with Mildly Context Sensitive Languages). The representations learned by our algorithm (...)
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  37.  20
    Shimon Edelman, How Seriously Should We Take Minimalist Syntax? A Comment on Lasnik.
    Lasnik’s review of the Minimalist program in syntax [1] offers cognitive scientists help in navigating some of the arcana of the current theoretical thinking in transformational generative grammar. One may observe, however, that this journey is more like a taxi ride gone bad than a free tour: it is the driver who decides on the itinerary, and questioning his choice may get you kicked out. Meanwhile, the meter in the cab of the generative theory of grammar is running, and has (...)
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  38.  12
    Shimon Edelman, An Evolved Model Agent by R. Beer.
    Beer ’s paper devotes much energy to buttressing the walls of Castle Dynamic and dredging its moat in the face of what some of its dwellers perceive as a besieging army chanting “no cognition without representation”. The divide is real, as attested by the contrast between titles such as “Intelligence without representation” and “In defense of representation”, to pick just one example from each side. It is, however, not too late for people from both sides of the moat to meet (...)
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  39.  12
    Shimon Edelman, Learn the Source and Target Languages: (A) Learn a Grammar GA for the Source Language (A). (B) Estimate a Structural Statistical Language Model SSLMA for (A). Given a Grammar (Consisting Of..). [REVIEW]
    (a) Learn a grammar GA for the source language (A). (b) Estimate a structural statistical language model SSLMA for (A). Given a grammar (consisting of terminals and nonterminals) and a partial sentence (sequence of terminals (t1 . . . ti)), an SSLM assigns probabilities to the possible choices of the next terminal ti+1.
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  40.  18
    Shimon Edelman, Http://Kybele.Psych.Cornell.Edu/∼Edelman.
    The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of (...)
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  41.  24
    Shimon Edelman, Evolution of Language Diversity: The Survival of the Fitness.
    We examined the role of fitness, commonly assumed without proof to be conferred by the mastery of language, in shaping the dynamics of language evolution. To that end, we introduced island migration (a concept borrowed from population genetics) into the shared lexicon model of communication (Nowak et al., 1999). The effect of fitness linear in language coherence was compared to a control condition of neutral drift. We found that in the neutral condition (no coherence-dependent fitness) even a small migration rate (...)
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  42.  10
    Shimon Edelman (2014). How to Write a ‘How-to-Build-a-Brain’ Book. Trends in Cognitive Sciences 18 (3):118-119.
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  43.  20
    Shimon Edelman, Trade-off Between Capacity and Generalization in a Model of Memory.
    Although computational considerations suggest that a resource-limited memory system may have to trade off capacity for generalization ability, such a trade-off has not been demonstrated in the past. We describe a simple model of memory that exhibits this trade-off and describe its performance in a variety of tasks.
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  44.  23
    Shimon Edelman, A New Vision of Language.
    A metaphor that has dominated linguistics for the entire duration of its existence as a discipline views sentences as edifices consisting of Lego-like building blocks. It is assumed that each sentence is constructed (and, on the receiving end, parsed) ab novo, starting (ending) with atomic constituents, to logical semantic specifications, in a recursive process governed by a few precise algebraic rules. The assumptions underlying the Lego metaphor, as it is expressed in generative grammar theories, are: (1) perfect regularity of what (...)
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  45. Shimon Edelman & Sharon Duvdevani-Bar (1997). Similarity-Based Viewspace Interpolation and the Categorization of 3D Objects. In Proc. Edinburgh Workshop on Similarity and Categorization.
    Visual objects can be represented by their similarities to a small number of reference shapes or prototypes. This method yields low-dimensional (and therefore computationally tractable) representations, which support both the recognition of familiar shapes and the categorization of novel ones. In this note, we show how such representations can be used in a variety of tasks involving novel objects: viewpoint-invariant recognition, recovery of a canonical view, estimation of pose, and prediction of an arbitrary view. The unifying principle in all these (...)
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  46.  20
    Shimon Edelman, In Theoretical and Philosophical Psychology.
    By what empirical means can a person determine whether he or she is presently awake or dreaming? Any conceivable test addressing this question, which is a special case of the classical metaphysical doubting of reality, must be statistical (for the same reason that empirical science is, as noted by Hume). Subjecting the experienced reality to any kind of statistical test (for instance, a test for bizarreness) requires, however, that a set of baseline measurements be available. In a dream, or in (...)
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  47.  17
    Shimon Edelman, Characterizing Motherese: On the Computational Structure of Child-Directed Language.
    We report a quantitative analysis of the cross-utterance coordination observed in child-directed language, where successive utterances often overlap in a manner that makes their constituent structure more prominent, and describe the application of a recently published unsupervised algorithm for grammar induction to the largest available corpus of such language, producing a grammar capable of accepting and generating novel wellformed sentences. We also introduce a new corpus-based method for assessing the precision and recall of an automatically acquired generative grammar without recourse (...)
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  48.  26
    Shimon Edelman & Elise M. Breen (1999). On the Virtues of Going All the Way. Behavioral and Brain Sciences 22 (4):614-614.
    Representational systems need to use symbols as internal stand-ins for distal quantities and events. Barsalou's ideas go a long way towards making the symbol system theory of representation more appealing, by delegating one critical part of the representational burden to image-like entities. The target article, however, leaves the other critical component of any symbol system theory underspecified. We point out that the binding problem can be alleviated if a perceptual symbol system is made to rely on image-like entities not only (...)
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  49.  17
    Shimon Edelman, Generalization to Novel Images in Upright and Inverted Faces.
    An image of a face depends not only on its shape, but also on the viewpoint, illumination conditions, and facial expression. A face recognition system must overcome the changes in face appearance induced by these factors. This paper investigate two related questions: the capacity of the human visual system to generalize the recognition of faces to novel images, and the level at which this generalization occurs. We approach this problems by comparing the identi cation and generalization capacity for upright and (...)
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  50.  15
    Shimon Edelman (2008). A Swan, and Pike, and a Crawfish Walk Into a Bar. Journal of Experimental and Theoretical Ai 20:261-268.
    The three commentaries of Van Orden, Spivey and Anderson, and Dietrich (with Markman’s as a backdrop) form a tableau that reminds me of a fable by Ivan Andreevich Krylov (1769 - 1844), in which a swan, a pike, and a crawfish undertake jointly to move a cart laden with goods. What transpires then is not unexpected: the swan strives skyward, the pike pulls toward the river, and the crawfish scrambles backward. The call for papers for the present ecumenically minded special (...)
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