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Shimon Edelman
Cornell University
  1. Computing the Mind: How the Mind Really Works.Shimon Edelman - 2008 - 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. Representation is Representation of Similarities.Shimon Edelman - 1998 - 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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  3.  73
    Representation, Similarity, and the Chorus of Prototypes.Shimon Edelman - 1995 - 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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  4.  90
    General Cognitive Principles for Learning Structure in Time and Space.Michael H. Goldstein, Heidi R. Waterfall, Arnon Lotem, Joseph Y. Halpern, Jennifer A. Schwade, Luca Onnis & Shimon Edelman - 2010 - Trends in Cognitive Sciences 14 (6):249-258.
  5.  29
    Learn Locally, Act Globally: Learning Language From Variation Set Cues.Luca Onnis, Heidi R. Waterfall & Shimon Edelman - 2008 - Cognition 109 (3):423.
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  6. System, Subsystem, Hive: Boundary Problems in Computational Theories of Consciousness.Tomer Fekete, Cees van Leeuwen & Shimon Edelman - 2016 - Frontiers in Psychology 7.
    A computational theory of consciousness should include a quantitative measure of consciousness, or MoC, that (i) would reveal to what extent a given system is conscious, (ii) would make it possible to compare not only different systems, but also the same system at different times, and (iii) would be graded, because so is consciousness. However, unless its design is properly constrained, such an MoC gives rise to what we call the boundary problem: an MoC that labels a system as conscious (...)
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  7.  48
    On the Nature of Minds, Or: Truth and Consequences.Shimon Edelman - 2008 - 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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  8.  89
    Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2014 - 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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  9.  64
    Towards a Computational Theory of Experience.Tomer Fekete & Shimon Edelman - 2011 - 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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  10.  12
    Dynamical Emergence Theory (DET): A Computational Account of Phenomenal Consciousness.Roy Moyal, Tomer Fekete & Shimon Edelman - 2020 - Minds and Machines 30 (1):1-21.
    Scientific theories of consciousness identify its contents with the spatiotemporal structure of neural population activity. We follow up on this approach by stating and motivating Dynamical Emergence Theory, which defines the amount and structure of experience in terms of the intrinsic topology and geometry of a physical system’s collective dynamics. Specifically, we posit that distinct perceptual states correspond to coarse-grained macrostates reflecting an optimal partitioning of the system’s state space—a notion that aligns with several ideas and results from computational neuroscience (...)
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  11.  59
    How Seriously Should We Take Minimalist Syntax?Shimon Edelman & Morten H. Christiansen - 2003 - Trends in Cognitive Sciences 7 (2):60-61.
  12.  56
    Constraining the Neural Representation of the Visual World.Shimon Edelman - 2002 - Trends in Cognitive Sciences 6 (3):125-131.
  13.  51
    Towards Structural Systematicity in Distributed, Statically Bound Visual Representations.Shimon Edelman & Nathan Intrator - 2003 - 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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  14.  48
    Bridging Computational, Formal and Psycholinguistic Approaches to Language.Shimon Edelman - unknown
    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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  15.  40
    Generalization to Novel Images in Upright and Inverted Faces.Shimon Edelman - unknown
    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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  16. Computational Theories of Object Recognition.Shimon Edelman - 1997 - Trends in Cognitive Sciences 1 (8):296-304.
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  17.  34
    Automatic Acquisition and Efficient Representation of Syntactic Structures.Shimon Edelman - unknown
    The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic structures from corpus data. We extend this principle by applying it recursively, and by using mutual information for estimating category coherence. The resulting model learns, in an unsupervised fashion, highly structured, distributed representations of syntactic knowledge from corpora. It also exhibits promising behavior in tasks usually thought to require representations anchored (...)
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  18.  55
    How Seriously Should We Take Minimalist Syntax?Shimon Edelman - 2003 - Trends in Cognitive Sciences 7 (2):60-61.
    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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  19.  29
    The (Lack of) Mental Life of Some Machines.Tomer Fekete & Shimon Edelman - 2012 - In Shimon Edelman, Tomer Fekete & Neta Zach (eds.), Being in Time: Dynamical Models of Phenomenal Experience. John Benjamins.. pp. 88--95.
    The proponents of machine consciousness predicate the mental life of a machine, if any, exclusively on its formal, organizational structure, rather than on its physical composition. Given that matter is organized on a range of levels in time and space, this generic stance must be further constrained by a principled choice of levels on which the posited structure is supposed to reside. Indeed, not only must the formal structure fit well the physical system that realizes it, but it must do (...)
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  20.  45
    Unsupervised Learning of Visual Structure.Shimon Edelman - unknown
    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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  21.  43
    Unsupervised Efficient Learning and Representation of Language Structure.Shimon Edelman - unknown
    We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively structured constituent patterns, and by placing strings that have an identical backbone and similar context structure into the same equivalence class. The resulting representations constitute an efficient encoding of linguistic knowledge and support systematic generalization to unseen sentences.
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  22.  39
    Characterizing Motherese: On the Computational Structure of Child-Directed Language.Shimon Edelman - unknown
    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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  23.  29
    Unsupervised Context Sensitive Language Acquisition From a Large Corpus.Shimon Edelman - unknown
    We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learning structured knowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm represents sentences as paths on a graph whose vertices are words. Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are represented by trees composed of significant patterns and (...)
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  24.  13
    Juvenile Zebra Finches Learn the Underlying Structural Regularities of Their Fathers’ Song.Otília Menyhart, Oren Kolodny, Michael H. Goldstein, Timothy J. DeVoogd & Shimon Edelman - 2015 - Frontiers in Psychology 6.
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  25.  7
    Towards Structural Systematicity in Distributed, Statically Bound Visual Representations.Shimon Edelman & Nathan Intrator - 2003 - Cognitive Science 27 (1):73-109.
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  26.  22
    A Productive, Systematic Framework for the Representation of Visual Structure.Shimon Edelman - unknown
    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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  27.  23
    But Will It Scale Up? Not Without Representations.Shimon Edelman - 2003 - Adaptive Behavior 11:273-275.
  28.  35
    Being in Time.Shimon Edelman & Tomer Fekete - 2012 - In Shimon Edelman, Tomer Fekete & Neta Zach (eds.), Being in Time: Dynamical Models of Phenomenal Experience. John Benjamins. pp. 88--81.
  29.  56
    On Look-Ahead in Language: Navigating a Multitude of Familiar Paths.Shimon Edelman - unknown
    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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  30.  71
    Being in Time: Dynamical Models of Phenomenal Experience.Shimon Edelman, Tomer Fekete & Neta Zach (eds.) - 2012 - John Benjamins.
    The chapters comprising this book represent a collective attempt on the part of their authors to redress this aberration.
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  31.  27
    Neural Spaces: A General Framework for the Understanding of Cognition?Shimon Edelman - 2001 - 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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  32.  51
    Trade-off Between Capacity and Generalization in a Model of Memory.Shimon Edelman - unknown
    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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  33.  30
    Vision, Reanimated and Reimagined.Shimon Edelman - unknown
    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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  34.  81
    How Representation Works is More Important Than What Representations Are.Shimon Edelman - 1995 - Behavioral and Brain Sciences 18 (4):630-631.
  35.  28
    Viewpoint Generalization in Face Recognition: The Role of Category-Speci C Processes.Shimon Edelman - unknown
    The statistical structure of a class of objects such as human faces can be exploited to recognize familiar faces from novel viewpoints and under variable illumination conditions. We present computational and psychophysical data concerning the extent to which class-based learning transfers or generalizes within the class of faces. We rst examine the computational prerequisite for generalization across views of novel faces, namely, the similarity of di erent faces to each other. We next describe two computational models which exploit the similarity (...)
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  36. Mostly Harmless: Review of Action in Perception by Alva Noë. [REVIEW]Shimon Edelman - 2006 - Artificial Life 12:183-186.
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  37. Similarity-Based Viewspace Interpolation and the Categorization of 3D Objects.Shimon Edelman & Sharon Duvdevani-Bar - 1997 - 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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  38.  18
    Vision Reanimated.Shimon Edelman - unknown
    Computer vision systems are, on most counts, poor performers, when compared to their biological counterparts. The reason for this may be that computer vision is handicapped by an unreasonable assumption regarding what it means to see, which became prevalent as the notions of intrinsic images and of representation by reconstruction took over the field in the late 1970’s. Learning from biological vision may help us to overcome this handicap.
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  39.  17
    The Neglected Universals: Learnability Constraints and Discourse Cues.Heidi Waterfall & Shimon Edelman - 2009 - Behavioral and Brain Sciences 32 (5):471-472.
    Converging findings from English, Mandarin, and other languages suggest that observed may be algorithmic. First, computational principles behind recently developed algorithms that acquire productive constructions from raw texts or transcribed child-directed speech impose family resemblance on learnable languages. Second, child-directed speech is particularly rich in statistical (and social) cues that facilitate learning of certain types of structures.
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  40.  60
    Learning Syntactic Constructions From Raw Corpora.Shimon Edelman - unknown
    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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  41.  88
    Complex Cells and Object Recognition.Shimon Edelman - unknown
    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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  42.  56
    Competitive Learning in Biological and Artificial Neural Computation.Nathan Intrator & Shimon Edelman - 1997 - Trends in Cognitive Sciences 1 (7):268-272.
  43.  56
    Evolution of Dynamic Coordination.Shimon Edelman & Erich D. Jarvis - unknown
    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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  44.  22
    Better Limited Systematicity in Hand Than Structural Descriptions in the Bush: A Reply to Hummel.Shimon Edelman & Nathan Intrator - 2003 - Cognitive Science 27 (2):331-332.
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  45.  75
    The Metaphysics of Embodiment.Shimon Edelman - 2011 - 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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  46.  52
    Visual Processing of Object Structure.Shimon Edelman & Nathan Intrator - 2002 - In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press.
  47.  46
    Similarity-Based Word Sense Disambiguation.Shimon Edelman - unknown
    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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  48.  66
    (Object Recognition/Multidimensional Scaling/Computational Model).Shimon Edelman - unknown
    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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  49.  42
    Representing 3D Ob Jects by Sets of Activities of Receptiv E Elds.Shimon Edelman - unknown
    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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  50.  11
    Journal of The Cognitive Science Society.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 - Cognitive Science 28 (3).
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