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Copycat is a model of analogy making and human cognition based on the concept of the parallel terraced scan, developed in 1988 by Douglas Hofstadter, Melanie Mitchell, and others at the Center for Research on Concepts and Cognition, Indiana University Bloomington.The original Copycat was written in Common Lisp and is bitrotten (as it relies on now-outdated graphics libraries for Lucid Common Lisp); however, Java and Python ports exist. The latest version in 2018 is a Python3 port by Lucas Saldyt and J. Alan Brogan.
Copycat produces answers to such problems as "abc is to abd as ijk is to what?" (abc:abd :: ijk:?). Hofstadter and Mitchell consider analogy making as the core of high-level cognition, or high-level perception, as Hofstadter calls it, basic to recognition and categorization. High-level perception emerges from the spreading activity of many independent processes, called codelets, running in parallel, competing or cooperating. They create and destroy temporary perceptual constructs, probabilistically trying out variations to eventually produce an answer. The codelets rely on an associative network, slipnet, built on pre-programmed concepts and their associations (a long-term memory). The changing activation levels of the concepts make a conceptual overlap with neighboring concepts.
Copycat's architecture is tripartite, consisting of a slipnet, a working area (also called workspace, similar to blackboard systems), and the coderack (with the codelets). The slipnet is a network composed of nodes, which represent permanent concepts, and weighted links, which are relations, between them. It differs from traditional semantic networks, since the effective weight associated with a particular link may vary through time according to the activation level of specific concepts (nodes). The codelets build structures in the working area and modify activations in the slipnet accordingly (bottom-up processes), and the current state of slipnet determines probabilistically which codelets must be run (top-down influences).
Copycat differs considerably in many respects from other cognitive architectures such as ACT-R, Soar, DUAL, Psi, or subsumption architectures.
Copycat is Hofstadter's most popular model. Other models presented by Hofstadter et al. are similar in architecture, but different in the so-called microdomain, their application, e.g. Letter Spirit, etc.
Since the 1995 book Fluid Concepts and Creative Analogies describing the work of the Fluid Analogies Research Group (FARG) book, work on Copycat-like models has continued: as of 2008 the latest models are Phaeaco (a Bongard problem solver), SeqSee (number sequence extrapolation), George (geometric exploration), and Musicat (a melodic expectation model). The architecture is known as the "FARGitecture" and current implementations use a variety of modern languages including C# and Java. A future FARG goal is to build a single generic FARGitecture software framework to facilitate experimentation.
Douglas Richard Hofstadter is an American scholar of cognitive science, physics, and comparative literature whose research includes concepts such as the sense of self in relation to the external world, consciousness, analogy-making, artistic creation, literary translation, and discovery in mathematics and physics. His 1979 book Gödel, Escher, Bach: An Eternal Golden Braid won both the Pulitzer Prize for general nonfiction and a National Book Award for Science. His 2007 book I Am a Strange Loop won the Los Angeles Times Book Prize for Science and Technology.
The ELIZA effect, in computer science, is the tendency to unconsciously assume computer behaviors are analogous to human behaviors; that is, anthropomorphisation.
Analogy is a cognitive process of transferring information or meaning from a particular subject to another, or a linguistic expression corresponding to such a process. In a narrower sense, analogy is an inference or an argument from one particular to another particular, as opposed to deduction, induction, and abduction, in which at least one of the premises, or the conclusion, is general rather than particular in nature. The term analogy can also refer to the relation between the source and the target themselves, which is often a similarity, as in the biological notion of analogy.
Semantic memory is one of the two types of explicit memory. Semantic memory refers to general world knowledge that we have accumulated throughout our lives. This general knowledge is intertwined in experience and dependent on culture. Semantic memory is distinct from episodic memory, which is our memory of experiences and specific events that occur during our lives, from which we can recreate at any given point. For instance, semantic memory might contain information about what a cat is, whereas episodic memory might contain a specific memory of petting a particular cat. We can learn about new concepts by applying our knowledge learned from things in the past. The counterpart to declarative or explicit memory is nondeclarative memory or implicit memory.
Soar is a cognitive architecture, originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University. It is now maintained and developed by John Laird's research group at the University of Michigan.
ACT-R is a cognitive architecture mainly developed by John Robert Anderson and Christian Lebiere at Carnegie Mellon University. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind. In theory, each task that humans can perform should consist of a series of these discrete operations.
A Bongard problem is a kind of puzzle invented by the Russian computer scientist Mikhail Moiseevich Bongard, probably in the mid-1960s. They were published in his 1967 book on pattern recognition. The objective is to spot the differences between the two sides. Bongard, in the introduction of the book credits the ideas in it to a group including M. N. Vaintsvaig, V. V. Maksimov, and M. S. Smirnov.
A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. One of the main goals of a cognitive architecture is to summarize the various results of cognitive psychology in a comprehensive computer model. However, the results need to be formalized so far as they can be the basis of a computer program. The formalized models can be used to further refine a comprehensive theory of cognition, and more immediately, as a commercially usable model. Successful cognitive architectures include ACT-R and SOAR.
Egbert B. Gebstadter is a fictional author who appears in the indexes of books by Douglas R. Hofstadter. For each Hofstadter book, there is a corresponding Gebstadter book. His name is derived from "GEB", the abbreviation for Hofstadter's first book Gödel, Escher, Bach: An Eternal Golden Braid; the letters appear in his last name, permuted in his first name, and permuted again in his initials.
Melanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.
The following outline is provided as an overview of and topical guide to thought (thinking):
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought is a 1995 book by Douglas Hofstadter and other members of the Fluid Analogies Research Group exploring the mechanisms of intelligence through computer modeling. It contends that the notions of analogy and fluidity are fundamental to explain how the human mind solves problems and to create computer programs that show intelligent behavior. It analyzes several computer programs that members of the group have created over the years to solve problems that require intelligence.
Psi-theory, developed by Dietrich Dörner at the University of Bamberg, is a systemic psychological theory covering human action regulation, intention selection and emotion. It models the human mind as an information processing agent, controlled by a set of basic physiological, social and cognitive drives. Perceptual and cognitive processing are directed and modulated by these drives, which allow the autonomous establishment and pursuit of goals in an open environment.
Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS). This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
The LIDA cognitive architecture is an integrated artificial cognitive system that attempts to model a broad spectrum of cognition in biological systems, from low-level perception/action to high-level reasoning. Developed primarily by Stan Franklin and colleagues at the University of Memphis, the LIDA architecture is empirically grounded in cognitive science and cognitive neuroscience. In addition to providing hypotheses to guide further research, the architecture can support control structures for software agents and robots. Providing plausible explanations for many cognitive processes, the LIDA conceptual model is also intended as a tool with which to think about how minds work.
Cognitive musicology is a branch of cognitive science concerned with computationally modeling musical knowledge with the goal of understanding both music and cognition.
Robert M. French is a research director at the French National Centre for Scientific Research. He is currently at the University of Burgundy in Dijon. He holds a Ph.D. from the University of Michigan, where he worked with Douglas Hofstadter on the Tabletop computational cognitive model. He specializes in cognitive science and has made an extensive study of the process of analogy-making.
Secondary consciousness is an individual's accessibility to their history and plans. The ability allows its possessors to go beyond the limits of the remembered present of primary consciousness. Primary consciousness can be defined as simple awareness that includes perception and emotion. As such, it is ascribed to most animals. By contrast, secondary consciousness depends on and includes such features as self-reflective awareness, abstract thinking, volition and metacognition. The term was coined by Gerald Edelman.
Quantum cognition is an emerging field which applies the mathematical formalism of quantum theory to model cognitive phenomena such as information processing by the human brain, language, decision making, human memory, concepts and conceptual reasoning, human judgment, and perception. The field clearly distinguishes itself from the quantum mind as it is not reliant on the hypothesis that there is something micro-physical quantum mechanical about the brain. Quantum cognition is based on the quantum-like paradigm or generalized quantum paradigm or quantum structure paradigm that information processing by complex systems such as the brain, taking into account contextual dependence of information and probabilistic reasoning, can be mathematically described in the framework of quantum information and quantum probability theory. Genetics and personality traits can be used to calculate an individual's or group's risk taking coefficient, and used with Quantum Decision Theory, can predict risky decisions.