ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.

[1]  R. Shepard.  Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space , 1957 .

[2]  R. Shepard.  Stimulus and response generalization: deduction of the generalization gradient from a trace model. , 1958, Psychological review.

[3]  R. Shepard,et al.  Learning and memorization of classifications. , 1961 .

[4]  R. Shepard.  The analysis of proximities: Multidimensional scaling with an unknown distance function. II , 1962 .

[5]  H. Kendler,et al.  Vertical and horizontal processes in problem solving. , 1962, Psychological review.

[6]  Eugene Galanter,et al.  Handbook of mathematical psychology: I. , 1963 .

[7]  R. Shepard.  Attention and the metric structure of the stimulus space. , 1964 .

[8]  L. Kamin.  Predictability, surprise, attention, and conditioning , 1967 .

[9]  L. E. Bourne.  Knowing and Using Concepts. , 1970 .

[10]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[11]  W. F. Prokasy,et al.  Classical conditioning II: Current research and theory. , 1972 .

[12]  R. Brown.  A First Language , 1973 .

[13]  W. R. Garner.  The Processing of Information and Structure , 1974 .

[14]  E. Rosch.  Cognitive reference points , 1975, Cognitive Psychology.

[15]  E. Rosch,et al.  Structural bases of typicality effects. , 1976 .

[16]  L. E. Bourne,et al.  Frequency analysis of attribute identification , 1976 .

[17]  A. Tversky.  Features of Similarity , 1977 .

[18]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[19]  E. Kremer.  The Rescorla-Wagner model: losses in associative strength in compound conditioned stimuli. , 1978, Journal of experimental psychology. Animal behavior processes.

[20]  B. Fischhoff,et al.  Journal of Experimental Psychology: Human Learning and Memory , 1980 .

[21]  D. Medin,et al.  Linear separability in classification learning. , 1981 .

[22]  Mark W. Altom,et al.  Correlated symptoms and simulated medical classification. , 1982, Journal of experimental psychology. Learning, memory, and cognition.

[23]  R. Nosofsky.  American Psychological Association, Inc. Choice, Similarity, and the Context Theory of Classification , 2022 .

[24]  W. Estes.  Memory storage and retrieval processes in category learning. , 1986 .

[25]  D. Rumelhart.  Learning internal representations by back-propagating errors , 1986 .

[26]  James L. McClelland,et al.  On learning the past-tenses of English verbs: implicit rules or parallel distributed processing , 1986 .

[27]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[28]  W. Estes.  Array models for category learning , 1986, Cognitive Psychology.

[29]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[30]  Ryszard S. Michalski,et al.  Constraints and Preferences in Inductive Learning: An Experimental Study of Human and Machine Performance , 1987 .

[31]  R. Nosofsky.  Attention and learning processes in the identification and categorization of integral stimuli. , 1987, Journal of experimental psychology. Learning, memory, and cognition.

[32]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[33]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[34]  Terrence J. Sejnowski,et al.  Network model of shape-from-shading: neural function arises from both receptive and projective fields , 1988, Nature.

[35]  Christopher J. Matheus.  Exemplar versus prototype network models for concept representation , 1988, Neural Networks.

[36]  W. Estes,et al.  Toward a framework for combining connectionist and symbol-processing models☆ , 1988 .

[37]  Gerald Tesauro,et al.  A study of scaling and generalization in neural networks , 1988, Neural Networks.

[38]  D. Medin,et al.  Problem structure and the use of base-rate information from experience. , 1988, Journal of experimental psychology. General.

[39]  G. Bower,et al.  Evaluating an adaptive network model of human learning , 1988 .

[40]  Robert M. Nosofsky,et al.  On Exemplar-Based Exemplar Representations: Reply to Ennis (1988) , 1988 .

[41]  R. Nosofsky.  Similarity, frequency, and category representations. , 1988 .

[42]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[43]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988 .

[44]  Richard T. Scalettar,et al.  Emergence of grandmother memory in feed forward networks: learning with noise and forgetfulness , 1988 .

[45]  W. Estes,et al.  Base-rate effects in category learning: a comparison of parallel network and memory storage-retrieval models. , 1989, Journal of experimental psychology. Learning, memory, and cognition.

[46]  M. Niranjan,et al.  Generalising the nodes of the error propagation network , 1989, International 1989 Joint Conference on Neural Networks.

[47]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .

[48]  R. Nosofsky,et al.  Rules and exemplars in categorization, identification, and recognition. , 1989, Journal of experimental psychology. Learning, memory, and cognition.

[49]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[50]  R Ratcliff,et al.  Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. , 1990, Psychological review.

[51]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[52]  Stephen José Hanson,et al.  What connectionist models learn: Learning and representation in connectionist networks , 1990, Behavioral and Brain Sciences.

[53]  D R Shanks,et al.  Connectionism and the Learning of Probabilistic Concepts , 1990, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[54]  V. Marchman,et al.  U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition , 1991, Cognition.

[55]  James L. McClelland.  Stochastic interactive processes and the effect of context on perception , 1991, Cognitive Psychology.

[56]  M. Gluck.  Stimulus Generalization and Representation in Adaptive Network Models of Category Learning , 1991 .

[57]  John R. Anderson,et al.  The Adaptive Nature of Human Categorization. , 1991 .

[58]  R. Nosofsky.  Exemplars, prototypes, and similarity rules. , 1992 .

[59]  R. Nosofsky,et al.  Combining exemplar-based category representations and connectionist learning rules. , 1992, Journal of experimental psychology. Learning, memory, and cognition.