| 2001 | - | Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity - Kirby | :: | 96 |
| 2001 | - | Evolution of universal grammar - Nowak,Komarova,Niyogi | :: | 90 |
| 1965 | - | Aspects of the theory of syntax - Chomsky | :: | 88 |
| 2000 | - | The evolution of syntactic communication - Nowak,Plotkin,Jansen | :: | 81 |
| 1999 | - | Function, Selection and Innateness: the Emergence of Language Universals - Kirby | :: | 76 |
| 2002 | - | The Emergence of Linguistic Structure: An overview of the Iterated Learning Model - Kirby,Hurford | :: | 58 |
| 1998 | - | Approaches to the Evolution of Language: Social and Cognitive Bases - Hurford,Studdert-Kennedy,Knight | :: | 55 |
| 2002 | - | Compositional Syntax from Cultural Transmission - Brighton | :: | 53 |
| 2002 | - | Computational and evolutionary aspects of language - Nowak,Komarova,Niyogi | :: | 49 |
| 2002 | - | Linguistic Evolution through Language Acquisition: Formal and Computational Models - Briscoe | :: | 47 |
| 1960 | - | Word and object - Quine | :: | 40 |
| 2003 | - | Iterated Learning: a framework for the emergence of language - Smith,Kirby,Brighton | :: | 34 |
| 2003 | - | Language Evolution: The States of the Art - Christiansen,Kirby | :: | 33 |
| 2001 | - | The evolutionary dynamics of grammar acquisition - Komarova,Niyogi,Nowak | :: | 33 |
| 1997 | - | Evolutionary Consequences of Language Learning - Niyogi,Berwick | :: | 29 |
| 1995 | - | The Logical Problem of Language Change - Niyogi,Berwick | :: | 26 |
| 1996 | - | A Language Learning Model for Finite Parameter Spaces - Niyogi,Berwick | :: | 24 |
| 1994 | - | Triggers - Gibson,Wexler | :: | 23 |
| 1997 | - | A Dynamical Systems Model for Language Change - Niyogi,Berwick | :: | 17 |
| 2004 | - | From UG to Universals: linguistic adaptation through iterated learning - Kirby,Smith,Brighton | :: | 16 |
| 1981 | - | Language universals and linguistic typology - Comrie | :: | 13 |
| 1983 | - | The neutral theory of molecular evolution - Kimura | :: | 12 |
| 1963 | - | Universals of language - Greenberg | :: | 12 |
| 2003 | - | Language dynamics in finite populations - Komarova,Nowak | :: | 12 |
| 1988 | - | Explaining language universals - Hawkins | :: | 12 |
| 1995 | - | The nature of statistical learning theory - Vapnik | :: | 11 |
| 1982 | - | Vision - Marr | :: | 9 |
| 1999 | - | Foundations of statistical natural language processing - Manning,Schutze | :: | 8 |
| 1986 | - | On learning the past tenses of English verbs - Rumelhart,McClelland | :: | 8 |
| 1997 | - | Markov chains - Norris | :: | 6 |
| 1994 | - | An introduction to computational learning theory - Kearns,Vazirani | :: | 6 |
| 2001 | - | Compositionality - Krifka | :: | 6 |
| 1974 | - | Differential equations, dynamical systems, and linear algebra - Hirsch,Smale | :: | 5 |
| 1987 | - | Towards a universal law of generalization for psychological science - Shepard | :: | 5 |
| 1992 | - | Neural networks and the bias-variance dilemma - Geman,Bienenstock,Doursat | :: | 5 |
| 1992 | - | Alcove: An exemplar-based connectionist model of category learning - Kruschke | :: | 5 |
| 1986 | - | Attention, similarity, and the identification-categorization relationship - Nosofsky | :: | 4 |
| 2001 | - | Generalization, similarity, and Bayesian inference - Tenenbaum,Griffiths | :: | 4 |
| 2003 | - | Information theory, inference, and learning algorithms - Mackay | :: | 4 |
| 1959 | - | Individual choice behavior - Luce | :: | 3 |
| 1997 | - | An introduction to Kolmogorov complexity and its applications - Li,Vitanyi | :: | 3 |
| 2003 | - | Probability theory: The logic of science - Jaynes | :: | 2 |
| 1996 | - | Reconciling simplicity and likelihood principles in perceptual organization - Chater | :: | 2 |
| 1990 | - | The adaptive character of thought - Anderson | :: | 2 |
| 1995 | - | Categorization as probability density estimation - Ashby,Alfonso-Reese | :: | 2 |
| 2004 | - | Evolutionary theory: Mathematical and conceptual foundations - Rice | :: | 2 |
| 1954 | - | Foundations of statistics - Savage | :: | 2 |
| 1984 | - | Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images - Geman,Geman | :: | 2 |
| 1994 | - | The Bayesian choice: A decision-theoretic motivation - Robert | :: | 2 |
| 1983 | - | Finite Markov chains - Kemeny,Snell | :: | 2 |
| 1977 | - | Maximum likelihood from incomplete data via the EM algorithm - Dempster,Laird,Rubin | :: | 2 |
| 2006 | - | Innateness and culture in the evolution of language - Dowman,Kirby,Griffiths | :: | 1 |
| 1992 | - | A stochastic approximation type EM algorithm for the mixture problem - Celeux,Diebolt | :: | 1 |
| 1976 | - | Probability learning and sequence learning - Myers | :: | 1 |
| 2003 | - | Inferring causal networks from observations and interventions - Steyvers,Tenenbaum,Wagenmakers,Blum | :: | 1 |
| 1998 | - | Rational models of cognition - Oaksford,Chater | :: | 1 |
| 2000 | - | An economist's perspective on probability matching - Vulkan | :: | 1 |
| 1995 | - | Convergence rates of Markov chains - Rosenthal | :: | 1 |
| 1987 | - | Attention and learning processes in the identification and categorization of integral stimuli - Nosofsky | :: | 1 |
| 1993 | - | Asymptotic properties of a stochastic EM algorithm for estimating mixing proportions - Diebolt,Celeux | :: | 1 |
| 1999 | - | Convergence of a stochastic approximation version of the EM algorithm - Delyon,Lavielle,Moulines | :: | 1 |
| 1985 | - | The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem - Celeux,Diebolt | :: | 1 |
| 1992 | - | On the convergence of successive substitution sampling - Schervish,Carlin | :: | 1 |
| 1995 | - | Probable networks and plausible predictions - a review of practical bayesian methods for supervised neural networks - MacKay | :: | 1 |
| 1999 | - | Ten years of the rational analysis of cognition - Chater,Oaksford | :: | 1 |
| 1998 | - | A view EM algorithm that justifies incremental, sparse, and other variants - Neal,Hinton | :: | 1 |
| 2002 | - | On single versus multiple imputation for a class of stochastic algorithms estimating maximum likelihood - Ip | :: | 1 |
| 2000 | - | The stochastic EM algorithm: estimation and asymptotic results - Nielsen | :: | 1 |
| 1996 | - | Markov chain Monte Carlo in practice - Gilks,Richardson,Spiegelhalter | :: | 1 |
| 1999 | - | Conditions for convergence of Monte-Carlo EM sequences with an application to product diffusion modeling - Sherman,Ho,Dalal | :: | 1 |
| 1992 | - | Connectionist learning of belief networks - Neal | :: | 1 |
| 1998 | - | The Bayesian structural EM algorithm - Friedman | :: | 1 |
| 1988 | - | A probabilistic teacher algorithm for iterative maximum likelihood estimation - Celeux,Diebolt | :: | 1 |
| 1997 | - | The EM algorithm and extensions - McLachlan,Krishnan | :: | 1 |
| 1994 | - | Introduction to the numerical solution of Markov chains - Stewart | :: | 1 |
| 1992 | - | Multidimensional models of perception and cognition - Ashby | :: | 1 |
| 2003 | - | Convergence of the Monte Carlo expectation maximization for curved exponential families - Fort,Moulines | :: | 1 |
| 2005 | - | Regularizing unpredictable variation: The roles of adult and child learners in language formation and change - Hudson-Kam,Newport | :: | 1 |
| 1993 | - | Relations between prototype, exemplar, and decision bound models of categorization - Ashby,Maddox | :: | 1 |
| 1995 | - | Covariance structure and convergence rate of the Gibbs sampler with various scans - Liu,Wong,Kong | :: | 1 |
| 1996 | - | Stochastic EM: method and application - Diebolt,Ip | :: | 1 |
| 1990 | - | A Monte-Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms - Wei,Tanner | :: | 1 |