Learning Words in Space and Time

A major debate in the study of word learning centers on the extension of categories to new items. The rational approach assumes that learners make structured inferences about category membership, whereas the mechanistic approach emphasizes the attentional and memory processes that form the basis of generalization behaviors. Recent support for the rational view comes from observations of the suspicious-coincidence effect: People generalize category membership narrowly when presented with three subordinate-level exemplars that share the same label and generalize category membership broadly when presented with one exemplar. Across three experiments, we examined the mechanistic basis of this effect. Results showed that the presentation of multiple subordinate-level exemplars led to narrow generalization only when the exemplars were presented simultaneously, even when the number of exemplars was increased from three to six. These data demonstrate that the suspicious-coincidence effect is firmly grounded in the general cognitive processes of attention, memory, and visual comparison.

[1]  Steven Pinker,et al.  Language learnability and language development , 1985 .

[2]  Linda B. Smith,et al.  An attentional learning account of the shape bias: reply to Cimpian and Markman (2005) and Booth, Waxman, and Huang (2005). , 2006, Developmental psychology.

[3]  D. Spalding The Principles of Psychology , 1873, Nature.

[4]  Linda B. Smith,et al.  From the lexicon to expectations about kinds: a role for associative learning. , 2005, Psychological review.

[5]  E. Markman,et al.  Word learning in children: an examination of fast mapping. , 1987, Child development.

[6]  Terry Regier,et al.  The Human Semantic Potential: Spatial Language and Constrained Connectionism , 1996 .

[7]  Catherine M. Sandhofer,et al.  Order of Presentation Effects in Learning Color Categories , 2008 .

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

[9]  Christopher D. Manning,et al.  Probabilistic models of language processing and acquisition , 2006, Trends in Cognitive Sciences.

[10]  S. Stokes,et al.  Factors that influence vocabulary development in two-year-old children. , 2009, Journal of child psychology and psychiatry, and allied disciplines.

[11]  W. A. Phillips On the distinction between sensory storage and short-term visual memory , 1974 .

[12]  AccountMichael,et al.  Learning Nouns and Adjectives : A Connectionist , 1998 .

[13]  K. Nakayama,et al.  On the Functional Role of Implicit Visual Memory for the Adaptive Deployment of Attention Across Scenes , 2000 .

[14]  E. Gibson Principles of Perceptual Learning and Development , 1969 .

[15]  J. Tenenbaum,et al.  Word learning as Bayesian inference. , 2007, Psychological review.

[16]  James L. McClelland Running Head : Letting Structure Emerge Letting Structure Emerge : Connectionist and Dynamical Systems Approaches to Understanding Cognition , 2009 .

[17]  J. Tenenbaum,et al.  Sensitivity to Sampling in Bayesian Word Learning We Thank Members of the Ubc Baby Cognition Lab for Their Help with Data Collection, And , 2022 .

[18]  J. Tenenbaum,et al.  Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.

[19]  B. Love,et al.  Putting the psychology back into psychological models: Mechanistic versus rational approaches , 2008, Memory & cognition.

[20]  Linda B. Smith,et al.  Grounding development in cognitive processes. , 2000, Child development.

[21]  T. Regier Emergent constraints on word-learning: a computational perspective , 2003, Trends in Cognitive Sciences.

[22]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[23]  Kim Plunkett,et al.  Theories of early language acquisition , 1997, Trends in Cognitive Sciences.

[24]  Alex Pentland,et al.  Learning words from sights and sounds: a computational model , 2002, Cogn. Sci..

[25]  L. Rips,et al.  Categories and resemblance. , 1993, Journal of experimental psychology. General.

[26]  Michael Gasser,et al.  Learning Nouns and Adjectives: A Connectionist Account , 1998 .

[27]  Larissa K. Samuelson,et al.  Dynamic Noun Generalization: Moment-to-Moment Interactions Shape Children's Naming Biases. , 2007 .

[28]  J. Tenenbaum,et al.  Structured statistical models of inductive reasoning. , 2009, Psychological review.

[29]  N. Mackintosh THE EFFECT OF ATTENTION ON THE SLOPE OF GENERALIZATION GRADIENTS. , 1965, British journal of psychology.

[30]  Ulrike Hahn,et al.  Effects of category diversity on learning, memory, and generalization , 2005, Memory & cognition.

[31]  H. Barlow Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .

[32]  Catherine M. Sandhofer,et al.  Why children learn color and size words so differently: evidence from adults' learning of artificial terms. , 2001, Journal of experimental psychology. General.

[33]  J. Siskind A computational study of cross-situational techniques for learning word-to-meaning mappings , 1996, Cognition.

[34]  J. Tenenbaum,et al.  What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes , 2009 .

[35]  W. K. Honig,et al.  Discrimination and Generalization on a Dimension of Stimulus Difference , 1962, Science.

[36]  D L Medin,et al.  Presentation order and recognition of categorically related examples , 1994, Psychonomic bulletin & review.

[37]  Larissa K. Samuelson,et al.  The dynamic nature of knowledge: Insights from a dynamic field model of children’s novel noun generalization , 2009, Cognition.

[38]  Haley A. Vlach,et al.  The spacing effect in children’s memory and category induction , 2008, Cognition.

[39]  Lisa Gershkoff-Stowe,et al.  Fast mapping skills in the developing lexicon. , 2007, Journal of speech, language, and hearing research : JSLHR.

[40]  James L. McClelland,et al.  Letting structure emerge: connectionist and dynamical systems approaches to cognition , 2010, Trends in Cognitive Sciences.