Sage Journals: Discover world-class research

Abstract

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.

Keywords

language cognitive processes

Get full access to this article

View all access options for this article.

References

Berwick

R. C.

(1986). Learning from positive-only examples: The subset principle and three case studies. In Carbonell

J. G.

Michalski

R. R.

Mitchell

T. M.

(Eds.), Machine learning: An artificial intelligence approach (Vol. 2, pp. 625–645). Los Altos, CA: Morgan Kauffman.

Chater

Manning

C. D.

(2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences, 10, 335–344.

Chun

M. M.

Nakayama

(2000). On the functional role of implicit visual memory for the adaptive deployment of attention across scenes. Visual Cognition, 7, 65–81.

Colunga

Smith

L. B.

(2005). From the lexicon to expectations about kinds: A role for associative learning. Psychological Review, 112, 347–382.

Garner

W. R.

(1974). The processing of information and structure. Potomac, MD: Erlbaum.

Gasser

Smith

L. B.

(1998). Learning nouns and adjectives: A connectionist approach. Language and Cognitive Processes, 13, 269–306.

Gentner

Namy

L. L.

(2006). Analogical processes in language learning. Current Directions in Psychological Science, 15, 297–301.

Gershkoff-Stowe

Hahn

E. R.

(2007). Fast mapping skills in the developing lexicon. Journal of Speech, Language, and Hearing Research, 50, 682–696.

Gibson

E. J.

(1969). Principles of perceptual learning and development. East Norwalk, CT: Appleton-Century-Crofts.

10.

Griffiths

T. L.

Chater

Kemp

Perfors

Tenenbaum

J. B.

(2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357–364.

11.

Gweon

Tenenbaum

J. B.

Schulz

L. E.

(2009). What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes. In Taatgen

van Rijn

(Eds.), Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 1282–1287). Mahwah, NJ: Erlbaum.

12.

Hahn

Bailey

T. M.

Elvin

L. B. C.

(2005). Effects of category diversity on learning, memory, and generalization. Memory & Cognition, 33, 289–302.

13.

Heibeck

T. H.

Markman

E. M.

(1987). Word learning in children: An examination of fast mapping. Child Development, 58, 1021–1034.

14.

Honig

W. K.

Day

R. W.

(1962). Discrimination and generalization on a dimension of stimulus difference. Science, 138, 29–31.

15.

James

(1890). The principles of psychology. New York, NY: Henry Holt.

16.

Kemp

Tenenbaum

J. B.

(2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 20–58.

17.

Mackintosh

N. J.

(1965). The effect of attention on the slope of generalization gradients. British Journal of Psychology, 56, 87–93.

18.

Marr

(1982). Vision: A computational investigation into the human representation and processing of visual information. New York, NY: W.H. Freeman.

19.

McClelland

J. L.

Botvinick

M. M.

Noelle

D. C.

Plaut

D. C.

Rogers

T. T.

Seidenberg

M. S.

Smith

L. B.

(2010). Letting structure emerge: Connectionist and dynamical systems approaches to understanding cognition. Trends in Cognitive Sciences, 14, 348–356.

20.

Medin

D. L.

Bettger

J. G.

(1994). Presentation order and recognition of categorically related examples. Psychonomic Bulletin & Review, 1, 250–254.

21.

Phillips

W. A.

(1974). On the distinction between sensory storage and short-term visual memory. Perception & Psychophysics, 16, 283–290.

22.

Pinker

(1984). Language learnability and language development. Cambridge, MA: Harvard University Press.

23.

Plunkett

(1997). Theories of early language acquisition. Trends in Cognitive Sciences, 1, 146–153.

24.

Regier

(1996). The human semantic potential: Spatial language and constrained connectionism. Cambridge, MA: MIT Press.

25.

Regier

(2003). Emergent constraints on word-learning: A computational perspective. Trends in Cognitive Sciences, 7, 263–268.

26.

Rips

L. J.

Collins

(1993). Categories and resemblance. Journal of Experimental Psychology: General, 122, 468–486.

27.

Roy

Pentland

(2004). Learning words from sights and sounds: A computational model. Cognitive Science, 26, 113–146.

28.

Sakamoto

Jones

Love

B. C.

(2008). Putting the psychology back into psychological models: Mechanistic versus rational approaches. Memory & Cognition, 36, 1057–1065.

29.

Samuelson

L. K.

Horst

J. S.

(2007). Dynamic noun generalization: Moment-to-moment interactions shape children’s naming biases. Infancy, 11, 97–110.

30.

Samuelson

L. K.

Schutte

A. R.

Horst

J. S.

(2009). The dynamic nature of knowledge: Insights from a dynamic field model of children’s novel noun generalizations. Cognition, 110, 322–345.

31.

Samuelson

L. K.

Smith

L. B.

(2000). Grounding development in cognitive processes. Child Development, 71, 98–106.

32.

Sandhofer

C. M.

Doumas

L. A. A.

(2008). Order of presentation effects in learning color categories. Journal of Cognition and Development, 9, 194–221.

33.

Sandhofer

C. M.

Smith

L. B.

(2001). Why children learn color and size words so differently: Evidence from adults’ learning of artificial terms. Journal of Experimental Psychology: General, 130, 600–617.

34.

Schyns

P. G.

Goldstone

R. L.

Thibaut

(1998). The development of features in object concepts. Behavioral & Brain Sciences, 21, 1–54.

35.

Siskind

J. M.

(1996). A computational study of cross-situational techniques for learning word-to-meaning mappings. Cognition, 61, 31–91.

36.

Smith

L. B.

Samuelson

(2006). An attentional learning account of the shape bias: Reply to Cimpian and Markman (2005) and Booth, Waxman, and Huang (2005). Developmental Psychology, 42, 1339–1343.

37.

Stokes

S. F.

Klee

(2009). Factors that influence vocabulary development in two-year-old children. Journal of Child Psychology and Psychiatry, 50, 498–505.

38.

Vlach

H. A.

Sandhofer

C. M.

Kornell

(2008). The spacing effect in children’s memory and category induction. Cognition, 109, 163–167.

39.

Tenenbaum

(2007a). Sensitivity to sampling in Bayesian word learning. Developmental Science, 10, 288–297.

40.

Tenenbaum

(2007b). Word learning as Bayesian inference. Psychological Review, 114, 245–272.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB

Learning Words in Space and Time

Abstract

Keywords

Get full access to this article

References

Supplementary Material