Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns embedded in both language and visual domains. Statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli. However, much of what learners must acquire about a structured domain consists of principles or rules that can be applied to novel inputs. It has been claimed that statistical learning and rule learning are separate mechanisms; in this article, however, we review evidence and provide a unifying perspective that argues for a single statistical-learning mechanism that accounts for both the learning of input stimuli and the generalization of learned patterns to novel instances. The balance between instance-learning and generalization is based on two factors: the strength of perceptual and cognitive biases that highlight structural regularities, and the consistency of elements’ contexts (unique vs. overlapping) in the input.
AblaD.KatahiraK.OkanoyaK. (2008). On-line assessment of statistical learning by event-related potentials. Journal of Cognitive Neuroscience, 20, 952–964.
2.
AblaD.OkanoyaK. (2008). Statistical segmentation of tone sequences activates the left inferior frontal cortex: A near-infrared spectroscopy study. Neuropsychologia, 46, 2787–2795.
3.
AustinA.NewportE. L. (2011). When children learn more than they are taught: Regularization in child and adult learners. Unpublished manuscript, University of Rochester, Rochester, NY.
4.
BulfH.JohnsonS. P.ValenzaE. (2011). Visual statistical learning in the newborn infant. Cognition, 121, 127–132.
5.
ChomskyN. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.
6.
ChomskyN. (1995). The minimalist program. Cambridge, MA: MIT Press.
7.
CreelS. C.NewportE. L.AslinR. N. (2004). Distant melodies: Statistical learning of non-adjacent dependencies in tone sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1119–1130.
8.
CroftW. (in press). Typology and universals. In AronoffM.Rees-MillerJ. (Eds.), The Blackwell handbook of linguistics (2nd ed.). Oxford, England: Basil Blackwell.
9.
CulbertsonJ.LegendreG. (2010, November). Investigating the evolution of agreement systems using an artificial language learning paradigm. Paper presented at the Western Conference on Linguistics, Fresno, CA.
10.
CulbertsonJ.SmolenskyP.LegendreG. (2011). Learning biases predict a word order universal. Unpublished manuscript, Johns Hopkins University, Baltimore, MD.
11.
DawsonC.GerkenL. A. (2009). From domain-generality to domain-sensitivity: 4-month-olds learn an abstract repetition rule in music that 7-month-olds do not. Cognition, 111, 378–382.
12.
EndressA. D.BonattiL. L. (2007). Rapid learning of syllable classes from a perceptually continuous speech stream. Cognition, 105, 247–299.
13.
EndressA. D.NesporM.MehlerJ. (2009). Perceptual and memory constraints on language acquisition. Trends in Cognitive Sciences, 13, 348–353.
14.
FedzechkinaM.JaegerT.NewportE. (2011). Functional biases in language learning: Evidence from word order and case-marking interaction. In CarlsonL.HölscherC.ShipleyT. (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 318–323). Austin, TX: Cognitive Science Society.
15.
FinleyS.BadeckerW. (2009). Vowel harmony and feature-based representations. Journal of Memory and Language, 61, 423–437.
16.
FiserJ.AslinR. N. (2002). Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences, USA, 99, 15822–15826.
17.
FriedericiA. D.BahlmannJ.HelmS.SchubotzR. I.AnwanderA. (2006). The brain differentiates human and non-human grammars: Functional localization and structural connectivity. Proceedings of the National Academy of Sciences, USA, 103, 2458–2463.
18.
GebhartA. L.NewportE. L.AslinR. N. (2009). Statistical learning of adjacent and non-adjacent dependencies among non-linguistic sounds. Psychonomic Bulletin & Review, 16, 486–490.
19.
GerkenL. A. (2006). Decisions, decisions: Infant language learning when multiple generalizations are possible. Cognition, 98, B67–B74.
20.
GervainJ.MacagnoF.CogoiS.PenaM.MehlerM. (2008). The neonate brain detects speech structure. Proceedings of the National Academy of Sciences, USA, 105, 14222–14227.
21.
GomezR. L.GerkenL. A. (1999). Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge. Cognition, 70, 109–135.
22.
GreenbergJ. (1963). Some universals of grammar with particular reference to the order of meaningful elements. In GreenbergJ. (Ed.), Universals of language (pp. 73–113). Cambridge, MA: MIT Press.
23.
Hudson KamC. L.NewportE. L. (2005). Regularizing unpredictable variation: The roles of adult and child learners in language formation and change. Language Learning and Development, 1, 151–195.
24.
Hudson KamC. L.NewportE. L. (2009). Getting it right by getting it wrong: When learners change languages. Cognitive Psychology, 59, 30–66.
25.
JohnsonS. P.FernandesK. J.FrankM. C.KirkhamN. Z.MarcusG. F.RabagliatiH.SlemmerJ. A. (2009). Abstract rule learning for visual sequences in 8- and 11-month-olds. Infancy, 14, 2–18.
26.
KaruzaE.NewportE. L.AslinR. N.DavisS.TivarusM.BavelierD. (2011, October). Neural correlates of statistical learning in a word segmentation task: An fMRI study. Paper presented at Neurobiology of Language Conference, Annapolis, MD.
27.
KirkhamN. Z.SlemmerJ. A.JohnsonS. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83, B35–B42.
28.
MarcusG. F. (2000). Pabiku and Ga Ti Ga: Two mechanisms infants use to learn about the world. Current Directions in Psychological Science, 9, 145–147.
29.
MarcusG. F.FernandesK. J.JohnsonS. P. (2007). Infant rule learning facilitated by speech. Psychological Science, 18, 387–391.
30.
MarcusG. F.VijayanS.Bandi RaoS.VishtonP. M. (1999). Rule learning in seven-month-old infants. Science, 283, 77–80.
31.
MayeJ.WeissD. J.AslinR. N. (2008). Statistical phonetic learning in infants: Facilitation and feature generalization. Developmental Science, 11, 122–134.
32.
MayeJ.WerkerJ. F.GerkenL. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition, 82, B101–B111.
33.
McNealyK.MazziottaJ. C.DaprettoM. (2006). Cracking the language code: Neural mechanisms underlying speech parsing. Journal of Neuroscience, 26, 7629–7639.
34.
McNealyK.MazziottaJ. C.DaprettoM. (2010). The neural basis of speech parsing in children and adults. Developmental Science, 13, 385–406.
35.
NewportE. L.AslinR. N. (2004). Learning at a distance 1: Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48, 127–162.
36.
OrbanG.FiserJ.AslinR. N.LengyelM. (2008). Bayesian learning of visual chunks by human observers. Proceedings of the National Academy of Sciences, USA, 105, 2745–2750.
37.
ReederP. A.NewportE. L.AslinR. N. (2009). The role of distributional information in linguistic category formation. In TaatgenN.van RijnH. (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2564–2569). Austin, TX: Cognitive Science Society.
38.
ReederP. A.NewportE. L.AslinR. N. (2010). Novel words in novel contexts: The role of distributional information in form-class category learning. In OhlssonS.CatramboneR. (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2063–2068). Austin, TX: Cognitive Science Society.
39.
SaffranJ. R.AslinR. N.NewportE. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.
40.
SaffranJ. R.PollakS. D.SeibelR. L.ShkolnikA. A. (2007). Dog is a dog is a dog: Infant rule learning is not specific to language. Cognition, 105, 669–680.
41.
TeinonenT.FellmanV.NaatanenR.AlkuP.HuotilainenM. (2009). Statistical language learning in neonates revealed by event-related brain potentials. BMC Neuroscience, 10, 21.
42.
TilyH.FrankM. C.JaegerT. F. (2011). The learnability of constructed languages reflects typological patterns. In CarlsonL.HölscherC.ShipleyT. (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 1364–1369). Austin, TX: Cognitive Science Society.
43.
Turk-BrowneN. B.SchollB. J.ChunM. M.JohnsonM. K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21, 1934–1945.
44.
XuF.TenenbaumJ. B. (2007). Word learning as Bayesian inference. Psychological Review, 114, 245–272.
