TY - JOUR
T1 - The profile of abstract rule learning in infancy
T2 - Meta-analytic and experimental evidence
AU - Rabagliati, Hugh
AU - Ferguson, Brock
AU - Lew-Williams, Casey
N1 - Funding Information:
This work was funded by grants ES/ L01064X/1 and ES/N005635/1 from the ESRC and by grant R03HD079779 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
Funding Information:
This work was funded by grants ES/L01064X/1 and ES/N005635/1 from the ESRC (to HR), as well as by grant R03HD079779 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (to CLW). We thank Christina Bergmann, Alex Cristia, Michael C. Frank, Claartje Levelt, Hermann Bulf, Scott P. Johnson, Gary F. Marcus, Erik Thiessen, Chia-huei Tseng, and Angeline Tsui for data and discussion, as well as all authors whose studies we included, and all the scientists who contacted us following our call for findings. We are grateful to Sarah Hampton, Katrina Rowe, Eva Fourakis, Maritza Gomez, Bridgett Riverol, Emily Wills, Rinat Tal, and Sarah Yashar-Gershman for help collecting data.
Publisher Copyright:
© 2018 The Authors. Developmental Science Published by John Wiley & Sons Ltd.
PY - 2019/1
Y1 - 2019/1
N2 - Everyone agrees that infants possess general mechanisms for learning about the world, but the existence and operation of more specialized mechanisms is controversial. One mechanism—rule learning—has been proposed as potentially specific to speech, based on findings that 7-month-olds can learn abstract repetition rules from spoken syllables (e.g. ABB patterns: wo-fe-fe, ga-tu-tu…) but not from closely matched stimuli, such as tones. Subsequent work has shown that learning of abstract patterns is not simply specific to speech. However, we still lack a parsimonious explanation to tie together the diverse, messy, and occasionally contradictory findings in that literature. We took two routes to creating a new profile of rule learning: meta-analysis of 20 prior reports on infants’ learning of abstract repetition rules (including 1,318 infants in 63 experiments total), and an experiment on learning of such rules from a natural, non-speech communicative signal. These complementary approaches revealed that infants were most likely to learn abstract patterns from meaningful stimuli. We argue that the ability to detect and generalize simple patterns supports learning across domains in infancy but chiefly when the signal is meaningfully relevant to infants’ experience with sounds, objects, language, and people.
AB - Everyone agrees that infants possess general mechanisms for learning about the world, but the existence and operation of more specialized mechanisms is controversial. One mechanism—rule learning—has been proposed as potentially specific to speech, based on findings that 7-month-olds can learn abstract repetition rules from spoken syllables (e.g. ABB patterns: wo-fe-fe, ga-tu-tu…) but not from closely matched stimuli, such as tones. Subsequent work has shown that learning of abstract patterns is not simply specific to speech. However, we still lack a parsimonious explanation to tie together the diverse, messy, and occasionally contradictory findings in that literature. We took two routes to creating a new profile of rule learning: meta-analysis of 20 prior reports on infants’ learning of abstract repetition rules (including 1,318 infants in 63 experiments total), and an experiment on learning of such rules from a natural, non-speech communicative signal. These complementary approaches revealed that infants were most likely to learn abstract patterns from meaningful stimuli. We argue that the ability to detect and generalize simple patterns supports learning across domains in infancy but chiefly when the signal is meaningfully relevant to infants’ experience with sounds, objects, language, and people.
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U2 - 10.1111/desc.12704
DO - 10.1111/desc.12704
M3 - Article
C2 - 30014590
AN - SCOPUS:85050379187
SN - 1363-755X
VL - 22
JO - Developmental Science
JF - Developmental Science
IS - 1
M1 - e12704
ER -