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The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex

Overview of attention for article published in PLOS ONE, May 2012
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Title
The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex
Published in
PLOS ONE, May 2012
DOI 10.1371/journal.pone.0036399
Pubmed ID
Authors

Celeste Kidd, Steven T. Piantadosi, Richard N. Aslin

Abstract

Human infants, like immature members of any species, must be highly selective in sampling information from their environment to learn efficiently. Failure to be selective would waste precious computational resources on material that is already known (too simple) or unknowable (too complex). In two experiments with 7- and 8-month-olds, we measure infants' visual attention to sequences of events varying in complexity, as determined by an ideal learner model. Infants' probability of looking away was greatest on stimulus items whose complexity (negative log probability) according to the model was either very low or very high. These results suggest a principle of infant attention that may have broad applicability: infants implicitly seek to maintain intermediate rates of information absorption and avoid wasting cognitive resources on overly simple or overly complex events.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 523 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 2%
Germany 2 <1%
France 2 <1%
Israel 2 <1%
Italy 1 <1%
Switzerland 1 <1%
Spain 1 <1%
Canada 1 <1%
Luxembourg 1 <1%
Other 1 <1%
Unknown 499 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 161 31%
Researcher 78 15%
Student > Master 48 9%
Student > Bachelor 48 9%
Student > Doctoral Student 41 8%
Other 78 15%
Unknown 69 13%
Readers by discipline Count As %
Psychology 253 48%
Neuroscience 33 6%
Linguistics 29 6%
Computer Science 20 4%
Agricultural and Biological Sciences 18 3%
Other 63 12%
Unknown 107 20%