Using machine learning to understand age and gender classification based on infant temperament

HIGHLIGHTS

• who: Maria A. Gartstein and collaborators from the Washington State University, Pullman, WA, United States of America, University of Idaho, Moscow have published the paper: Using machine learning to understand age and gender classification based on infant temperament, in the Journal: PLOS ONE of 14/Oct/2021
• what: The Infant Behavior Questionnaire-Revised (IBQ-R) designed to provide indicators of infant temperament comprises 14 fine-grained scales: Activity Level, Smiling/Laughter, Approach, High Intensity Pleasure, Perceptual Sensitivity, Vocal Reactivity, Fear, Distress to Limitations, Sadness, Falling Reactivity, Duration of Orienting, Soothability, Cuddliness/Affiliation, and Low Intensity . . .
  

   

  

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