An iterative probabilistic model of speech-related vocalization rate growth due to child-caregiver interaction

Over the course of the first four years of life, the proportion of children's vocalizations that are speech-related increases steadily. The rate of this growth is reduced for children with autism spectrum disorder (ASD) and for children from households with relatively lower socioeconomic status (SES). The present study attempts to model this set of findings, treating adult responses as reinforcers of child behavior. The model starts with a 50% chance of producing a speech-related vocalization and gradually increases this probability by updating its speechrelated vocalization and not-speech-related vocalization probabilities each time a response is received. Numbers of vocalizations per day and rates of adult responding to the two vocalization types are drawn from human data to create high SES typically developing (TD), high SES ASD, low SES TD, and low SES ASD versions. The model shows growth in speech-related vocalizations that matches well to that observed for the human children and that matches the differences observed across clinical and SES groups. Some aspects of speech-related vocalization development are not well accounted for by the model; possible explanations and extensions are proposed.

[1]  Umit Yapanel,et al.  Reliability of the LENA Language Environment Analysis System in Young Children’s Natural Home Environment , 2009 .

[2]  A. Wetherby,et al.  Communicative profiles of preschool children with handicaps: implications for early identification. , 1989, The Journal of speech and hearing disorders.

[3]  G. Westermann,et al.  A new model of sensorimotor coupling in the development of speech , 2004, Brain and Language.

[4]  A. Karmiloff-Smith.  Development itself is the key to understanding developmental disorders , 1998, Trends in Cognitive Sciences.

[5]  G. Dawson,et al.  Early social attention impairments in autism: social orienting, joint attention, and attention to distress. , 2004, Developmental psychology.

[6]  Pierre-Yves Oudeyer,et al.  Self-organization of early vocal development in infants and machines: the role of intrinsic motivation , 2014, Front. Psychol..

[7]  A. Neal,et al.  Neural plasticity, joint attention, and a transactional social-orienting model of autism* , 2000 .

[8]  D K Oller,et al.  Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development , 2010, Proceedings of the National Academy of Sciences.

[9]  Michael Ramscar,et al.  Children Value Informativity Over Logic in Word Learning , 2013, Psychological science.

[10]  F. Tion,et al.  Reliability of the LENA TM Language Environment Analysis System in Young Children's Natural Home Environment , 2009 .

[11]  I. Howard,et al.  Modeling the development of pronunciation in infant speech acquisition. , 2011, Motor control.

[12]  Melody Dye,et al.  The Effects of Feature-Label-Order and Their Implications for Symbolic Learning , 2010, Cogn. Sci..

[13]  Tetsuya Ogata,et al.  Continuous vocal imitation with self-organized vowel spaces in Recurrent Neural Network , 2009, 2009 IEEE International Conference on Robotics and Automation.

[14]  Katharina J. Rohlfing,et al.  Microdynamics of Interaction: Capturing and Modeling Infants' Social Learning [Guest Editorial] , 2013, IEEE Trans. Auton. Ment. Dev..

[15]  M. Goldstein,et al.  Social interaction shapes babbling: Testing parallels between birdsong and speech , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Elena Patten,et al.  Vocal Patterns in Infants with Autism Spectrum Disorder: Canonical Babbling Status and Vocalization Frequency , 2014, Journal of Autism and Developmental Disorders.

[17]  Andrew Pickles,et al.  Patterns of growth in verbal abilities among children with autism spectrum disorder. , 2007, Journal of consulting and clinical psychology.

[18]  Satrajit S. Ghosh,et al.  Neural modeling and imaging of the cortical interactions underlying syllable production , 2006, Brain and Language.

[19]  Bernd J. Kröger,et al.  Towards a neurocomputational model of speech production and perception , 2009, Speech Commun..

[20]  Anne Fernald,et al.  Talking to Children Matters , 2013, Psychological science.

[21]  John H. L. Hansen,et al.  Signal processing for young child speech language development , 2008, WOCCI.

[22]  E. Hoff.  The specificity of environmental influence: socioeconomic status affects early vocabulary development via maternal speech. , 2003, Child development.

[23]  Erika Hoff,et al.  Socioeconomic Status and Parenting , 2019, Handbook of Parenting.

[24]  Ping Li,et al.  Early lexical development in a self-organizing neural network , 2004, Neural Networks.

[25]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[26]  Chrystopher L. Nehaniv,et al.  Interactive Language Learning by Robots: The Transition from Babbling to Word Forms , 2012, PloS one.

[27]  Sue Peppé,et al.  Receptive and expressive prosodic ability in children with high-functioning autism. , 2007, Journal of speech, language, and hearing research : JSLHR.

[28]  J. Iverson,et al.  Maternal verbal responses to communication of infants at low and heightened risk of autism , 2014, Autism : the international journal of research and practice.

[29]  R. Rescorla.  A theory of pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement , 1972 .

[30]  P. Mundy,et al.  Joint Attention and Early Social Communication: Implications for Research on Intervention with Autism , 1997, Journal of autism and developmental disorders.

[31]  Marilyn M. Vihman,et al.  Supporting Early Vocabulary Development: What Sort of Responsiveness Matters? , 2013, IEEE Transactions on Autonomous Mental Development.

[32]  S. Suter.  Meaningful differences in the everyday experience of young American children , 2005, European Journal of Pediatrics.

[33]  Gordon Ramsay,et al.  Out of the mouths of babes: vocal production in infant siblings of children with ASD. , 2011, Journal of child psychology and psychiatry, and allied disciplines.

[34]  Anne S. Warlaumont,et al.  Salience-based reinforcement of a spiking neural network leads to increased syllable production , 2013, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[35]  Minoru Asada,et al.  Vowel Acquisition Based on an Auto-Mirroring Bias with a Less Imitative Caregiver , 2012, Adv. Robotics.

[36]  Jill Gilkerson,et al.  A Social Feedback Loop for Speech Development and Its Reduction in Autism , 2014, Psychological science.

[37]  Ami Klin,et al.  Young autistic children's listening preferences in regard to speech: A possible characterization of the symptom of social withdrawal , 1991, Journal of autism and developmental disorders.

[38]  Anne Fernald,et al.  SES differences in language processing skill and vocabulary are evident at 18 months. , 2013, Developmental science.

[39]  Gert Westermann,et al.  Prespeech motor learning in a neural network using reinforcement , 2013, Neural Networks.