Modeling the Dynamics of Early Communication and Development 2017-10-24T20:29:00+00:00

What’s for dynr: A package for linear and nonlinear dynamic modeling in R

Computational modeling is central to a rigorous understanding of the development of the child’s first social relationships. The project addresses this challenge by modeling longitudinal change in the dynamics of early social interactions. Our proposed models integrate objective (automated) measurements of emotion and attention and common genetic variants relevant to those constructs. This project is supported by funding from the National Institute of General Medical Sciences.

Affiliated collaborators/members/students

Daniel Messinger
Jeff Cohn
Zakia Hammal
Meng Chen

Workshops and demos

Cohn, J.F. (March 2015). Face processing. Society for Affective Science. San Francisco, CA.

Cohn, J.F. (September 2015). Sentiment and other affects: What are measuring? How well are we doing? 1st International Workshop on Automatic Sentiment Analysis in the Wild (WASA’15), Xi’an, China.

Cohn, J.F. & Jeni, L (January 2016). Automated 3D face and gaze estimation and expression detection. The 6th Symposium on International Collaborative Laboratories, Pittsburgh, PA.

De la Torre, F., CHU, WS, Xiong, X., & Cohn, J.F. (May 2015), IntraFace. IEEE International Conference on Automatic Face and Gesture Detection. FG2015 Best Demo Award.

De la Torre, F., Girard, J.M., & Cohn, J.F. (September 2015). Facial expression analysis. IEEE International Conference on Biometrics Theory and Applications, Washington, DC.

Jeni, L., Kanade, T., & Cohn, J.F. (May 2015). ZFace. IEEE International Conference on Automatic Face and Gesture Detection, Ljubjana, Slovenia.

Phillips, J.P., Boyer, K., Beveridge, R., & Cohn, J.F. (May 2015). The Promise and Perils of Found Data. IEEE International Conference on Automatic Face and Gesture Detection, Ljubljana, Slovenia.

Related Publications

Chow, S. – M., Lu, Z., Sherwood, A., & Zhu, H.. (2016). Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm. Psychometrika, 81(1), 102 – 134. doi: 10.1007/s11336-014-9431-z

Lu, Z. – H., Chow, S. – M., & Loken, E.. (2016). Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences. Multivariate Behavioral Research, 1 – 21. doi: 10.1080/00273171.2016.1168279

Helm, J. Lee, Ram, N., Cole, P. M., & Chow, S. – M.. (2016). Modeling Self-Regulation as a Process Using a Multiple Time-Scale Multiphase Latent Basis Growth Model. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 635 – 648. doi: 10.1080/10705511.2016.1178580

McDonald, N. M., Baker, J. K., & Messinger, D. S.. (2016). Oxytocin and parent–child interaction in the development of empathy among children at risk for autism.. Developmental Psychology, 52(5), 735 – 745. doi: 10.1037/dev0000104

Ou, L., Chow, S. – M., Ji, L., & Molenaar, P. C. M.. (2016). An Examination of Initial Condition Specification in Autoregressive Latent Trajectory Models. Multivariate Behavioral Research.

Gangi, D. N., Messinger, D. S., Martin, E. R., & Cuccaro, M. L.. (2016). Dopaminergic variants in siblings at high risk for autism: Associations with initiating joint attention. Autism Research, 9(11), 1142 – 1150. doi: 10.1002/aur.2016.9.issue-11 10.1002/aur.1623

Calvo, R., D’Mello, S., Gratch, J., Kappas, A., Messinger, D. S., Duvivier, L. Lobo, et al.. (2015). The Oxford Handbook of Affective ComputingAffective Computing, Emotional Development, and Autism. (R. Calvo, D’Mello, S., Gratch, J., & Kappas, A.). Oxford University Press. doi: 10.1093/oxfordhb/9780199942237.001.0001 10.1093/oxfordhb/9780199942237.013.012

Ozonoff, S., Young, G. S., Landa, R. J., Brian, J., Bryson, S., Charman, T., et al.. (2015). Diagnostic stability in young children at risk for autism spectrum disorder: a baby siblings research consortium study. Journal of Child Psychology and Psychiatry, 56(9), 988 – 998. doi: 10.1111/jcpp.2015.56.issue-9 10.1111/jcpp.12421

Aviezer, H., Messinger, D. S., Zangvil, S., Mattson, W. I., Gangi, D. N., & Todorov, A.. (2015). Thrill of victory or agony of defeat? Perceivers fail to utilize information in facial movements. Emotion, 15(6), 791 – 797. doi: 10.1037/emo0000073

Chow, S. – M., Witkiewitz, K., Grasman, R. P. P. P., & Maisto, S. A.. (2015). The cusp catastrophe model as cross-sectional and longitudinal mixture structural equation models. Psychological Methods, 20(1), 142 – 164. doi: 10.1037/a0038962

Ruvolo, P., Messinger, D., & Movellan, J.. (2015). Infants Time Their Smiles to Make Their Moms Smile. (K. A. Bard)PLOS ONE, 10(9), e0136492. doi: 10.1371/journal.pone.0136492

Lu, Z. – H., Chow, S. – M., Sherwood, A., & Zhu, H.. (2015). Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data. The Annals of Applied Statistics, 9(3), 1601 – 1620. doi: 10.1214/15-AOAS846 10.1214/15-AOAS846SUPP

Vinciarelli, A., Esposito, A., André, E., Bonin, F., Chetouani, M., Cohn, J. F., et al.. (2015). Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions. Cognitive Computation, 7(4), 397 – 413. doi: 10.1007/s12559-015-9326-z

Hammal, Z., Cohn, J. F., & Messinger, D. S.. (2015). Head Movement Dynamics during Play and Perturbed Mother-Infant Interaction. IEEE Transactions on Affective Computing, 6(4), 361 – 370. doi: 10.1109/TAFFC.2015.2422702