Peter Molenaar

Peter Molenaar

Distinguished Professor of Human Development & Family Studies

Graduate Faculty, Social Data Analytics

415 BBH Building
Email:

Education:

  1. Ph.D., 1981, Social Sciences, University of Utrecht, The Netherlands
  2. M.A., 1976, Mathematical Psychology, cum laude, University of Utrecht, The Netherlands
  3. M.A., 1976, Psychophysiology, cum laude, University of Utrecht , The Netherlands
  4. B.A., 1972, Psychology, cum laude, University of Utrecht, The Netherlands
  5. B.A., 1976, Philosophical Logic, University of Utrecht, The Netherlands

Biography:

The general theme of my work concerns the application of mathematical theories to solve substantive psychological issues. Some specific elaborations of this theme are the following.

1. An important aim of Psychology is to describe, explain and guide processes occurring at the level of individual subjects. I have proven that the appropriate methodology required for realizing this aim has to be based on person-specific analyses of intra-individual variation, i.e., time series analysis. The new person-specific methodology is being applied to a variety of psychological processes, including mother-child interaction, personality development, and cognitive aging. Additional applications to individual psycho-therapeutic processes and person-specific brain imaging are in preparation.

2. An important feature of person-specific methodology is the possibility to apply state-of-the-art engineering techniques, in particular computational control theory, in order to optimally guide learning and developmental processes as well as disease processes. Applications of control theory to patient-specific optimal treatment of diabetes type I and asthma patients are in progress.

3. Additional applications of mathematical theories to solve substantive psychological issues in my work include a) the use of artificial neural networks to investigate nonlinear epigenetic processes, b) innovative structural equation modeling techniques to analyze longitudinal data, and c) the use of nonlinear dynamical models of developmental stage transitions

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