Predicting Child Self-Regulation at 4 and 6 years by Evolved Developmental Niche in Infancy

• In Event: Poster Session 01
  In Poster Session: PS 01 Topic - Biological Processes: Psychophysiology

Abstract

Like other mammals, humans have an Evolved Developmental Niche (EDN), a system for caregiving that matches our offsprings’ maturational schedule (Gottlieb, 1998). For example, the intensive care provided during the neonatal period corresponds with the immaturity of neonates relative to other species (Trevathan, 2011). Anthropologists, through studies of nomadic foraging societies representative of humanity’s environment of evolutionary adaptedness (Hewlett & Lamb, 2005), describe EDN practices as extensive breastfeeding and affection, responsive care from multiple adults, positive social support, and self-directed play. The various components of this system influence biopsychosocial development (e.g., Harlow, 1958; Meaney, 2001), and by extension, likely play a role in regulatory systems. We hypothesized that EDN-consistent care in infancy would positively predict self-regulatory capacities longitudinally.

Participants were 157 mother-child dyads observed when children were approximately one year, ages 4 and 6 (T1 Maternal age: M=28.75, SD=5.39; child age: M=.89, SD=.06). At each timepoint, trained experimenters administered observational assessments and mothers completed a survey in exchange for gift cards.

At T1, measures included observations of maternal responsivity (e.g., KIPS; Comfort et al., 2011) and the self-report Family Life Behavior and Attitude Measure (FLAM; Narvaez et al., 2013), assessing maternal attitudes and behaviors regarding EDN components, such as attitudes toward responsive caregiving and time spent in companionate activities (e.g., reading, singing).

Measures at T2 (4 years) included a maternal survey (CBQ child inhibitory control; My Child: internalized conduct) and child self-regulation tasks (lab gift, head/toes).

At T3 (6 years), we measured child respiratory sinus arrhythmia (RSA), a biomarker for self-regulation (Beauchaine, 2015; Laborde, Mosley & Thayer, 2017), at rest (baseline), stress, and recovery.

Although all EDN components are optimal for development, we were uncertain which measures would predict longitudinal outcomes. Thus, we used Lasso-SEM variable selection machine learning, an iterative exploratory method, to identify relevant variables using the R-package RegSem (Jacobucci et al., 2020), relating standardized predictors to a latent variable formed by the four indicators of self-regulation at T2. Of 45 variables, Lasso selected 10 for the SEM analyses, which were used to predict 4-year self-regulation and 6-year RSA, controlling for maternal education and child gender, and yielded good fitting models (see Figures 1-2b). Sensitive and companionate caregiving and number of weekday caregivers positively and number of weekend caregivers negatively predicted self-regulation at 4 years; at 6 years, sensitive caregiving predicted mean levels of RSA—across all three conditions—and rate of RSA change (vagal flexibility); negative touch predicted higher stress levels from stress to recovery but not at baseline.

Although sensitive and responsive parenting are well-known predictors of self-regulation in early childhood, these models suggest that other aspects of EDN-consistent care influence both the development of top-down forms of self-regulation (executive function) as well as bottom-up regulatory processes (RSA). In addition, the predictive role of negative touch speaks to the need for models that include both positive and negative environmental predictors (Belsky & Beaver, 2010). Consistency with, and lower deviation from, the caregiving practices of the EDN might facilitate optimal formation of biopsychosocial systems such as self-regulation.

Authors

• Mary Tarsha, University of Notre Dame
  Presenting Author

• Angela Kurth, University of Notre Dame
  Non-Presenting Author

• Ying Cheng, University of Notre Dame
  Non-Presenting Author

• Tracy Gleason, Wellesley College
  Non-Presenting Author

• Darcia Narvaez, University of Notre Dame
  Non-Presenting Author