Maternal care is a critical determinant of child development. However, our understanding of processes and mechanisms by which maternal behavior influences the developing human brain remains limited. Animal research has illustrated that patterns of sensory information is important in shaping neural circuits during development. Here we examined the relation between degree of predictability of maternal sensory signals early in life and subsequent cognitive function in both humans ( = 128 mother/infant dyads) and rats ( = 12 dams; 28 adolescents). Behaviors of mothers interacting with their offspring were observed in both species, and an entropy rate was calculated as a quantitative measure of degree of predictability of transitions among maternal sensory signals (visual, auditory, and tactile). Human cognitive function was assessed at age 2 y with the Bayley Scales of Infant Development and at age 6.5 y with a hippocampus-dependent delayed-recall task. Rat hippocampus-dependent spatial memory was evaluated on postnatal days 49-60. Early life exposure to unpredictable sensory signals portended poor cognitive performance in both species. The present study provides evidence that predictability of maternal sensory signals early in life impacts cognitive function in both rats and humans. The parallel between experimental animal and observational human data lends support to the argument that predictability of maternal sensory signals causally influences cognitive development.

Background Early life experiences have persisting influence on brain function throughout life. Maternal signals constitute a primary source of early life experiences, and their quantity and quality during sensitive developmental periods exert enduring effects on cognitive function and emotional and social behaviors. Here we examined if, in addition to established qualitative dimensions of maternal behavior during her interactions with her infant and child, patterns of maternal signals may contribute to the maturation of children's executive functions. We focused primarily on effortful control, a potent predictor of mental health outcomes later in life. Methods In two independent prospective cohorts in Turku, Finland ( N = 135), and Irvine, CA, USA ( N = 192) that differed significantly in race/ethnicity and sociodemographic parameters, we assessed whether infant exposure to unpredictable patterns of maternal-derived sensory signals portended poor effortful control. Outcomes In both the Irvine and Turku cohorts, unpredictable sequences of maternal behavior during infancy were associated with worse effortful control at one year of age. Longitudinal analyses demonstrated that this association persisted for as long as each cohort was assessed-until two years of age in the Turku cohort and to 9.5 years in the Irvine cohort. The relation of unpredictable maternal signals during infancy and the measures of executive function persisted after adjusting for covariates. Interpretations The consistency of our findings across two cohorts from different demographic backgrounds substantiated the finding that patterns, and specifically unpredictable sequences, of maternal behaviors may influence the development of executive functions which may be associated with vulnerability to subsequent psychopathology. Fund This research was supported by the National Institutes of Health (NIH) awards P50MH096889, HD051852, NS041298, HD02413, HD050662, HD065823, and by the FinnBrain funders: Academy of Finland (129839, 134950, 253270, 286829, 287908, 308176, 308252), Jane and Aatos Erkko Foundation, Signe and Ane Gyllenberg Foundation, Yrjö Jahnsson Foundation, and State Research Grants (P3498, P3654).

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Predictability of behavior has emerged an an important characteristic in many fields including biology, medicine, and marketing. Behavior can be recorded as a sequence of actions performed by an individual over a given time period. This sequence of actions can often be modeled as a stationary time-homogeneous Markov chain and the predictability of the individual's behavior can be quantified by the entropy rate of the process. This paper provides a comprehensive investigation of three estimators of the entropy rate of finite Markov processes and a bootstrap procedure for providing standard errors. The first two methods directly estimate the entropy rate through estimates of the transition matrix and stationary distribution of the process; the methods differ in the technique used to estimate the stationary distribution. The third method is related to the sliding-window Lempel-Ziv (SWLZ) compression algorithm. The first two methods achieve consistent estimates of the true entropy rate for reasonably short observed sequences, but are limited by requiring a priori specification of the order of the process. The method based on the SWLZ algorithm does not require specifying the order of the process and is optimal in the limit of an infinite sequence, but is biased for short sequences. When used together, the methods can provide a clear picture of the entropy rate of an individual's behavior.

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