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The global burden of early life adversity (ELA) is profound. The World Health Organization has estimated that ELA accounts for almost 30% of all psychiatric cases. Yet, our ability to identify which individuals exposed to ELA will develop mental illness remains poor and there is a critical need to identify underlying pathways and mechanisms. This review proposes unpredictability as an understudied aspect of ELA that is tractable and presents a conceptual model that includes biologically plausible mechanistic pathways by which unpredictability impacts the developing brain. The model is supported by a synthesis of published and new data illustrating the significant impacts of patterns of signals on child development. We begin with an overview of the existing unpredictability literature, which has focused primarily on longer patterns of unpredictability (e.g. years, months, and days). We then describe our work testing the impact of patterns of parental signals on a moment-to-moment timescale, providing evidence that patterns of these signals during sensitive windows of development influence neurocircuit formation across species and thus may be an evolutionarily conserved process that shapes the developing brain. Next, attention is drawn to emerging themes which provide a framework for future directions of research including the evaluation of functions, such as effortful control, that may be particularly vulnerable to unpredictability, sensitive periods, sex differences, cross-cultural investigations, addressing causality, and unpredictability as a pathway by which other forms of ELA impact development. Finally, we provide suggestions for prevention and intervention, including the introduction of a screening instrument for the identification of children exposed to unpredictable experiences.


This article was originally published in Journal of Child Psychology and Psychiatry in 2024.

jcpp13958-sup-0001-tables1.docx (64 kB)
Table S1. A survey of studies examining the relevance of unpredictability across cultures and diverse populations.


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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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