The simulation theory of human development posits that humans use simulations or “mental models” built on predictive heuristics to understand the world around them. This theoretical framework has gained substantial empirical support through recent advances in predictive processing research, which provides precise computational and neural mechanisms for how the brain constructs and deploys mental simulations (Hohwy, 2020; Seth & Bayne, 2022; Sprevak & Smith, 2023). Martin Heidegger in Being and Time (1927/1962) presented an ontology of human existence which proposed that humans understand the world through their practical engagement with it rather than through detached observation. This was perhaps the earliest modern expression of the idea that humans use mental simulations to understand the world, an insight now validated by contemporary embodied and enactive cognitive science (Gallagher, 2023). Like other theories that have attempted to conceptualize human thinking, the most powerful support for Heidegger’s idea comes from the study of children (Bruner, 1986; Goddu & Gopnik, 2024; Köster et al., 2020; Spelke, 1994).
One researcher whose work illuminated this connection between the development of mental models and problem-solving was Jerome Bruner (1986, 2009). Bruner (1986) is known for his work on the development of representation, which proposed that children construct mental models or simulations of the world through the process of abstraction. Bruner’s research provided evidence that humans use mental simulations to understand the world. Recent neuroscience research has demonstrated that these mental models employ common neural spatial structures regardless of whether they represent spatial, numerical, or abstract content (Alfred et al., 2020). This work was expanded upon by Alan Leslie (1994), a theory of mind researcher, who proposed that children develop an understanding of other people’s minds by simulating their own mental states in response to their behaviors. In other words, humans use their own cognitions to produce mental simulations of other people’s mental states and use that knowledge to regulate their behavior. Contemporary research has substantially advanced this framework, demonstrating that theory of mind develops through multiple stages from implicit understanding in infancy to explicit reasoning in middle childhood (Rakoczy, 2022; Sodian et al., 2020, 2024), with parental mental state talk playing a crucial role in scaffolding children’s capacity to simulate others’ minds (Devine & Hughes, 2019).
How sensory information is guided by heuristics to create a useful simulation of our environment is strikingly analogous to what is seen in children when they use their prior knowledge about their experiences to create useful simulations that regulate their behavior. Research across fields seems to be converging on a process that uses past experience to actively construct predictive perceptions to guide and support our actions (Hohwy, 2020; Pezzulo et al., 2024; Seth, 2020). Contemporary predictive processing frameworks propose that the brain functions as a hierarchical prediction machine, continuously generating predictions about sensory input and updating internal models based on prediction errors—the mismatch between predicted and actual sensory signals (Hohwy & Seth, 2020; Köster et al., 2020; Walsh et al., 2020). Infants and young children exemplify this process, functioning as intuitive scientists who actively construct causal models, seek information to reduce uncertainty, and engage in counterfactual reasoning to understand both physical causation and social minds (Basch et al., 2024; Goddu & Gopnik, 2024; Wang et al., 2021).
This connection is important because it extends through both the biological and psychological developmental processes and could indicate there is an interactive problem-solving process that transcends both. The integration of predictive processing with active inference, essentially using a framework where organisms minimize uncertainty through both perception and action, provides unified computational principles explaining how organisms from infancy onward build, test, and refine generative models of their physical and social worlds (Friston et al., 2017; Pezzulo et al., 2024; Smith et al., 2020). Active inference demonstrates how children develop adaptive models through embodied exploration, with learning and action existing in circular dependence rather than as separate processes (Ciaunica et al., 2024; Hamburg et al., 2024; Tschantz et al., 2020). Such interdisciplinary correlations, spanning neuroscience (Alfred et al., 2020; Ramstead et al., 2022), developmental psychology (Goddu & Gopnik, 2024; Rakoczy, 2022), computational modeling (Sajid et al., 2021; Tschantz et al., 2023), and embodied cognition research (Gallagher, 2023; Farina, 2021), lend strong support to the child scientist theory.
However, perhaps the most powerful evidence that there is an underlying biological heuristic compelling the problem-solving behavior of children can be found in the roots of our modern development of a scientific method. Children with greater uncertainty in their intuitive theories actively seek domain-relevant information to test and refine their mental models, paralleling scientific hypothesis testing (Wang et al., 2021). Preschoolers construct abstract theoretical frameworks incorporating multiple causal factors, demonstrating sophisticated theory-building capacities previously attributed only to older children (Muradoglu & Cimpian, 2020). The developmental trajectory from implicit prediction-based learning in infancy through explicit causal reasoning and counterfactual simulation in childhood reveals the emergence of progressively more sophisticated generative models (Keil & Kominsky, 2024; Nagai, 2019). These findings demonstrate that the scientific method is not imposed from without but emerges naturally from the brain’s fundamental architecture as a prediction machine (Hohwy, 2020; Seth & Bayne, 2022). Children’s spontaneous engagement in hypothesis generation, information-seeking to reduce uncertainty, model revision based on prediction errors, and counterfactual reasoning all reflect core features of scientific inquiry grounded in the biological imperative to minimize prediction error and maintain accurate models of the world (Goddu & Gopnik, 2024; Köster et al., 2020; Pezzulo et al., 2024).
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