Background. Causes produce effects via underlying mechanisms that must be inferred from observable and unobservable structures. Preschoolers show sensitivity to mechanisms in machine-like systems with perceptually distinct causes and effects, but little is known about how children extend causal reasoning to the natural continuous processes studied in elementary school science, or how other abilities impact on this.Aims. We investigated the development of children's ability to predict, observe, and explain three causal processes, relevant to physics, biology, and chemistry, taking into account their verbal and non-verbal ability.Sample. Children aged 5-11 years (N = 107) from London and Oxford, with wide ethnic/linguistic variation, drawn from the middle/upper socioeconomic status (SES) range.Methods. Children were tested individually on causal tasks focused on sinking, absorption, and dissolving, using a novel approach in which they observed contrasting instances of each, to promote attention to mechanism. Further tasks assessed verbal (expressive vocabulary) and non-verbal (block design) ability.Results. Reports improved with age, though with differences between tasks. Even young participants gave good descriptions of what they observed. Causal explanations were more strongly related to observation than to prediction from prior knowledge, but developed more slowly. Non-verbal but not generic verbal ability predicted performance.Conclusions. Reasoning about continuous processes is within the capacity of children from school entry, even using verbal reports, though they find it easier to address more rapid processes. Mechanism inference is uncommon, with non-verbal ability an important influence on progress. Our research is the first to highlight this key factor in children's progress towards thinking about scientific phenomena.Causal cognitionthe ability to perceive and infer cause-effect relationslies at the core of scientific investigation and is equally crucial in everyday thinking. It revolves around the This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

In physics, the analysis of the space representing states of physical systems often takes the form of a layer-cake of increasingly rich structure. In this paper, we propose an analogous hierarchy in the cognition of spacetime. Firstly, we explore the interplay between the objective physical properties of space-time and the subjective compositional modes of relational representations within the reasoner. Secondly, we discuss the compositional structure within and between layers. The existing evidence in the available literature is reviewed to end with some testable consequences of our proposal at the brain and behavioral level.

Past research has largely ignored children's ability to conjointly manipulate spatial and temporal information, but there are indications that the capacity to do so may provide important support for reasoning about causal processes. We hypothesised that spatial-temporal thinking is central to children's ability to identify the invisible mechanisms that tie cause and effect together in continuous casual processes, which are focal in primary school science and crucial to understanding of the natural world. We investigated this in two studies (N = 107, N = 124), employing two methodologies, one shorter, the other more in depth. Further tasks assessed spatial-temporal (flow of liquid, extrapolation of relative speed, distance-timevelocity), spatial (two mental rotation, paper folding), verbal (expressive vocabulary), and nonverbal (block design) ability. Age dependent patterns were detected for both causal and predictor tasks. Two spatial-temporal tasks were unique and central predictors of children's causal reasoning, especially inference of mechanism. Nonverbal ability predicted the simpler components of causal reasoning. One mental rotation task predicted only young children's causal thinking. Verbal ability became significant when the sample included children from a wide range of socioeconomic backgrounds. Causal reasoning about continuous processes, including inferences of causal mechanism, appears to be within the reach of children from school entry age, but mechanism inference is uncommon. Analytic forms of spatial-temporal capacity seem to be important requirements for children to progress to this rather than merely perceptual forms.

Verbal and nonverbal forms of thinking exhibit widespread dissociation at neural and behavioral level. The importance of this for children's causal thinking and its implications for school science are largely unknown. Assessing 231 5-10-year-olds' responses: verbal ability predicted causal reasoning, but only at lower levels, while nonverbal ability was the strongest predictor at higher levels of causal inference. We also distinguished generic/scientific vocabulary use for 101 children to see if this furthered understanding. Use of scientific vocabulary predicted causal reasoning beyond generic, and connected more to nonverbal thinking. Parental education showed a marginally significant interaction with nonverbal ability, and was associated with its differential effects. The findings highlighted (1) the importance of elementary school science activities supporting application of nonverbal ability in thinking about causal processes, (2) the benefits of linking nonverbal imagery directly to scientific vocabulary, (3) shortcomings in understanding of the forms/sources of nonverbal ability and their role in learning.

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components.