To What Extent Is General Intelligence Relevant to Causal Reasoning? A Developmental Study.

To what extent general intelligence mechanisms are associated with causal thinking is unclear. There has been little work done experimentally to determine which developing cognitive capacities help to integrate causal knowledge into explicit systems. To investigate this neglected aspect of development, 138 children aged 5-11 studying at mainstream primary schools completed a battery of three intelligence tests: one investigating verbal ability (WASI vocabulary), another looking at verbal analogical (Verbal Analogies subset of the WRIT), and a third assessing non-verbal/fluid reasoning (WASI block design). Children were also interviewed over the course of three causal tasks (sinking, absorption, and solution), with the results showing that the developmental paths exhibited uneven profiles across the three causal phenomena. Children consistently found that explaining solution, where substances disappeared toward the end of the process, was more challenging. The confirmatory factor analyses suggested that the impact of cognitive ability factor in explicitly identifying causal relations was large. The proportion of the direct effect of general intelligence was 66% and it subsumed the variances of both verbal measures. Of this, 37% was the indirect effect of age. Fluid reasoning explained a further 28% of the variance, playing a unique role in causal explanation. The results suggested that, overall, cognitive abilities are substantially related to causal reasoning, but not entirely due to developmental differences in "g" during the age periods studied.

Causal reasoning without mechanism.

Unobservable mechanisms that tie causes to their effects generate observable events. How can one make inferences about hidden causal structures? This paper introduces the domain-matching heuristic to explain how humans perform causal reasoning when lacking mechanistic knowledge. We posit that people reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones. When thinking about a cause, people tend to think about possible effects that participate in the same domain, and vice versa. To explore the specific domains that people use, we asked people to cluster artifacts. The analyses revealed three commonly employed mechanism domains: the mechanical, chemical, and electromagnetic. Using these domains, we tested the domain-matching heuristic by testing adults' and children's causal attribution, prediction, judgment, and subjective understanding. We found that people's responses conform with domain-matching. These results provide evidence for a heuristic that explains how people engage in causal reasoning without directly appealing to mechanistic or probabilistic knowledge.

The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes.

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.

Cognitive Structures of Space-Time.

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.

The role of spatial and spatial-temporal analysis in children's causal cognition of continuous processes.

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-time-velocity), 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.

Children's reasoning about continuous causal processes: The role of verbal and non-verbal ability.

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.