Preschool physics: Using the invisible property of weight in causal reasoning tasks

Combining forces for causal reasoning: Children's predictions about physical interactions

Reasoning about causal relations is essential for children's early cognitive development. The current study investigated 4-year-olds' (N = 58) reasoning about complex causal physical interactions in terms of predicting the endpoint of motion. In an online task, children were presented with four configurations that involved different interactions of forces and consequently different patterns of motion. These were Cause (one force moving an object), Enable (a secondary force promoting the motion), Prevent-180° (an opposing force hindering the motion), and Prevent-90° (two-dimensional; a perpendicular force altering the motion). Each prediction was made in terms of either the Distance or Direction of the motion, which was novel in this task compared with previous assessments. Results revealed differences between the configurations, with Cause being the easiest and Prevent-90° being the most difficult to predict. Furthermore, predictions were more accurate when options were about the motion's Direction, whereas Distance options may have aggravated reasoning. The current study extends previous findings on children's intuitive physics and causal cognition by showing that accuracy in reasoning not only is dependent on the number of forces and dimensions at work but also interacts with estimating the motion's Distance and Direction.

Phy-Q as a measure for physical reasoning intelligence

Humans are well versed in reasoning about the behaviours of physical objects and choosing actions accordingly to accomplish tasks, while this remains a major challenge for artificial intelligence. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. We create a wide variety of distinct task templates, and we ensure that all the task templates within the same scenario can be solved by using one specific strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely local generalization and broad generalization. We conduct an extensive evaluation with human players, learning agents with various input types and architectures, and heuristic agents with different strategies. Inspired by how the human intelligence quotient is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered. Our evaluation shows that (1) all the agents are far below human performance, and (2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human-level Phy-Q score.

Elementary Students’ Reasoning in Drawn Explanations Based on a Scientific Theory

Constructing explanations of scientific phenomena is a high-leverage practice that promotes student understanding. In the context of this study, we acknowledge that children are used to receiving explanations from teachers. However, they are rarely encouraged to construct explanations about the causes and consequences of phenomena. We modified a strategy to elicit and analyze primary students’ reasoning based on scientific theory as a methodological advance in learning and cognition. The participants were fourth-graders of middle socioeconomic status in Chile’s geographical zone with high seismic risk. They drew explanations about the causes and consequences of earthquakes during a learning unit of eighteen hours oriented toward explanation-construction based on the Tectonic Plates Theory. A constant comparative method was applied to analyze drawings and characterize students’ reasoning used in pictorial representations, following the first coding step of the qualitative Grounded Theory approach. The results show the students expressed progressive levels of reasoning. However, several participants expressed explanations based on the phenomena causes even at an early stage of formal learning. More sophisticated reasoning regarding the scientific theory underpinning earthquakes was found at the end of the learning unit. We discuss approaching elementary students’ scientific reasoning in explanations based on theory, connected with context-based science education.

What happened? Do preschool children and capuchin monkeys spontaneously use visual traces to locate a reward?

The ability to infer unseen causes from evidence is argued to emerge early in development and to be uniquely human. We explored whether preschoolers and capuchin monkeys could locate a reward based on the physical traces left following a hidden event. Preschoolers and capuchin monkeys were presented with two cups covered with foil. Behind a barrier, an experimenter (E) punctured the foil coverings one at a time, revealing the cups with one cover broken after the first event and both covers broken after the second. One event involved hiding a reward, the other event was performed with a stick (order counterbalanced). Preschoolers and, with additional experience, monkeys could connect the traces to the objects used in the puncturing events to find the reward. Reversing the order of events perturbed the performance of 3-year olds and capuchins, while 4-year-old children performed above chance when the order of events was reversed from the first trial. Capuchins performed significantly better on the ripped foil task than they did on an arbitrary test in which the covers were not ripped but rather replaced with a differently patterned cover. We conclude that by 4 years of age children spontaneously reason backwards from evidence to deduce its cause.

Preschoolers’ STEM Learning on a Haptic Enabled Tablet

The research on children’s learning of science, technology, engineering, and math (STEM) topics from electronic applications (apps) is limited, though it appears that children can reasonably transfer learning from tablet games to particular tasks. We were interested to determine whether these findings would translate to the emerging technology of haptic feedback tablets. The research on haptic feedback technology, specifically, has found that this type of feedback is effective in teaching physics concepts to older students. However, haptic feedback has not yet been sufficiently explored with younger groups (e.g., preschoolers). To determine the effect of playing a STEM game enhanced with haptic technology on learning outcomes, we designed an experiment where preschool participants were randomly exposed to one of three different conditions: (a) STEM game with no haptic feedback (tablet), (b) STEM game enabled with haptic feedback (haptics), or (c) a puzzle game (control). Results revealed no significant differences in comprehension or transfer by condition. Results from this study contribute to the literature on the effectiveness of haptic feedback for preschool STEM learning.