No abstract

Whether supervised or unsupervised, human and machine learning is usually characterised as event-based. However, learning may also proceed by systems alignment in which mappings are inferred between systems, such as visual and linguistic systems. Systems alignment is possible because items that share similar visual contexts, such as a car and a truck, will also tend to share similar linguistic contexts. Because of the mirrored similarity relationships, the visual and linguistic systems and can be aligned at some later time absent either input. We considered whether children's early concepts are learned by systems alignment. We found that children's early concepts are close to optimal for inferring novel concepts through system alignment. Structurally, children's early concepts are distinguished by their dense semantic neighbourhoods. Artificial agents using these structural features were highly effective, including in domains that exclude children's early concepts. For children, system alignment and event-based learning appear complementary. Artificial systems can benefit from incorporating these developmental principles.

The purpose of the study was to discover the effects of physical activity on men's diets. The food consumption of 1306 men aged 50 to 69 years was studied using the dietary history method. The men were grouped in four classes according to their physical activity. With increasing activity their consumptions of cereals, potatoes, milk and milk products, fats and sugar increased. The men in East Finland used more milk, butter and fish than those in the West. The consumption of cereals, potatoes and eggs was higher in West than in East Finland. The intake of energy and energy yielding nutrients was greatly dependent on the physical activity but their contributions to the total energy intake were independent. The consumption of minerals and vitamins was adequate in all activity classes. The changes in the diet caused by physical activity seemed to be more quantitative than qualitative.

Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for system alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants mapped objects that varied on two visual features to locations that varied along two spatial dimensions. We manipulated whether the featural and spatial systems were \textit{aligned} or \textit{misaligned}. Although system alignment was not required to complete this supervised learning task, we found that participants learned more efficiently when systems aligned and that aligned systems facilitated zero-shot generalisation. We fit a variety of models to individuals' responses and found that models which included an offline unsupervised alignment mechanism best accounted for human performance. Our results provide empirical evidence that people align entire representation systems to accelerate learning, even when learning seemingly arbitrary associations between two domains.