Four-Day-Old Human Neonates Look Longer at Non-Biological Motions of a Single Point-of-Light

BACKGROUND: Biological motions, that is, the movements of humans and other vertebrates, are characterized by dynamic regularities that reflect the structure and the control schemes of the musculo-skeletal system. Early studies on the development of the visual perception of biological motion showed that infants after three months of age distinguished between biological and non-biological locomotion. METHODOLOGY/PRINCIPAL FINDINGS: Using single point-light motions that varied with respect to the “two-third-power law” of motion generation and perception, we observed that four-day-old human neonates looked longer at non-biological motions than at biological motions when these were simultaneously presented in a standard preferential looking paradigm. CONCLUSION/SIGNIFICANCE: This result can be interpreted within the “violation of expectation” framework and can indicate that neonates' motion perception — like adults'—is attuned to biological kinematics

Structure-mapping processes enable infants' learning across domains including language

Humans have an astounding ability to acquire new information. Like many other animals, we can learn by association and by perceptual generalization. However, unlike most other species, we also acquire new information by means of relational generalization and transfer. In this chapter, we explore the origins of a uniquely developed human capacity-our ability to learn relational abstractions through analogical comparison. We focus on whether and how infants can use analogical comparison to derive relational abstractions from examples. We frame our work in terms of structure-mapping theory, which has been fruitfully applied to analogical processing in children and adults. We find that young infants show two key signatures of structure mapping: first, relational abstraction is fostered by comparing alignable examples, and second, relational abstraction is hampered by the presence of highly salient objects. The studies we review make it clear that structure-mapping processes are evident in the first months of life, prior to much influence of language and culture. This finding suggests that infants are born with analogical processing mechanisms that allow them to learn relations through comparing examples

SEON: A pyramid of ontologies for software evolution and its applications

The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization