Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

Embedding Languages Without Breaking Tools

Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics.

Practical Dynamic Grammars for Dynamic Languages

Grammars for programming languages are traditionally specified statically. They are hard to compose and reuse due to ambiguities that inevitably arise. PetitParser combines ideas from scannerless parsing, parser combinators, parsing expression grammars and packrat parsers to model grammars and parsers as objects that can be reconfigured dynamically. Through examples and benchmarks we demonstrate that dynamic grammars are not only flexible but highly practical.