A Subsymbolic Model of Complex Story Understanding
نویسندگان
چکیده
A computational model of story understanding is presented that is able to process stories consisting of multiple scripts. This model is built from subsymbolic neural networks, but unlike previous such models, it can handle stories of variable structure and length. The model can successfully parse and paraphrase script-based stories that share long sequences of common events, with no confusion between the stories. It also exhibits several aspects of human behavior, including robustness to small changes in the sequence of events and emotion priming effects in response to ambiguous cues. It can therefore serve as a foundation for testing theories of normal and impaired story processing in humans.
منابع مشابه
A Subsymbolic Model of Language Pathology in Schizophrenia
This paper reports first results of a simulation of language pathology in schizophrenia. Using DISCERN, a subsymbolic model of story understanding and recall, the impact of different simulated lesions hypothesized to underlie schizophrenia is investigated. In response to excessive connection pruning, the model reproduces symptoms of delusions and disorganized language seen in schizophrenia, as ...
متن کاملA symbolic/subsymbolic interface protocol for cognitive modeling
Researchers studying complex cognition have grown increasingly interested in mapping symbolic cognitive architectures onto subsymbolic brain models. Such a mapping seems essential for understanding cognition under all but the most extreme viewpoints (namely, that cognition consists exclusively of digitally implemented rules; or instead, involves no rules whatsoever). Making this mapping reduces...
متن کاملRainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...
متن کاملSubsymbolic natural language processing - an integrated model of scripts, lexicon, and memory
Much connectionist research in natural language processing has been concerned with isolated aspects of understanding language. Very few researchers have attempted to build comprehensive computational models that are biologically and psychologically plausible and that incorporate the components necessary for modeling and testing various complex high-level cognitive phenomena. Miikkulainen's book...
متن کاملConnections, Communication and Collaboration in Healthcare’s Complex Adaptive Systems; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”
A more sophisticated understanding of the unpredictable, disorderly and unstable aspects of healthcare organisations is developing in the knowledge translation (KT) literature. In an article published in this journal, Kitson et al introduced a new model for KT in healthcare based on complexity theory. The Knowledge Translation Complexity Network Model (KTCNM) provides a fresh perspective by mak...
متن کامل