Embodied Construction Grammar in simulation-based language understanding

Author(s):  
Benjamin K. Bergen ◽  
Nancy Chang
2016 ◽  
Vol 27 (2) ◽  
pp. 181-203 ◽  
Author(s):  
Nian Liu ◽  
Benjamin Bergen

AbstractEmbodied approaches to comprehension propose that understanding language entails performing mental simulations of its content. The evidence, however, is mixed. Action-sentence Compatibility Effect studies (Glenberg and Kaschak 2002) report mental simulation of motor actions during processing of motion language. But the same studies find no evidence that language comprehenders perform spatial simulations of the corresponding locations. This challenges simulation-based approaches. If locations are not represented in simulation, but are still understood, then simulation may be unnecessary for understanding. We conducted a Location-sentence Compatibility experiment, to determine whether understanders mentally simulate locations. People did indeed simulate locations, but only when sentences used progressive (and not perfect) grammatical aspect. Moreover, mental simulations of locations differed for language about concrete versus abstract events. These findings substantiate the role of mental simulation in language understanding, while highlighting the importance of the grammatical form of utterances as well as their content.


2017 ◽  
Vol 9 (2) ◽  
pp. 178-225 ◽  
Author(s):  
Luc Steels

Abstract Fluid Construction Grammar (FCG) is a fully operational computational platform for developing grammars from a constructional perspective. It contains mechanisms for representing grammars and for using them in computational experiments and applications in language understanding, production and learning. FCG can be used by grammar writers who want to test whether their grammar fragments are complete and coherent for the domain they are investigating (for example verb phrases) or who are working in a team and have to share grammar fragments with others. It can be used by computational linguists implementing practical language processing systems or exploring how machine learning algorithms can acquire grammars. This paper introduces some of the basic mechanisms of FCG, illustrated with examples.


2009 ◽  
Vol 23 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Astrid Wichmann ◽  
Detlev Leutner

Seventy-nine students from three science classes conducted simulation-based scientific experiments. They received one of three kinds of instructional support in order to encourage scientific reasoning during inquiry learning: (1) basic inquiry support, (2) advanced inquiry support including explanation prompts, or (3) advanced inquiry support including explanation prompts and regulation prompts. Knowledge test as well as application test results show that students with regulation prompts significantly outperformed students with explanation prompts (knowledge: d = 0.65; application: d = 0.80) and students with basic inquiry support only (knowledge: d = 0.57; application: d = 0.83). The results are in line with a theoretical focus on inquiry learning according to which students need specific support with respect to the regulation of scientific reasoning when developing explanations during experimentation activities.


2004 ◽  
Author(s):  
L. L. Kusumoto ◽  
◽  
R. M. Gehorsam ◽  
B. D. Comer ◽  
J. R. Grosse

Sign in / Sign up

Export Citation Format

Share Document