Abstract
Recent studies on the organization of conceptual knowledge in the human brain have reported the remarkable ability to predict the word a person is thinking about, or the picture a person is seeing, from their brain activity. By using Machine Learning techniques to analyze neuroimaging data, researchers have been able to find stable patterns of brain activity across different people. These patterns allow computer algorithms to identify the brain activity associated with a specific word or picture. The studies have also reported striking commonalities across different people’s neural signature for the conceptual knowledge associated with thinking about words. The communal characteristic of the organization of meaning allows for the prediction of what one person is thinking about based on another person’s brain activity. The results of some of these studies and the implications for research on cognitive processes and second language learning/acquisition are discussed. Preliminary results from a brain imaging study on cross-language thought identification are also presented. These recent findings in neuroimaging of human semantics suggest the presence of a common semantic neural representation across people and across languages.
Highlights
When we read a word, we activate its meaning, thereby bringing various associated concepts into an activated state
In the light of some of the most recent cognitive neuroscience studies on bilingualism, the present paper addresses the use of artificial intelligence techniques (Machine Learning) and neuroimaging data as a promising combination for the identification and prediction of the neural underpinnings of the representation of word meaning in the brain
The combination of machine learning analysis techniques and brain imaging data provides a glimpse into the future of cognitive neuroscience studies of bilingual brain activation
Summary
When we read a word, we activate its meaning, thereby bringing various associated concepts into an activated state. In the light of some of the most recent cognitive neuroscience studies on bilingualism, the present paper addresses the use of artificial intelligence techniques (Machine Learning) and neuroimaging data as a promising combination for the identification and prediction of the neural underpinnings of the representation of word meaning in the brain. These studies have been construed as the first steps towards “mind reading,” “brain reading” (Cox and Savoy, 2003), or as the Carnegie Mellon researchers like to call them, “thought identification” experiments (see interview with Marcel Just and Tom Mitchell in Frank, 2009). Partial results from a new study on cross-language thought identification are presented and discussed (Buchweitz et al, 2009)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
