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TextAnalyst processes textual data through what is termed "natural language text analysis." Using linguistic rules and "artificial neural network technology," the program mimics human cognitive analytical processes. It begins by processing each document as a sequence of symbols, generating a hierarchical semantic network structure based on the frequency of terms and the relationships between them. After analyzing the document, each term (or theme) within the network is assigned an individual statistical weight (range 0-100) relative to its importance within the entire text. Additionally, the relationships between terms are also assigned a statistical weight, in effect highlighting the strength of thematic associations. TextAnalyst then engages in the process of renormalization - adjusting the statistical weight of each term based on its relationship to others. The renormalized values are termed "semantic weights" and can be arranged into a semantic network. High semantic weights are indicative of a term or theme having considerable significance within the overall text. Inter-item weights, also presented in the figures to follow, suggest significant association between text themes.

TextAnalyst processes textual data through what is termed "natural language text analysis." Using linguistic rules and "artificial neural network technology," the program mimics human cognitive analytical processes. It begins by processing each document as a sequence of symbols, generating a hierarchical semantic network structure based on the frequency of terms and the relationships between them. After analyzing the document, each term (or theme) within the network is assigned an individual statistical weight (range 0-100) relative to its importance within the entire text. Additionally, the relationships between terms are also assigned a statistical weight, in effect highlighting the strength of thematic associations. TextAnalyst then engages in the process of renormalization - adjusting the statistical weight of each term based on its relationship to others. The renormalized values are termed "semantic weights" and can be arranged into a semantic network. High semantic weights are indicative of a term or theme having considerable significance within the overall text. Inter-item weights, also presented in the figures to follow, suggest significant association between text themes.

The pen and paper of Feynman's thought are just such feedback loops, physical machinery that shape the flow of thought and enlarge the boundaries of mind. Drawing upon recent work in psychology, linguistics, neuroscience, artificial intelligence, robotics, human-computer systems, and beyond, Supersizing the Mind offers both a tour of the emerging cognitive landscape and a sustained argument in favor of a conception of mind that is extended rather than "brain-bound." The importance of this new perspective is profound. If our minds themselves can include aspects of our social and physical environments, then the kinds of social and physical environments we create can reconfigure our minds and our capacity for thought and reason.