Algorithms for Determining Thinking Styles

Algorithms for Determining Thinking Styles

DOI: 10.4018/978-1-4666-0972-3.ch005
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Abstract

The algorithms used to identify thinking style patterns derive from Davis’s theoretical construct of RTS. As such, we can demonstrate that they are capturing predicable patterns of human behavior. These patterns are based upon the priority and combination of ways in which individuals of a particular style habitually engage the order and direction of thoughts (sequence), confront options (intensity), and use time (duration). These categories are engaged by means of action patterns comprised of simple or complex repeating, and random or deliberate varying. Identifying habitual inferencing patterns may lead to a better understanding of decision-making and other fields of inquiry.
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Introduction

Employing algorithms to analyze habitual non-verbal inference patterns may strike some readers as misguided and perverse—due to the ubiquity of the fact/value distinction, which posits an ontological difference between qualities and quantities. Modern scientists secure inferences in every field by transforming selected qualities of experience into quantities that can be related and measured (Tristan, 2002).

David Berlinski provides an image of a feed-forward neural net and a brief introduction for the non-mathematician in his book, The Advent of the Algorithm (Berlinski, 2001, pp. 258-270). The algorithms used to analyze results from the Davis Non-Verbal Assessment (DNV) describe macro-inferencing processes and capture relatively predicable patterns of human behavior over intermediate to long-term periods1. For the application of the DNV, trained coders observe and identify specific task behaviors. Chiasson’s computer program (Chiasson, 2001) then analyzes these observations to identify the resulting style pattern for that individual (Chiasson, Malle, & Simmons, 2003)2. Without intervention, these inferencing habits described by the theory of Relational Thinking Styles (RTS) are relatively stable over time (although severe emotional trauma may temporarily alter thinking ability)3. Indentifying habit patterns facilitates prediction of a range of likely future outcomes enable the prediction of a range of likely future outcomes and because each element of each style is linked to behavior exhibited during standardized testing conditions the analysis is of empirical verification (Tristan, 2002).

This chapter addresses the construction of the algorithms that define the four habitual inferencing styles identified by the DNV4: Transient, Direct, Analytical, and Relational. These inferencing styles are identified in the context of each of three stages of a thinking process: Discerning, Goal-Setting, and Problem-Solving. For background, we summarize the thinking habits identified by RTS, the advantages and disadvantages of various combinations of thinking styles, as an example of the way they are assessed by the DNV. The main objective of this chapter is to discuss the algorithms used by Chiasson’s (2001) computer analysis program to determine thinking style patterns, which are characterized by the categories of sequence, intensity, and duration.

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