Paper
5 November 1993 Style as mechanism: from man to a map of the interval and back
Karen A. Selz, Arnold J. Mandell M.D.
Author Affiliations +
Abstract
Human subjects performed a set of mouse-driven computer tasks that required them to `remove dots from the screen' under fast and slow dot return (redraw) conditions. The binary partition of the 1D phase space generated a shift map, (sigma) , on two symbols. Assuming this sequence to be the symbolic dynamic product of a parametrized, symmetric tent map, the parity sequence associated with (sigma) was used in an inverse autoexpansion to recover the tent height parameter, (mu) , with 1 <EQ (mu) <EQ 2. We obtained a single convergent (mu) value for most subject sequences. In the case of T(mu) (X), the topological entropy is equal to the height of the tent, hT equals (mu) , and is monotonic increasing with respect to (mu) . We, therefore, may order the subjects (with convergent (mu) 's) in terms of the orbital complexity made available to them by their associated (mu) equals hT values. The distribution of (mu) 's in the sample could not be distinguished from a normal distribution. Linear multiple regression models (with higher order terms) were built, significantly relating (mu) f (fast redraw), (mu) s (slow redraw), and (mu) d equals ((mu) f - (mu) s) to established quantitative personality variables. The subject-map equivalence used here also allows the extension of sample lengths (unavailable in the experimental setting) in self-initiated tasks with widely differing N, while preserving topological properties of the subjects' performance.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Karen A. Selz and Arnold J. Mandell M.D. "Style as mechanism: from man to a map of the interval and back", Proc. SPIE 2036, Chaos in Biology and Medicine, (5 November 1993); https://doi.org/10.1117/12.162710
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Cited by 2 scholarly publications.
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KEYWORDS
Statistical modeling

Chaos

Statistical analysis

Biology

Medicine

Binary data

Error analysis

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