scholarly journals Instance theory predicts information theory: Episodic uncertainty as a determinant of keystroke dynamics.

2019 ◽  
Vol 73 (4) ◽  
pp. 203-215 ◽  
Author(s):  
Matthew J. C. Crump ◽  
Walter Lai ◽  
Nicholaus P. Brosowsky
Keyword(s):  
Information Theory ◽  
Keystroke Dynamics ◽  
Instance Theory

scholarly journals Instance theory predicts information theory: Episodic uncertainty as a determinant of keystroke dynamics

2018 ◽  
Author(s):  
Matthew John Charles Crump ◽  
Walter Lai ◽  
Nicholaus Brosowsky
Keyword(s):  
Information Theory ◽  
Word Length ◽  
Process Model ◽  
Letter Position ◽  
Keystroke Dynamics ◽  
General Process ◽  
Skilled Performance ◽  
Instance Theory ◽  
N Gram ◽  
Context Specific

How does prior experience shape skilled performance in structured environments? We use skilled typing of natural text to evaluate correspondence between performance (keystroke timing) and structure in the environment (letter uncertainty). We had ~350 typists copy-type english text. We reproduced Ostry's (1983) analysis of interkeystroke interval as a function of letter position and word length, that showed prominent first-letter and mid-word slowing effects. We propose a novel account that letter position and word length effects on keystroke dynamics reflect informational uncertainty about letters in those locations, rather than resource limited planning/buffering processes. We computed positional uncertainty for letters in all positions of words from length one to nine using Google's n-gram database. We show that variance in inter-keystroke interval by letter position and word length tracks natural variation in letter uncertainty. Finally, we provide a model showing how a general learning and memory process could acquire sensitivity to patterns of letter uncertainty in natural english. In doing so, we draw an equivalence between Logan's (1988) instance theory of automatization and Shannon's measure of entropy (H) from information theory. Instance theory's predictions for automatization as a function of experience follow exactly the uncertainty in the choice set being automatized. As a result, instance theory stands as a general process model explaining how context-specific experiences in a structured environment tune skilled performance.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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