Electronic Thesis and Dissertation Repository

Degree

Doctor of Philosophy

Program

Psychology

Supervisor

Dr. Richard W. J. Neufeld

Abstract

Engaging the environment through reason, humankind evaluates information, compares it to a standard of desirability, and selects the best option available. Stress is theorized to arise from the perception of survival-related demands on an organism. Cognitive efforts are no mere intellectual exercise when ontologically backed by survival-relevant reward or punishment. This dissertation examines the stressful impact, and countervailing peaceful impact, of environmental demands on cognitive efforts and of successful cognitive efforts on a person’s day-to-day environment, through mathematical modeling of ‘decisional control’. A modeling approach to clinical considerations is introduced in the first paper, “Clinical Mathematical Psychology”. A general exposition is made of the need for, and value of, mathematical modeling in examining psychological questions wherein complex relations between quantities are expected and observed. Subsequently, two documents are presented that outline an analytical and a computational basis, respectively, for assessing threat and its potential reduction. These two studies are followed by two empirical studies that instantiate the properties of the decisional control model, and examine the relation of stress and cognition within the context of psychometric, psychophysiological, and cognition-based dependent measures. Confirming the central hypothesis, results support the validity and reliability of best-option availability Pr(t1) as an index of cumulative situational threat E(t). Strong empirical support also emerges for disproportional obstruction of control by ‘uncertainty’, a lack of both information and control, compared to less obstruction of control by ‘no-choice’, a simple lack of control. Empirical evidence suggests this effect extends beyond reduction in control to an increase in cognitive efforts when even control is not present. This highlights an existing feature of the decisional control model, Outcome Set Size, an index of efforts at cognitive evaluation of potential encounters regardless of control availability. In addition to these findings, the precise specification of model expectancies and consequent experimental design, refinement of research tools, and proposal of an integrative formula linking empirical and theoretical results are unique contributions.

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