Date of Award

January 2025

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Joan Monin

Abstract

Introduction: Motivational incentives such as rewards and penalties shape cognitive performance, yet little is known about how these effects interact with physiological responses. This study examined how different types of incentives influence behavioral performance and cardiovascular reactivity during a cognitive control task, drawing on the Biopsychosocial (BPS) model of challenge and threat. Methods: A total of 141 adults completed a Stroop task under three conditions: Low Reward–Low Penalty (LRLP), High Reward–Low Penalty (HRLP), and Low Reward–High Penalty (LRHP). Physiological signals (e.g., heart rate, inter-beat interval, pre-ejection period, cardiac output) were recorded continuously. Task order was counterbalanced (HRLP first vs. LRHP first). Behavioral performance was measured by accuracy and reaction time. Mixed-effects models tested the effects of incentive condition, task order, and physiological reactivity on performance. Results: Participants responded faster in the reward-based condition but more accurately in the penalty-based condition. Incentive conditions did not influence physiology on their own, but effects emerged depending on task order. When reward came first, the following penalty-based condition triggered stronger cardiovascular responses (increased HR, reduced IBI, and PEP), and vice versa for the reward-based condition when penalty came first. Physiological reactivity predicted higher accuracy only under penalty-based conditions, especially for participants who began with the reward condition or showed stronger early physiological reactivity. Conclusion: Motivational incentives and their sequence shape both physiological reactivity and cognitive performance. Starting with rewards may support later performance under penalty conditions by enhancing physiological readiness. These findings offer insight into how motivational framing and ordering influence decision-making and may help reduce errors in applied settings like healthcare.

Comments

This thesis is restricted to Yale network users only. It will be made publicly available on 06/16/2027

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