Zachary A. Caddick, PhD
Cognitive Scientist | Behavioral Researcher
Alexandria, VA
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ResearchGate
Alexandria, VA
Email
Github
Google Scholar
LinkedIn
ORCID
OSF
ResearchGate
At a Glance
I study how people think, make decisions, and behave—and use that understanding to help build systems, policies, and environments aligned with how people actually process information. My research spans experimental design and behavioral measurement to applied evaluation, across domains ranging from technology and AI to health and public policy.
Core Expertise
Behavioral Research & Measurement
I design and build measures that capture how people think and behave, including developing novel tools when existing ones don't fit the problem. My focus is on understanding the interaction between people, technology, and environment, and identifying where to intervene for optimal outcomes.
Human Cognition & Decision-Making
I study human cognition and decision-making across multiple levels, from individual differences like personality, expertise, and cognitive style, to the structural features of environments and information that shape how everyone thinks and behaves. My work is grounded in the insight that context often drives behavior more powerfully than individual traits, and that understanding both layers is what reveals where meaningful intervention is possible.
Applied Evaluation & Policy Translation
I translate empirical research and cutting-edge methodology into concrete, actionable recommendations, bridging the gap between what the science supports and what technical teams and decision-makers can actually implement. My work is grounded in the principle that good policy and good design should be inseparable from the evidence base behind them.
Professional Journey
My foundation is in cognitive and behavioral science, which I first developed in an undergraduate research lab studying creativity and individual differences and learned the craft of running studies, coding qualitative data, and measuring what makes people distinct from one another. My interest in reasoning, decision-making, and belief formation deepened during my master's at San Jose State, but it was a research appointment at NASA's Human Systems Integration (HSI) Division that fundamentally shifted how I see the world. At HSI I absorbed a core philosophy: human performance is best understood in the context of the systems and environments people operate within. It has shaped every research question I've asked since.
During my PhD at the University of Pittsburgh I joined a lab focused on how individuals learn cause-effect relationships, are influenced by different types of information exposure, and make decisions over time, which allowed me to bring my earlier work in reasoning and individual differences into that context. My research spanned causal learning and motivated reasoning, expertise acquisition and maintenance, and collective decision-making — each a different context for the same core question: how do people process information and arrive at decisions. A research appointment at OHSU extended my methods into sleep science and misinformation research, and most recently as an AAAS Science & Technology Policy Fellow at the National Science Foundation, I've applied this same perspective at a much larger scale — working within the Technology, Innovation, and Partnerships Directorate to understand how federal programs translate scientific investment into real-world outcomes.
Technical Skills
I work across the full research stack, from designing and building experimental platforms and data pipelines to cleaning, analyzing, and visualizing behavioral data. My primary tools are Python and R, which I use for everything from statistical modeling and data transformation to building interactive research applications.
Core Research Interests
Learning, reasoning, decision-making
Broader Research Interests
Causality, cognitive biases & heuristics, dual-process theories, expertise, political psychology, human factors, sleep, spaceflight, meta-sciences, qualitative & quantitative analysis, science policy, program evaluation, systems thinking, technology ecosystems
I study how people think, make decisions, and behave—and use that understanding to help build systems, policies, and environments aligned with how people actually process information. My research spans experimental design and behavioral measurement to applied evaluation, across domains ranging from technology and AI to health and public policy.
Core Expertise
Behavioral Research & Measurement
I design and build measures that capture how people think and behave, including developing novel tools when existing ones don't fit the problem. My focus is on understanding the interaction between people, technology, and environment, and identifying where to intervene for optimal outcomes.
- Featured Project: Motivated Reasoning in an Explore-Exploit Task (Caddick & Rottman, 2021, Cognitive Science)
- Built a full-stack behavioral microworld to simulate dynamic policy decision-making and measure how prior beliefs shape causal learning. Found robust evidence of motivated reasoning even when participants had direct financial incentives to reason accurately.
- Featured Project: When Beliefs and Evidence Collide (Caddick & Feist, 2021, Thinking & Reasoning)
- Developed a novel psychometric instrument to measure motivated reasoning about anthropogenic climate change, combining qualitative and quantitative methods. Found that cognitive style, personality, and ideology each independently predicted both reasoning bias and acceptance of climate science.
Human Cognition & Decision-Making
I study human cognition and decision-making across multiple levels, from individual differences like personality, expertise, and cognitive style, to the structural features of environments and information that shape how everyone thinks and behaves. My work is grounded in the insight that context often drives behavior more powerfully than individual traits, and that understanding both layers is what reveals where meaningful intervention is possible.
- Featured Project: Politically Motivated Causal Evaluations of Economic Performance (Caddick & Rottman, 2019)
- Examined how political identity shapes interpretation of identical objective economic data. Conservatives and liberals drew systematically different conclusions from the same graphs, but differences disappeared when political framing was removed, suggesting motivated reasoning is context-driven and can influence basic tasks like graph interpretation.
- Featured Project: Learning, Choice Consistency, and Voting System Preferences (Caddick, 2022)
- Studied how people form and update preferences about electoral systems through implicit learning rather than explicit deliberation. Found meaningful individual differences in choice consistency and learning trajectories across different voting structures.
Applied Evaluation & Policy Translation
I translate empirical research and cutting-edge methodology into concrete, actionable recommendations, bridging the gap between what the science supports and what technical teams and decision-makers can actually implement. My work is grounded in the principle that good policy and good design should be inseparable from the evidence base behind them.
- Featured Project: Cognitive Perspectives on Maintaining Physicians' Medical Expertise (Rottman, Caddick et al., 2023 — special edition, 5 papers)
- A series of five papers published in a special edition, examining how physicians acquire, maintain, and update expertise throughout their careers. The work recommends shifting toward distributed, routine learning practices. The research findings now inform how medical expertise is managed for over 700,000 physicians in the United States.
- Featured Project: Sleep Environment Recommendations for Future Spaceflight Vehicles (Caddick, Gregory & Flynn-Evans, 2016/2018)
- Synthesized evidence across noise, temperature, lighting, and air quality to develop formal sleep environment guidelines for NASA spaceflight vehicles. The recommendations were delivered to NASA-JSC engineers and factored directly into next-generation vehicle design specifications.
Professional Journey
My foundation is in cognitive and behavioral science, which I first developed in an undergraduate research lab studying creativity and individual differences and learned the craft of running studies, coding qualitative data, and measuring what makes people distinct from one another. My interest in reasoning, decision-making, and belief formation deepened during my master's at San Jose State, but it was a research appointment at NASA's Human Systems Integration (HSI) Division that fundamentally shifted how I see the world. At HSI I absorbed a core philosophy: human performance is best understood in the context of the systems and environments people operate within. It has shaped every research question I've asked since.
During my PhD at the University of Pittsburgh I joined a lab focused on how individuals learn cause-effect relationships, are influenced by different types of information exposure, and make decisions over time, which allowed me to bring my earlier work in reasoning and individual differences into that context. My research spanned causal learning and motivated reasoning, expertise acquisition and maintenance, and collective decision-making — each a different context for the same core question: how do people process information and arrive at decisions. A research appointment at OHSU extended my methods into sleep science and misinformation research, and most recently as an AAAS Science & Technology Policy Fellow at the National Science Foundation, I've applied this same perspective at a much larger scale — working within the Technology, Innovation, and Partnerships Directorate to understand how federal programs translate scientific investment into real-world outcomes.
Technical Skills
I work across the full research stack, from designing and building experimental platforms and data pipelines to cleaning, analyzing, and visualizing behavioral data. My primary tools are Python and R, which I use for everything from statistical modeling and data transformation to building interactive research applications.
Core Research Interests
Learning, reasoning, decision-making
Broader Research Interests
Causality, cognitive biases & heuristics, dual-process theories, expertise, political psychology, human factors, sleep, spaceflight, meta-sciences, qualitative & quantitative analysis, science policy, program evaluation, systems thinking, technology ecosystems