Kendra Seaman

Assistant Professor - Behavioral and Brain Sciences
+1 (972) 883-3783
The Aging Well Laboratory
Tags: aging decision making neuroimaging

Professional Preparation

Ph.D. - Applied Experimental Psychology
Catholic University of America - 2015
MA - Psychology
Catholic University of America - 2010
BA - Psychology
University of Kansas - 2002
BA - Biology
University of Kansas - 2002


Partial-volume correction increases estimated dopamine D2-like receptor binding potential and reduces adult age differences - Journal Article
The Effect of Delay Duration on Delay Discounting across Adulthood 2021 - Other
Adult age differences in subjective responses to dynamic socioemotional incentives 2020 - Other
Decision making across adulthood during social distancing 2020 - Other
Temporal Discounting Across Adulthood: A Systematic Review and Meta-analysis 2020 - Other
Boundary Conditions for the Positive Skew Bias 2020 - Other
Reproducibility of the correlative triad among aging, dopamine receptor availability, and cognition 2019 - Journal Article
Development and Validation of Social Motivation Questionnaire 2019 - Journal Article
Differential regional decline in dopamine receptor availability across adulthood: Linear and nonlinear effects of age 2019 - Journal Article
Individual Differences in Dopamine Are Associated with Reward Discounting in Clinical Groups But Not in Healthy Adults 2019 - Journal Article


Assistant Professor
University of Texas at Dallas [2019–Present]
Postdoctoral Research
Duke University [2017–2019]
Postdoctoral Research Fellow
Yale University [2015–2017]


The effect of differential delay on time preferences across adulthood
• Shelby Leverett, UT Dallas
• Christopher (Dominic) Garza, UT Dallas
• Kendra Seaman, UT Dallas

Many decisions involve considering a smaller sooner option and a larger later option. In these situations, people tend to devalue the later option, which is known as temporal discounting. Prior research has shown mixed age effects, with some studies suggesting that older adults discount more than young adults and other studies showing the opposite pattern. We posit that one reason for these heterogeneous results is that prior studies have used a mixture of time delays. We predict that older adults may discount more than younger adults, but only for longer time delays due to older adults’ greater uncertainty about the distant future.

OSF preregistration includes details about the study design, hypotheses, and analysis code.
Affective and Cognitive Mechanisms of Skewed Decision Making Across Adulthood
• Kendra Seaman, UT Dallas

This research seeks to improve our understanding of adult age differences in decision making when facing large, but unlikely gains coupled with small, but more likely losses, or positively-skewed gambles.We will examine the affective and cognitive mechanisms underlying this process, as well as clarify the conditions under which individuals display a bias towards positively-skew gambles. Our findings will be relevant across a range of disciplines as society faces increasing challenges and opportunities of an aging population.

Do Age Differences in Associative Learning and Stimulus Generalization Lead to Age Differences in Trust?
• Brittany Cassidy, UNC-Greensboro
• Kendra Seaman, UT Dallas
• Jessica Cooper, Emory University
• Daisy Burr, Duke University
• Alex Christensen, UNC-Greensboro

When meeting others, people make quick decisions on whether to trust people or not that affect decision-making and that pose serious consequences for physical, interpersonal, and financial well-being. However, older adults exhibit excessive trust relative to younger adults and this excessive trust leaves older adults particularly such serious consequences, including financial fraud. One explanation for their excessive trust is that older adults may learn to trust differently than do younger adults. The study adopts an interdisciplinary approach to examine this possibility at both the behavioral and neural levels.

This study is funded by SRNDNA. OSF preregistration includes details about the hypotheses and study design.
Temporal Discounting and Brain Age
  • Eduardo Betancourt, UT Dallas
  • Christopher "Dominic" Garza, UT Dallas
  • Kendra Seaman, UT Dallas
Brain-predicted age (also known as “Brain Age”) estimates the age of individuals from structural brain images using machine learning algorithms trained on a large healthy reference sample. Having an older-appearing brain has been linked to a variety of outcomes, including lower fluid intelligence (Cole et al., 2017). 

Our prior research has shown there are not consistent age differences in temporal discounting in terms of behavior (Seaman et al.,2016; Seaman et al 2018; Seaman et al., under review) and in terms of the neural correlates of value while making temporal discounting decisions (Seaman et al., 2018). However, this lack of consistent age differences may be due to the heterogeneity within older adult populations. Other studies have observed large variability in neural representation of value within older adult populations (Halfmann et al., 2015). Brain Age is one possible way to capture the heterogeneity in aging-related changes in the brain. 

Can we explain some of the heterogeneity observed in temporal discounting behavior using Brain Age? The objective of this research study is to gain new insights into temporal discounting. Specifically, we seek to determine if the physiological appearance of the brain as it changes with time (Brain Age) might be a better predictor of temporal discounting than chronological age.