Daniel Randles

RESEARCH INTERESTS

I’m a social psychologist and Banting postdoctoral fellow at the University of Toronto. My current work focuses on the psychological processes underlying self-control. In particular, aiming to better describe self-control at a cognitive level, develop strategies for improving it at the individual level, and constructing models for understanding different patterns of self-control that emerge at societal levels.

I have two other ongoing research lines. One is focused on understanding how uncertainty is subjectively experienced, and the potential overlapping neurological processes involved in uncertainty, physical pain, social strife, and learning. The other focuses on how people’s behavior is shaped by their emotional reactions to critical events in life. This work has focused on the role of human self-conscious emotions, in particular shame, in affecting behavioral trajectories over time.

In addition to these two research lines, I am also working on a book project that examines the role of emotions in social life. The book will explore how different emotions shape our interactions with others, and how these emotional reactions can have long-term consequences for our social relationships. I am currently finishing up the manuscript and hope to have it published in the next year or two, also sometimes asking my favorite essay writers for help.

I am also working on a number of other projects, including a study of how people use social media to cope with stressful life events and another examining the role of emotions in decision-making. In addition to my research, I am also teaching classes on social psychology and research methods. I enjoy teaching and mentoring students, and I am always looking for ways to improve my teaching. In my spare time, I enjoy spending time with my family and friends, reading, and playing tennis.

My work along these lines involves a range of empirical tools, including experimental manipulations, pharmaceutical manipulations, behavioral coding, neurophysiological measurement techniques, machine learning, and secondary analysis of large-sample datasets.