Joyce S Pang

I received my B.A. training in psychology from Smith College, MA, USA, and my PhD in personality psychology from The University of Michigan, MI, USA.

As a personality psychologist, I am broadly interested in the influences of multiple social identities and individual differences on well-being, performance, and health psychology outcomes within different social contexts. In all my research, I adopt a person x situation perspective to understand how individual differences predict different reactions within different social contexts, which in turn lead to important personal and social outcomes.

To this end, I have two major lines of research. First, I am a motivation researcher who develops, validates, and refines methods of assessing implicit motives. In this line of work, I publish and investigate best practices for using the Picture Story Exercise, the most commonly used measure of implicit motives. I have also explored the use of machine learning for automating the laborious content coding process that is involved in scoring implicit motives in text. Second, I investigate how individual differences (such as in motivation and in social identities) can affect performance, interpersonal outcomes, and physiological and emotional changes during motive-relevant contexts, I have explored the intersection between the person and the situation on a wide range of personal and social outcomes such as addictive behaviors, body image, internet radicalization, consumerism, medical adherence, adolescent aggression, and inter-group relations.

Active Projects

Motives and Emotions

This is an experimental study examining the relationship between implicit motives and emotions.

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Measuring Motives

This area of work aims to design and validate a content coding based system for assessing implicit Hope of Success and Fear of Failure motivation. It is also involved with developing best-practices for the measurement of motives using the Picture Story Exercise.

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Measuring Motives using Machine Learning
This area of work involves the development of an automated process for scoring implicit motives in running text, by applying the techniques of deep learning, neural networks, and natural language processing.

Visit www.implicitmotives.com to learn more.

Active Projects

​Motives and Emotions

Measuring Motives

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Motives and Emotions