Motivation is important in almost every aspect of human behavior. When you make a decision, your choice is certainly influenced by your motivational state. When you study mathematics, your motivation to study mathematics clearly affects the way you learn it. Despite its obvious importance, empirical research on motivation has been segregated in different areas for long years, making it difficult to establish an integrative view on motivation. For example, I studied a number of motivation theories proposed in educational psychology (as my PhD is in educational psychology) but these theories are not connected with the motivational theories studied in social psychology or organizational psychology. Furthermore, the way motivation is defined and theorized is fundamentally different in cognitive/affective neuroscience (Murayama, in press). In other fields such as cognitive psychology, motivation has been normally treated as a nuisance factor that needs to be controlled (see Simon, 1994).
The times have changed, however. In recent years, researchers have recognized the importance of more unified and cross-disciplinary approach to study motivation (Braver et al., 2014). This multidisciplinary, multimethod pursuit, called Motivation Science, is now an emerging field (Kruglanski, Chemikova & Kopez, 2015). Our Motivation Science lab takes an integrative approach, drawing from multiple disciplines (e.g., cognitive, social and educational psychology, cognitive/social neuroscience) and multiple approaches (e.g., behavioral experiments, longitudinal data analysis, neuroimaging, meta-analysis, statistical simulation/computational modeling, network analysis ). We explore a number of overlapping basic and applied research questions with the ultimate goal of providing an integrated view on human motivation.
Motivation and learning
If you are motivated, you learn better and remember more of what you learned. This sounds like an obvious fact, but our lab showed that the reality is more nuanced. The critical fact is that not all motivations are created equal.
In the literature of achievement goals, for example, people study primarily for two different goals — to master materials and develop their competence, which are called mastery goals, and to perform well in comparison to others, which are called performance goals (Dweck, 1986; Nicholls, 1984). Mastery goals and performance goals represent the same overall quantity of motivation, but they are qualitatively distinct types of motivation. We conducted a series of behavioral experiments to examine how these two different types of motivation influence learning (Murayama & Elliot, 2011).
In the study, participants were engaged in a problem-solving task and received a surprise memory test related to the task. Critically, participants performed the problem-solving task with different goals. Participants in the mastery goal condition were told that the goal was to develop their cognitive ability through the task, whereas those in the performance goal condition were told that their goal was to demonstrate their ability relative to other participants. The participants in the performance goal condition showed better memory performance in an immediate memory test, but when the memory was assessed one week later, participants in the mastery goal condition showed better memory performance. These results indicate that performance goals help short-term learning, whereas mastery goals facilitate long-term learning.
That was a laboratory study where the learning situation was somewhat artificial. To further test whether mastery orientation facilitates long-term learning, we turned to an existing longitudinal survey dataset. In this study, we used longitudinal survey data on more than 3,000 schoolchildren from German schools (Murayama, Pekrun, Lichtenfeld & vom Hofe, 2013). Using latent growth curve modeling, we showed that items which focus on the performance aspect of learning (“In math I work hard, because I want to get good grades”) in Grade 7 predicted the immediate math achievement score whereas items focusing on the mastery aspect of learning (“I invest a lot of effort in math, because I am interested in the subject”) in Grade 7 predicted the growth in math achievement scores over three years. These results mirror our findings from the lab, providing convergent evidence that mastery-based motivation supports long-term learning whereas performance-based motivation only helps short-term learning.