Many models of human behavior indicate the importance of rewards, but humans are endowed with a remarkable capacity to motivate themselves in the absence of extrinsic incentives (e.g., money). We aim to address the fundamental question of how people can initiate and sustain long-term task engagement without explicit rewards.
Key words: Intrinsic motivation, curiosity, interest, reinforcement-learning, decision making, personality development
We often fail to regulate our motivation. We also often fail to regulate the motivation of other people (e.g., teachers fail to motivate students). Why do these situations occur? Inspired by the work on human metacognition, we suspect that we have a limited capacity to understand how motivation operates—which we call “metamotivation.” The work in this line of research aims to understand the nature and the consequences of metamotivation.
Key words: Metacognition, self-regulation
We often regard motivation as personal, but such personal motivation can be socially constructed. If your friend likes mathematics, you may come to like mathematics by observing and interacting with your friend. We call this motivation contagion and aim to examine whether and how the motivation contagion takes place.
Key words: Social learning, social network, synchrony
Researchers have collected many large-sample panel data to understand human motivation. In addition, in the current era of digitalization, we now have access to vast amount of information of people’s naturalistic motivated behavior. We aim to show whether and how such “big motivation data” can add to our understanding of motivation.
Key words: Machine learning, learning analytics
We all take motivation for granted, but what is motivation? Despite our lab’s goal of understanding motivation, we believe that motivation is simply a mental category that people subjectively construct from our subjective experiences, and thus does not exist in our mental processes themselves. We aim to reformulate various motivational constructs in the field from this theoretical perspective.
Key words: Naïve theories, dynamic systems, philosophy of mind
In the field of motivation science, we deal with many different types of longitudinal data (e.g., panel data, intensive longitudinal data, time-series data from behavioral experiments and neuroimaging). Despite the popularity, we still do not have a good idea to analyze such data given a specific research question. We aim to understand how applied researchers can appropriately choose the right methods to analyze longitudinal data.
Key words: Statistical simulation, mixed-effects modeling, structural equation modeling
