Moreover, to measure the dynamics of emotional state that are relevant to understanding adaptive behavior, well-being researchers have developed experience-sampling techniques that probe participants as to their current subjective state while they go about their daily lives 19, 20. Such research is inherently correlational, but has identified various factors that impact on mood, including outcomes of sporting events and levels of sunshine 17, 18. Monetary outcomes, by contrast, can be precisely controlled and have also been shown to affect mood 14, 15.Īnother line of research, originating primarily in an economics literature, considers real-world circumstances that covary with subjective well-being. While these stimuli are easy to present in laboratory experiments, they are not readily quantifiable and are typically applied categorically, without variation in either quantity or intensity. Other stimuli that reliably affect mood include music, self-referential statements, observed social interactions, and facial expressions. Presentation of a film or story with emotional content is a common and effective mood-induction technique. A vast psychological literature demonstrates that mood can be manipulated via a range of techniques. To understand the function of mood, we first need to consider its causes.

We propose that mood represents the overall momentum of reward in the environment, and that this representation serves to facilitate efficient learning by accounting for statistical dependencies in the availability of rewards that are prevalent in nature.Ĭauses: Mood Depends on the Cumulative Impact of Unexpected Outcomes We first review these two largely separate strands of research and then integrate them within a coherent theoretical framework. In particular, two burgeoning lines of research have sought to characterize precisely, on the one hand, the causes of moods, and on the other the consequences of mood states for learning and decision making.

Recently, scientists have begun to utilize the same computational framework to study the dynamics of human emotional states in health and in mental disorders, focusing on how these states affect and are affected by learning and decision-making processes 10, 11, 12. Based on these findings, we propose that moods benefit ‘moody’ agents by mitigating inefficiencies that can arise in the process of learning about the natural environment.Īdvances in computational modeling have greatly facilitated an understanding of how humans learn from outcomes to make better decisions 7, 8, 9. We elucidate this role by considering recent findings regarding the dynamics of mood, as well as its interaction with the processes of learning and decision making. We argue that moods serve an important role in adaptive behavior, even in the modern world. Our language also reflects this view, with expressions such as ‘moody’ and ‘being in a mood’ carrying negative connotations. Indeed, we often attribute irrational behavior to the emotional state of a person 3, 4, 5, 6.

The enormous and disruptive impact of mood disorders in society 1, 2 might suggest that mood (see Glossary) is an evolutionary relic that may have been advantageous for early humans but impedes adaptive behavior in the modern world.

Moods of a story list