Science

What is emotion science?

By Jeffrey Brooks, PhD on Sep 23, 2024

As AI becomes more integrated into daily life, emotional intelligence is essential for improving user experiences and well-being, enabling more meaningful interactions.
Emotion science, or affective science, is crucial for understanding how we experience and express emotions, informing AI systems in providing personalized, emotionally aware responses.
Modern emotion science uses advanced data collection and machine learning to uncover the complexity of human emotions, identifying up to 20 distinct emotional states.
Emotion science's insights are key to developing AI that is not just intelligent but empathic, capable of supporting human happiness and well-being through emotionally attuned interactions.

Introduction: The Intersection of Emotion Science and AI

How can artificial intelligence achieve the level of emotional intelligence required to understand what makes us happy? As AI becomes increasingly integrated into our daily lives, the need for AI to understand emotional behaviors and what they signal about our intentions and preferences has never been more critical. Every interaction we have with an AI system—whether it’s trained to provide emotional support or simply to answer factual questions—is a request for the AI to predict the response that will be most satisfying and conducive to our well-being. In order to achieve this, AI systems rely on the scientific foundation of emotion science—a rapidly advancing field that reveals the complexity of our emotional lives and equips AI with the knowledge to understand and learn from the way we express our feelings.

What is Emotion Science?

How can AI provide personalized, engaging responses? Fundamentally, this relies on a robust foundation of scientific knowledge about our emotions and how we express them.

This is where emotion science, also known as affective science, comes into play. It is a multidisciplinary field sitting at the interface of neuroscience and psychology which explores how we experience and express emotions. How do we react emotionally to the world around us? How do various situations, people, and experiences make us feel, and how do we communicate these internal states to others? How are emotions elicited, experienced, and recognized?

The History of Emotion Science: From Darwin to Data

Emotion science tackles these questions with a mix of historical and contemporary approaches. The formal field of emotion science is relatively new, with roots that can be traced back to foundational research on facial expressions conducted in the mid-20th century. However, some of its core ideas go back even further, to philosophers and scientists like William James and Charles Darwin. Nearly 150 years ago, they were already considering many of the same questions that modern emotion scientists continue to explore today.

Measuring and Interpreting Emotions in a Data-Driven Way

Central to emotion science is the quest to scientifically define what emotions are and how we can measure them. There’s general agreement that emotions are complex phenomena, involving multiple components:

our evaluation of significant events or threats in our environment
physiological changes in our bodies (such as a quickening heartbeat or a rise in blood pressure)
the subjective experience of feelings
noticeable changes in our behavior or expression, like frowning, smiling, or raising our eyebrows.

Given how multifaceted emotions are, most scientific approaches to emotion have tried to simplify them in ways that make them more amenable to scientific investigation. One approach is to break emotions down into measurable pieces, such as physiological responses (like heart rate, blood flow, and perspiration), brain activity, or verbal self-reports about how we feel.

Many scientific theories also aim to simplify our understanding of emotions by categorizing them into basic groups or dimensions. One popular approach is the "basic six" emotions: anger, disgust, fear, happiness, sadness, and surprise. These emotions are considered universal, with everyone able to recognize and express them. Another approach uses dimensions such as valence (whether an emotion feels positive or negative) and arousal (how intense the emotion is) to map out emotional experiences on a continuous scale.

However, in the past decade, advances in our ability to collect and analyze large amounts of data, as well as the introduction of machine learning approaches, have enabled scientists to develop a much more nuanced picture of emotional experience. By examining facial expressions, tone of voice, reactions to music and art, and even responses to videos, researchers have found that emotions are not confined to just six categories or a couple of dimensions.

In fact, studies now suggest that human emotions are far more varied and complex than previously thought. Research into large-scale emotional data has revealed that we may have upwards of 20 distinct emotional states that people regularly experience and describe. These include emotions like “amusement,” “calmness,” “pride,” “terror,” and “triumph,” among others. As a result, a new theoretical framework called semantic space theory has emerged, which maps emotions along a wide range of interacting dimensions. This theory suggests that emotions exist on a spectrum and are influenced by multiple factors simultaneously, providing a richer understanding of how emotions are organized.

Why Emotion Science Matters for AI

Why is emotion science so critical to the future of AI and human-machine interaction? The answer lies in the potential for AI to understand and learn from human emotional behaviors, leading to more meaningful, empathic interactions. If we want AI to truly improve human well-being—whether by providing emotional support or carrying out everyday requests in accordance with your unique preferences—it must be capable of recognizing, interpreting, and responding to our emotional expressions.

Whether it's through interpreting nuanced tones of voice, facial expressions, or even recognizing text-based proxies of emotional experience, these technologies can work hand in hand with emotion science to create AI that fosters better emotional connections. The goal is for AI to help us lead happier, more fulfilling lives.

Conclusion: Toward a Future of Empathic AI

Emotion science holds enormous promise, not just for understanding ourselves better, but also for shaping the future of AI. As AI continues to evolve, its ability to engage with us emotionally will transform how we interact with machines. By leveraging insights from emotion science, we are moving toward a future where AI is not just intelligent but empathic—capable of interpreting the subtleties of our emotional lives to form better responses even to mundane requests. This fusion of emotion science and AI will be key to building technologies that truly enhance our well-being and enrich our human experience.