Emotions are deeply personal, shaping the multitude of patterns and preferences that make up who we are: our social interactions, our relationships, our responses to art and music, and the decisions we make about what to work on and focus on in life.

But for years the scientific study of emotion has been grappling with foundational questions: what is an emotion? How are emotions best represented for scientific study in the first place?

Scientific approaches to emotion have typically simplified emotions into a small number of categories (e.g. anger, disgust, fear, happiness, sadness, and surprise; or the “basic 6”) or dimensions (e.g. valence and arousal).

These ideas have shaped the study of emotion for decades, from the stimulus sets available to researchers, to the outputs provided by computational models. Even researchers critical of the “basic 6” approach tend to only use the basic 6 as conditions in their studies due to these practical limitations.

How can we move beyond this to capture the complex, nuanced appraisals that people actually make about emotion?

The foundation of our research at Hume is semantic space theory (SST), which is an inductive, data-driven approach to these questions. Using wide-ranging naturalistic data and open-ended, cutting edge statistical approaches, SST maps the full spectrum of human emotion.

A data-driven, computational approach to emotion

With recent technological advances in computing, data storage, online data collection, and more, the stage has been set for a new generation of emotion science. Collecting large amounts of naturalistic data and using statistical modeling to quantitatively describe large datasets has become more tractable due to advances in our ability to store and process this amount of data.

Inductive, data-driven approaches make fewer assumptions about how the data are distributed in advance and – as a result – can pick up on a variety of different contributors to emotion – both the actual structure we’re trying to measure as well as the confounds that have been notoriously hard to measure in the past. Combining these techniques with large-scale data collection offers the ability to train models that pick up on specific cues and behaviors of interest and reduce the contribution of bias to these models.

SST moves beyond low-dimensional theories of emotion, conceiving of emotion as a high-dimensional semantic space, which these techniques allow us to map in detail.

These approaches have led to three major insights that pave the way for a detailed computational understanding of emotion.

1. Emotion is high-dimensional

How many kinds of emotion are distinguished within a semantic space?

Instead of deciding in advance that only 6 categories define the space of emotion and only including stimuli and response options that conform to this assumption, the SST approach has been to conduct experiments with a vast array of expressive and evocative stimuli, collecting responses for a broad range of emotions, mental states, and nonverbal behaviors. Identifying how many dimensions capture the systematic variation in these responses requires data-driven statistical modeling such as multidimensional reliability analysis.

These approaches have been taken to understand the semantic spaces of emotions represented in self-reported experience, facial expressions, speech prosody, nonverbal vocalizations (vocal bursts), the feelings evoked by music, and the expressions depicted in ancient art. Many of these studies have taken place in multiple cultures, including our most recent work which we conducted in 5-6 countries.

These studies consistently find that upwards of 20 distinct dimensions characterize the emotions people use to describe these various domains. The specific numbers of emotions matter less than the general insight - emotion is at least four times more complex than traditionally assumed.