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Computational Model Deciphers Human Emotions

Image credits: news.mit.edu

When individuals engage in social interactions with others, they encounter a range of emotions. Additionally, they make conscious efforts to either evade or predict these emotional responses based on the words spoken or actions taken. Referred to as the theory of mind, this ability empowers people to deduce the thoughts, wishes, objectives and feelings of those around them.

A computational model which enables forecasting of a range of emotions in individuals was developed by MIT neuroscientists, including joy, gratitude, confusion, regret, and embarrassment. This model closely mimics the social intelligence exhibited by human observers.

It was specifically designed to anticipate the emotional responses of individuals involved in a scenario based on the prisoner’s dilemma. It is a classic game theory scenario in which two people must decide whether to help and cooperate with their partner or betray them.

The construction of the model involved integrating various factors that are believed to impact an individual’s emotional responses. These factors encompassed the person’s desires, expectations in each situation, and whether their actions were being observed. By considering these elements, the researchers aimed to create a comprehensive framework that could capture the complexities of human emotional reactions.

By incorporating these factors, the computational model developed by the researchers aimed to approximate how individuals might express emotions in different contexts. This computational modelling advancement brings humanity closer to unravelling the mysteries of human emotions and enhances the understanding of how individuals perceive and respond to various situations.

Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research, and the study’s Senior Author stated that although comprehensive research has focused on training computer models to infer an individual’s emotional state through facial expressions, it is not the most crucial element of human emotional intelligence. The most critical factor is the capability to anticipate and predict someone’s emotional reaction to events before they occur. This ability holds greater significance in human emotional intelligence.

To simulate the prediction-making process of human observers, the researchers utilised scenarios taken from a British game show named “Golden Balls.” Depending on the game’s outcome, contestants may experience various emotional states, such as joy and relief when both contestants choose to share the winnings, surprise and anger if one contestant steals the pot, or a mix of guilt and excitement when successfully stealing the winnings.

The researchers devised three distinct modules to develop a computational model capable of predicting these emotions. The first module was trained to infer a person’s preferences and beliefs by analysing their actions, employing a technique known as inverse planning.

The second module assesses the game’s outcome with each player’s desired and anticipated outcomes. Subsequently, the third module utilises this information along with the contestants’ expectations to forecast the emotions they might be experiencing.

After implementing and activating the three modules, the researchers employed them on a new dataset obtained from the game show to evaluate the accuracy of the models’ emotion predictions compared to those made by human observers. The results demonstrated a significant improvement in the model’s performance compared to any previous model designed for emotion prediction.

In the future, the researchers are ready to enhance the model’s capabilities by further extending its predictive performance to various scenarios.

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