Professor's Opinion - Cheating in chess

I have a broad interest in human behaviour and in how the brain orchestrates this behaviour. My current research topics range from the decoding of psychological processes from the brain, to investigating brain responses with naturalistic stimuli (movies), to the neural underpinnings of cheating and deception. I will briefly outline these research lines below.

Cheating, unfairness and deception

Dishonest behaviour, such as tax evasion, music piracy or fraud, is highly prevalent in our society and inflicts huge economic costs. Every day, we are faced with the conflict between the temptation to cheat and deceive for financial gains and maintaining a positive image of ourselves as being a ‘good person’. In this line of research, we investigate the psychological and neural underpinnings of decisions to either cheat and deceive, or to remain fair and honest. We find that particularly individual differences in the engagement of cognitive control and theory of mind drive decisions to be fair and honest (or not). For example, in one study we found that cognitive control may override an individual’s moral default, allowing honest people to cheat, whereas it enables cheaters to be honest. These insights contribute to a deeper understanding of individual differences in honesty and may aid in developing more targeted interventions aiming at reducing dishonesty.

Brains at the movies

In the past, research in neuroscience has used decontextualized stimuli and highly artificial experimental designs to study the neural substrate of cognitive processes. Although this approach has been very successful, as it allows for tightly controlled experiments and straightforward interpretation of results, it has left open the question of how the brain responds to events in more naturalistic settings. In this line of research, we address this issue by investigating how brain processes unfold during movie watching. We find that we can track emotions, engagement and preference that follow the narrative of the presented videos. In addition, we observe that we can not only predict how well individual participants will like the movie they are watching, but also how well others will like this movie. That is, we can predict, from brain activity measured during movie-watching in a small set of participants, to what extent a different set of participants will like this movie, and even estimate how well the movie will do at the box office.

Decoding psychological processes from the brain

The human psyche pretty much remains a black box: we can observe or even manipulate the input a person’s psychological system receives, but not the feelings or cognitive processes that are evoked by this input. Likewise, we can observe the decisions made by the system, but not the feelings or cognitive processes that drove these decisions. In this line of research, we decode these latent processes or states from the brain, using machine learning methods applied to distributed pattern of brain activity. For example, in two studies (one using EEG, and one using fMRI), we presented participants with video content while measuring activity from their brains. Using machine learning, we trained classifiers to accurately decode the emotional experience evoked by these videos in our participants. As another example, in every-day life we observe large differences in honesty and fairness across individuals. In a set of two studies (using fMRI), we decode idiosyncrasies in the underlying motivations for honesty and fairness. We find that particularly individual differences in the engagement of cognitive control and theory of mind drive differences in prosocial behaviour.