Research

Social interactions are central to human life. Learning from one another, exchanging and creating ideas that lead to growth (Romer, 1990) and to the development of civilizations, global trade and travel would not be possible without them, but then again neither would wars nor the systematic exploitation of our environment. The complex demands of our social interactions may have driven human brain evolution (Dunbar, 1998). Conversely, many disorders of brain function impact social interactions, increasing the burden of these disorders by hampering social support. But how does the human brain support social interactions?

We study basic processes that seem important for social interactions: detecting social agents, processing the signals they emit, understanding their actions & estimating their intentions, deciding to interact with them or not, and how it feels to interact with them. These will be described here in ascending hierarchical order, with links to related publications.

Animacy

At the earliest stage, we are interested in how the brain detects potential interaction partners and distinguishes them from non-living things. This involves understanding how the percept of animacy, i.e. the feeling that a thing in our environment is alive, comes about. We first used motion stimuli mimicking simplified social interactions (Schultz et al., 2004; 2005), and have further simplified these stimuli to study animacy percepts evoked by a single moving item (Schultz & Bülthoff 2013; 2019).

Dynamic faces

As most social agents we interact with are other humans, and because major clues about them are displayed in their face and the way the face moves (Dobs et al., 2016), we like using dynamic face stimuli in our experiments (Dobs et al., 2018). Indeed, dynamic faces strongly activate brain regions implicated in face processing (Schultz & Pilz, 2009), meaningful face motion engages a particular part of the posterior superior temporal sulcus (Schultz et al., 2013), humans are highly sensitive to variations in face motion (Dobs et al., 2014), and the different kinds of information they carry are processed in parallel but neverthless influenced by the current task (Dobs et al., 2018).

Actions and social signals

At a higher processing level, we are interested in neural mechanisms allowing to identify and understand actions of the potential interaction partner; this involves studying the representations of their actions (de la Rosa et al., 2016), emotions (Kim et al., 2015), and of their goals and intentions (Schultz et al., 2004).

Social decisions

Next, based on our interpretation of the agents' social signals, we need to decide whether to interact with them or not. To quantify this drive, we attempt to measure the subjective value of engaging with the social agent and the neural correlates involved in making that decision (Schultz et al., 2019). As this work relates to value-based decision-making, we reconnect with previous work (O'Doherty et al., 2004) and venture into neuroeconomics.

Once engaged with the other agent, we are interested in understanding how agency affects the experience of decisions made in a social context, how our decisions and valuations are influenced by other people and why these other people are not all treated equally (these topics are work in progress).

Interindividual differences and pharmacology

Finally, we readily acknowledge that several of these processes function differently across people. We therefore study inter-individual differences in these processes across healthy participants (Schultz et al., 2019; Maier et al., 2019) or compare "average" participants to those who perceive things differently (David et al., 2014; Esins et al., 2016; Muthesius et al., 2022). We also collaborate with researchers interested in influencing social behaviour in positive or negative ways through the use of drugs such as oxytocin (Spengler et al., 2017) or ketamine (Becker et al., 2017; Lehmann et al., 2022).

Methods

We use mostly visual stimuli in experimental designs from experimental psychology, methods from behavioural economics to quantify subjective value, and functional magnetic resonance imaging (fMRI) to measure the BOLD signal, an indirect (metabolic) measure of neural activity.