NEURAL AND COMPUTATIONAL PRINCIPLES UNDERLYING SOCIAL VS NON-SOCIAL DECISION MAKING

Most strategic decisions occur under considerable uncertainty. For example, when investing in the stock market, a trader may use purely probabilistic models to estimate risk in the market's fluctuations. In contrast, when negotiating a deal in person, the trader's risk assessment may rely instead on how trustworthy the other party appears. In standard utility models, the rules governing such decisions are the same, regardless of the source of uncertainty (e.g. human vs on-line platform). However, recent advances in social neuroscience suggest that separate brain networks might distinctly process probabilistic and social information, possibly leading to different outcomes.
To date, there is no unified framework for integrating social and non-social sources of decision uncertainty as previous studies looked at these factors in isolation. This shortcoming is mainly due to the interdisciplinary nature of the endeavour, which requires major methodological developments in experimental design and brain analytics.
Here, we will combine two popular brain imaging techniques (EEG-fMRI), with novel experimental design and computational modelling to obtain information on when, where and how the brain processes social and non-social information during decision-making. We will investigate two different phases of the decision process: (1) the choice-phase, where decision alternatives are evaluated and compared to guide action and (2) the outcome-phase, where expected reward and risk signals are computed to update future expectations. We will model the integration of social and non-social forms of uncertainty at each stage and characterise the computational principles of the relevant neural systems. In doing so, we will place new, neurobiologically-derived, constraints on decision-theoretic models of information integration. The marriage of social and non-social forms of uncertainty into a comprehensive theory of decision-making promises to significantly improve our understanding of important real-life events, ranging from policy making and risk management to informing individual decisions on health behaviours and savings strategies.
The current project will look more closely into how people predict and learn from others' actions. And especially how we predict risk-taking in others and what are the responsible brain mechanisms. According to game theory players construct a model of others' behaviours and subsequently choose the best strategy that would maximise their rewards and minimise their losses. To decide on their best action each player either learns from the frequency of the other's actions or simulates the current state of the other player. Such predictions when violated produce the 'state prediction error', which occurs when there are unexpected changes in the environment. When predictions are made, and behaviour from the opponent is observed, participants update their current knowledge according to the difference between the predicted and the observed outcomes. This is termed 'simulated other's action prediction error' (sAPE). The simulated predictions are usually based on a player's preferences and values, with addition to knowledge about the other opponent (based on experiences and belief learning). Some outstanding questions are: what are the brain correlates of mental simulation during social decision making? Are the brain signals responsible for prediction error in decision making for oneself similar to predicting others' decisions? Are there specific brain areas and processes for simulating others' prediction error, or do they overlap with the areas for prediction error for oneself? The current PhD project will aim to answer some of these questions with using computational models and either fMRI, EEG or both simultaneously.