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

大多数战略决策都是在相当不确定的情况下做出的。例如，在投资股票市场时，交易者可能会使用纯粹的概率模型来估计市场波动中的风险。相比之下，当交易者亲自谈判交易时，交易者的风险评估可能取决于另一方看起来有多可信。在标准实用新型中，无论不确定性的来源是什么(例如人与在线平台)，管理此类决定的规则都是相同的。然而，社会神经科学的最新进展表明，独立的大脑网络可能会明显地处理概率信息和社会信息，可能会导致不同的结果。到目前为止，还没有统一的框架来整合决策不确定性的社会和非社会来源，因为以前的研究孤立地考察了这些因素。这一缺陷主要是由于这项工作的跨学科性质，这需要在实验设计和脑分析方面取得重大的方法学发展。在这里，我们将结合两种流行的大脑成像技术(EEG-fMRI)，与新颖的实验设计和计算模型相结合，以获得关于大脑在决策过程中何时、何地以及如何处理社会和非社会信息的信息。我们将研究决策过程的两个不同阶段：(1)选择阶段，评估和比较决策备选方案，以指导行动；(2)结果阶段，计算预期回报和风险信号，以更新未来预期。我们将在每个阶段模拟社会和非社会形式的不确定性的整合，并描述相关神经系统的计算原理。在这样做的过程中，我们将对信息集成的决策理论模型施加新的神经生物学派生的约束。将社会和非社会形式的不确定性结合到一个全面的决策理论中，有望显著提高我们对现实生活中重要事件的理解，从政策制定和风险管理到为个人关于健康行为和储蓄战略的决策提供信息。目前的项目将更深入地研究人们如何预测他人的行为并从中吸取教训。特别是我们如何预测他人的冒险行为，以及负责的大脑机制是什么。根据博弈论，玩家构建他人行为的模型，然后选择最佳策略，使他们的回报最大化，损失最小化。为了决定他们最好的动作，每个玩家要么学习对方动作的频率，要么模仿另一个玩家的当前状态。当这种预测被违反时，就会产生“状态预测误差”，当环境发生意外变化时就会发生这种情况。当做出预测并观察到对手的行为时，参与者会根据预测结果和观察结果之间的差异来更新他们目前的知识。这被称为“模拟他人动作预测误差”(SAPE)。模拟预测通常基于玩家的偏好和价值观，以及对其他对手的了解(基于经验和信念学习)。一些突出的问题是：在社会决策过程中，心理模拟的大脑关联是什么？在自己的决策中，大脑信号对预测错误的责任是否类似于预测他人的决定？是否有特定的大脑区域和过程来模拟他人的预测误差，或者它们是否与自己的预测误差区域重叠？目前的博士项目将致力于通过使用计算模型以及功能磁共振成像、脑电或同时使用两者来回答其中一些问题。

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.