Mechanisms of Adaptive Choice History Biases in the Human Brain

大量的行为研究表明，人类的决策过程深受强烈偏见的影响。这些偏见的一个重要来源是先前选择的历史。例如，在判断模棱两可的证据时，人们经常会比预期的更频繁地重复同一个选择。最近在认知、系统和理论神经科学方面的研究，让我们得以一窥决策过程中大脑内部状态的动态变化对选择历史偏差的影响。具体来说，我的团队在DFG研究基金的第一个资助期内的工作导致了两个假设，我们的目标是在该基金的第二个资助期内进行测试：（i）选择历史偏见对内部大脑状态的影响是由后续决策中与选择相关的神经信号的缓慢且依赖于上下文的积累介导的;以及（ii）由此产生的神经偏见通过偏向选择性注意，塑造了对新的决策相关证据的解释。为此，我们将对选择历史偏差和选择性注意进行系统的操作，然后在完整的人脑中，在行为、算法和神经水平上量化内部大脑状态的变化。我们将对选择行为的历史模式进行详细分析，这些行为模式的替代算法模型的拟合，以及大规模大脑动力学的瞳孔测量和脑磁图（MEG）记录。我们将专注于在不确定性下的基本感知决策过程中的选择历史偏差，使我们能够利用表征良好的神经群体测量感官输入（“证据”）的编码，不断发展的决策，以及由此产生的行动计划。反过来，这将有助于识别后续决策之间的这些措施的偏见，介导的选择历史偏见在行为层面上。我们还计划开发一个综合的理论框架，选择历史偏见的基础上，选择历史偏见的新兴理解。该框架将从不同的研究途径的见解之间的桥梁：从计算水平的见解以及潜在的动态神经机制，在本地微电路和全脑网络的水平;和从感知和基于价值的决策研究的见解。总的来说，这个项目将为人类在不确定性下的判断和决策的生物学基础提供深入的见解。

Numerous behavioral studies have revealed that human decision-making is profoundly shaped by strong biases. One important source of those biases is the history of previous choices. For example, when judging ambiguous evidence, people often repeat the same choice more frequently than expected by chance. Recent work in cognitive, systems, and theoretical neuroscience provides a glimpse into the emergence of choice history biases from the dynamics of internal brain states during sequences of decisions. Specifically, my team’s work during the first funding period of this DFG research grant has led to two hypotheses, which we aim to test in the second funding period of this grant: (i) the effect of choice history biases on internal brain state is mediated by the slow, and context-dependent, accumulation of choice-related neural signals across subsequent decisions; and (ii) the resulting neural biases shape the interpretation of new decision-relevant evidence, by biasing selective attention. To this end, we will perform systematic manipulations of choice history biases as well as of selective attention, and then quantify the resulting changes in internal brain states at the behavioral, algorithmic, and neural levels, in the intact human brain. We will perform detailed analyses of history patterns in choice behavior, fits of alternative algorithmic models to these behavioral patterns, and pupillometric and magnetoencephalographic (MEG) recordings of large-scale brain dynamics. We will focus on choice history biases during elementary perceptual decisions under uncertainty, enabling us to exploit well-characterized neural population measures of the encoding of sensory input (“evidence”), the evolving decision, and the resulting plan to act. This, in turn, will facilitate the identification of biases in these measures between subsequent decisions, which mediate the choice history bias at the behavioral level. We also plan to develop an integrated theoretical framework for choice history bias, based on the emerging understanding of choice history bias. The framework will bridge between insights from distinct avenues of research: insights from the levels of computation as well as underlying dynamical neural mechanisms, at the levels of local microcircuits and brain-wide networks; and insights from studies of perceptual and value-based decisions. Taken together, this project will provide deep insights into the biological basis of human judgment and decision-making under uncertainty.