NCS-FO: The role of noise in mental exploration for learning

是什么让人们的行为如此不同？想想我们如何做决定。有些人反应迅速、果断，但过于僵化，无法有效地适应新的机会或威胁。相比之下，其他人可能更谨慎，更不自信，使他们的决定更难以预测，但更能适应不断变化的环境。在美国国家科学基金会的资助下，宾夕法尼亚大学的约书亚·戈尔德博士和约瑟夫·凯布尔博士正在研究一种新的理论，即在真实的世界中，这两个极端之间存在着根本的权衡。该理论包括一个新的提议，即以前被研究人员视为人类行为随机变异的东西可能反映了与去甲肾上腺素有关的不确定性，适应性决策，去甲肾上腺素是一种与学习和唤醒有关的神经化学物质。这个特征能解释人类性格和行为的其他方面吗？大脑中的去甲肾上腺素水平可以被操纵来影响复杂的学习和决策行为吗？在回答这些问题时，这项工作将建立基础性的基本知识，从长远来看，这将有助于指导新工具的开发，以诊断和应对与异常学习和决策相关的疾病，包括注意力缺陷多动障碍（ADHD），焦虑，抑郁和精神分裂症。这种关于学习中个体差异的知识也将告知如何最好地定制教育和学习实践，以及如何设计从经验中自适应学习的计算机程序。这项工作的其他好处是有助于认知和神经系统的研究和教育的资源，包括可用的数据集，计算机代码和机器学习算法;通过高中和本科生的夏季研究经验，增加代表性不足的群体在这种综合研究中的参与;通过公开讲座，大脑意识周活动，以及对一个网站的贡献，该网站用外行的话解释了大脑研究。这项工作是基于一个关于大脑机制的新假设，独特的学习和决策过程。具体来说，在我们不可预测的世界中，决策者面临着一种内在的权衡：当世界稳定但无法适应变化时，更高的确定性会导致更精确和准确的选择，而更少的确定性会导致更大的适应性，但也会导致更多变和不精确的决策。 研究人员提出，这种权衡是由唤醒和皮质系统之间的相互作用来调节的。为了验证这一假设，他们使用了一套跨学科和综合的方法，主要目标有三个：1）开发一个理论框架，描述在不可预测的环境中，在分层的概率推理过程中，输出稳定性和学习之间的内在权衡; 2）识别行为，生理，以及个体在不可预测的环境中做出选择时如何驾驭这些权衡的神经相关性; 3）确定脑干蓝斑核（唤醒系统的关键组成部分）对适应性推理变异性的因果影响。这项工作在理论和实验之间建立了有意义的联系，跨越了多个空间和时间尺度以及抽象层次，以确定学习中个体差异的计算和生理基础。

What makes people behave so differently from one another? Consider how we make decisions. Some people are quick and decisive but overly rigid, unable to adapt effectively to new opportunities or threats. In contrast, others may be more deliberative and less confident, making their decisions less predictable but more adaptable to changing circumstances. With funding from the National Science Foundation, Drs. Joshua Gold and Joseph Kable of the University of Pennsylvania are investigating a new theory that in the real world, there is a fundamental tradeoff between these two extremes. The theory includes a novel proposal that what has previously been dismissed by researchers as random variability in human behavior might instead reflect uncertain, adaptable decision-making linked with norepinephrine, a neurochemical implicated in learning and arousal. Does this characteristic explain other aspects of human personality and behavior? Can norepinephrine levels in the brain be manipulated to affect complex learning and decision-making behaviors? In answering these questions, this work will establish foundational, basic knowledge that, in the long term, will help to guide the development of new tools to diagnose and counteract conditions associated with abnormal learning and decision-making, including attention deficit hyperactivity disorder (ADHD), anxiety, depression, and schizophrenia. This knowledge about individual differences in learning will also inform how to best tailor educational and learning practices, as well as how to design computer programs that learn adaptively from experience. Other benefits of this work are resources that will assist research and education in cognitive and neural systems, including publically available datasets, computer code and machine learning algorithms; increased participation of underrepresented groups in this kind of integrative research, via summer research experiences for high school and undergraduate students; and an increased public awareness of neuroscience via public lectures, Brain Awareness Week activities, and contributions to a website that explains brain research in laymen's terms.The work is based on a novel hypothesis about brain mechanisms that are responsible for certain idiosyncratic learning and decision processes. Specifically, in our unpredictable world, decision-makers face an inherent trade-off: higher certainty leads to more precise and accurate choices when the world is stable but an inability to adjust to change, whereas less certainty can lead to greater adaptability but also more variable and imprecise decisions. The investigators propose that this trade-off is regulated by interactions between arousal and cortical systems. To test this hypothesis, they use an interdisciplinary and integrative set of approaches with three primary objectives: 1) develop a theoretical framework describing inherent trade-offs between output stability and learning in hierarchical, probabilistic inference processes in unpredictable environments; 2) identify behavioral, physiological, and neural correlates of variability in how individuals navigate these trade-offs while making choices in unpredictable environments; and 3) identify causal influences of the brainstem nucleus locus coeruleus, a key component of the arousal system, on the variability in adaptive inference. The work forges meaningful connections across theory and experiment, spanning multiple spatial and temporal scales and levels of abstraction, to identify computational and physiological underpinnings of individual differences in learning.