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Development and validation of a computational model of higher-order statistical learning on graphs in humans

人类图高阶统计学习计算模型的开发和验证

基本信息

批准号：
10059133
负责人：
Danielle Smith Bassett
金额：
$ 43.09万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10059133
关键词：
Address Affect Attention Auditory Back Behavior Behavioral Bipolar Disorder Birds Brain Clinical Cognitive Computational Science Computer Analysis Computer Models Development Dimensions Disease Environment Event Fatigue Foundations Future Goals Grain Graph Human Impairment Intuition Knowledge Language Learning Linguistics Major Depressive Disorder Mammals Maps Mathematics Measurement Mental disorders Modeling Moods Motivation Mus Neurocognitive Neuropsychology Paper Patients Pattern Perception Persons Physics Population Process Property Psychiatry Psychology Psychometrics Rattus Research Personnel Schizophrenia Sensory Statistical Models Statistical Study Stimulus Stream Structure Symptoms Time Translating Translations Triplet Multiple Birth Validation Visual Wolves Work autism spectrum disorder behavior prediction demographics executive function expectation experience experimental study flexibility innovation learned behavior man mathematical model nonhuman primate novel patient population psychologic relating to nervous system reward processing sensory input sequence learning social statistical learning stem stress state theories trait

项目摘要

As humans navigate their environment, anticipation, planning, and perception all require an accurate map of the statistical regularities governing their visual, linguistic, auditory, and social experiences. In each context, hu- man experience consists of a sequence of events. Each event succeeds another according to a set of underlying rules codifying possible event-to-event transitions, and the likelihood of each. To make predictions about the fu- ture and respond to the environment with flexible behavior, humans must infer this network of transitions, forming a cognitive map of causes and effects. Such maps and inferences are made possible by statistical learning. The study of statistical learning represents a major opportunity for computational psychiatry for three reasons. First, statistical learning shows differential accuracy across psychiatric conditions, task domains, and temporal scales of experience. Second, statistical learning has marked potential for back-translation; multiple features of statistical learning behavior and its neural underpinnings are conserved in non-human primates, and simpler forms of sequence learning exist in other mammals (rats and mice) as well as birds. Third, – as we describe in depth in our proposal – statistical learning can be formally modeled mathematically. It is now timely to develop a flexible computational model of statistical learning. To serve the goals of com- putational psychiatry, the functional form of such a model should reflect general principles of statistical learning and the parameters should be sensitive to variability in behavior across the many specific disorders where deficits appear. In preliminary experimental, computational, and theoretical work, we have uncovered a novel behavioral signature of statistical learning; we have also translated that behavior into a formal model – inspired by principles of statistical physics – with mathematically well-defined parameters, thereby deriving a theory that is grounded in our previous experimental findings. Finally, we have experimentally validated the model by making accurate predictions of behavior in a novel experiment. Here we assemble a complementary set of co-investigators who have co-authored 31 papers in pairs or triplets, with expertise in mathematical modeling and statistical physics (Bassett), statistical models of behavior (Moore), intensive longitudinal experiments (Lydon-Staley), statistical learning (Thompson-Schill), and sensory process- ing in psychiatry (Wolf). Together, we offer a well-integrated theoretical and experimental plan to hone our math- ematical model of an aspect of human behavior that has not been extensively analyzed computationally, and in which the underlying dimensional process is affected in psychiatric disorders. We distill our aims into reliability, relevance, and generalizability of our model. Our approach is three-pronged, with innovations in experiment, computation, and theory building on our team’s diverse expertise. Each prong will address all three aims, thereby integrating our efforts to build a computational model of statistical learning behavior supporting future advances in computational psychiatry. Our proposed efforts provide the foundation for an R01 extending to patients.

当人类在环境中航行时，预期、计划和感知都需要一张准确的地图控制他们的视觉、语言、听觉和社会体验的统计规律。在每种情况下，hu- 人的经验由一系列事件组成。每个事件根据一组底层事件接替另一个事件编码可能的事件到事件转换以及每个事件的可能性的规则。对未来做出预测并以灵活的行为对环境做出反应，人类必须推断出这个转变网络，形成因果认知图。统计学习使此类地图和推论成为可能。出于三个原因，统计学习的研究为计算精神病学提供了一个重大机会。首先，统计学习显示出不同的精神疾病、任务领域和时间的准确性差异。经验尺度。其次，统计学习具有显着的反向翻译潜力；多种功能统计学习行为及其神经基础在非人类灵长类动物中是保守的，并且更简单其他哺乳动物（大鼠和小鼠）以及鸟类也存在序列学习的形式。第三，正如我们所描述的我们建议的深度——统计学习可以通过数学形式进行正式建模。现在是时候开发一种灵活的统计学习计算模型了。为了服务于公司的目标假设精神病学，这种模型的功能形式应该反映统计学习的一般原则并且参数应该对许多特定疾病的行为变化敏感，其中缺陷出现。在初步的实验、计算和理论工作中，我们发现了一种新颖的行为统计学习的签名；我们还将这种行为转化为正式模型——受到原则的启发统计物理学——具有数学上明确定义的参数，从而推导出一个有根据的理论在我们之前的实验结果中。最后，我们通过准确的实验验证了该模型新颖实验中的行为预测。在这里，我们聚集了一组互补的共同研究人员，他们成对或三人共同撰写了 31 篇论文，拥有数学建模和统计物理学（巴塞特）、行为统计模型（摩尔）方面的专业知识，强化纵向实验（Lydon-Staley）、统计学习（Thompson-Schill）和感觉过程- 精神病学（沃尔夫）。我们共同提供了一个综合的理论和实验计划来磨练我们的数学- 人类行为的一个方面的数学模型尚未经过广泛的计算分析，并且在精神疾病中潜在的维度过程受到影响。我们将目标提炼为可靠性，我们的模型的相关性和普遍性。我们的方法是三管齐下，通过实验创新，计算和理论建立在我们团队多样化的专业知识之上。每个分支都将解决所有三个目标，从而整合我们的努力来构建支持未来进步的统计学习行为的计算模型在计算精神病学中。我们提出的努力为 R01 扩展到患者奠定了基础。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Optimizing the human learnability of abstract network representations.

DOI：
10.1073/pnas.2121338119
发表时间：
2022-08-30
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
通讯作者：

Functional brain network architecture supporting the learning of social networks in humans.

DOI：
10.1016/j.neuroimage.2019.116498
发表时间：
2020-04-15
期刊：
NeuroImage
影响因子：
5.7
作者：
Tompson SH;Kahn AE;Falk EB;Vettel JM;Bassett DS
通讯作者：
Bassett DS

Quantifying the compressibility of complex networks.

DOI：
10.1073/pnas.2023473118
发表时间：
2021-08-10
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Lynn CW;Bassett DS
通讯作者：
Bassett DS