Unified probabilistic modelling of adaptive spatial-temporal structures in the human brain

人脑自适应时空结构的统一概率建模

• 批准号: BB/H012508/1
• 负责人: Peter Tino
• 金额: $ 78.2万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH012508%2F1
• 关键词: Unified; probabilistic; modelling; adaptive; spatial

Learning from experience and adapting our behaviour to new situations is a fundamental skill for our everyday interactions. But what are the brain plasticity mechanisms that mediate an individual's ability to make progress during training on complex tasks? What is it that differentiates `good' from `poor' learners in their ability to adapt? Recent advances in functional brain imaging technology provide us with the unique opportunity to study how the human brain changes with learning. However, the existing methods focus predominantly on modelling brain activity data within a single session rather than across training sessions. As such, these methods are not capable of capturing larger scale dependencies emerging in brain activity as training progresses. We will develop a novel methodology that allows holistic unified modelling of a series of brain imaging data measured during the course of learning. Using this methodology we will study brain changes that result from extensive training on complex visual tasks. Our work will offer scientists and practitioners advanced tools for using brain activity measurements to understand the brain learning mechanisms and how they improve our ability to make complex decisions. The proposed methodology may have predictive power for making inferences about 'prototypical' learning patterns that can be used to predict adaptive behaviour in individuals with different learning strategies and design training schemes tailored to the individuals' abilities and needs. Hence our findings have potential implications for the design of dedicated training programmes that take into account an individual's learning capacity. Such programmes may have applications in education or intervention and rehabilitation in normal and pathological development and ageing (e.g. stroke, neurodegenerative disorders).

从经验中学习并使我们的行为适应新的情况是我们日常互动的一项基本技能。但是，在复杂任务的训练中，调节个体取得进步能力的大脑可塑性机制是什么？“好的”和“差的”学习者在适应能力上有什么区别？最近脑功能成像技术的进步为我们提供了独特的机会来研究人类大脑如何随着学习而变化。然而，现有的方法主要集中在对单个会话内的大脑活动数据进行建模，而不是跨培训会话。因此，随着训练的进行，这些方法不能捕获大脑活动中出现的更大规模的依赖关系。我们将开发一种新的方法，允许对学习过程中测量的一系列脑成像数据进行整体统一建模。使用这种方法，我们将研究复杂视觉任务的广泛训练所导致的大脑变化。我们的工作将为科学家和从业者提供先进的工具，使用大脑活动测量来了解大脑学习机制，以及它们如何提高我们做出复杂决策的能力。建议的方法可能具有预测能力，可以推断出可以用来预测具有不同学习策略的个人的适应行为的“原型”学习模式，并根据个人的能力和需求设计培训方案。因此，我们的发现对设计考虑到个人学习能力的专门培训方案具有潜在的意义。这些方案可应用于正常和病理性发育和老龄化(如中风、神经退行性疾病)的教育或干预和康复。

项目成果

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

• DOI: 10.3389/fncom.2016.00117
• 发表时间: 2016
• 期刊: Frontiers in computational neuroscience
• 影响因子: 3.2
• 作者: Alahmadi HH;Shen Y;Fouad S;Luft CD;Bentham P;Kourtzi Z;Tino P
• 通讯作者: Tino P

Does money matter in inflation forecasting?

• DOI: 10.1016/j.physa.2010.06.015
• 发表时间: 2010-11-01
• 期刊: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
• 影响因子: 3.3
• 作者: Binner, J. M.;Tino, P.;Kendall, G.
• 通讯作者: Kendall, G.

Learning to predict: exposure to temporal sequences facilitates prediction of future events.

• DOI: 10.1016/j.visres.2013.10.017
• 发表时间: 2014-06
• 期刊: VISION RESEARCH
• 影响因子: 1.8
• 作者: Baker, Rosalind;Dexter, Matthew;Hardwicke, Tom E.;Goldstone, Aimee;Kourtzi, Zoe
• 通讯作者: Kourtzi, Zoe

Training transfers the limits on perception from parietal to ventral cortex.

• DOI: 10.1016/j.cub.2014.08.058
• 发表时间: 2014-10-20
• 期刊: Current biology : CB
• 作者: Chang DH;Mevorach C;Kourtzi Z;Welchman AE
• 通讯作者: Welchman AE