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跨模态动作选择的因果推理:决策框架中的计算研究

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

作者信息

Daemi Mehdi, Harris Laurence R, Crawford J Douglas

机构信息

Department of Biology and Neuroscience Graduate Diploma, York UniversityToronto, ON, Canada; Centre for Vision Research, York UniversityToronto, ON, Canada; Canadian Action and Perception NetworkToronto, ON, Canada; Department of Psychology, York UniversityToronto, ON, Canada.

Department of Biology and Neuroscience Graduate Diploma, York UniversityToronto, ON, Canada; Centre for Vision Research, York UniversityToronto, ON, Canada; Department of Psychology, York UniversityToronto, ON, Canada; School of Kinesiology and Health Sciences, York UniversityToronto, ON, Canada.

出版信息

Front Comput Neurosci. 2016 Jun 23;10:62. doi: 10.3389/fncom.2016.00062. eCollection 2016.

Abstract

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such as target selection among higher numbers of stimuli and selections among other modality combinations.

摘要

动物试图通过将来自多种模态的感官信息分类为对单个或多个外部对象或内部概念的感知,来理解这些信息。例如,大脑基于视网膜和耳蜗的刺激,在视觉和听觉皮层中构建视觉和听觉刺激位置的感官空间表征。目前,尚不清楚大脑如何比较这些感官表征的时间和空间特征,以确定它们是否源自空间中的同一来源或不同来源。在此,我们提出了一个关于大脑可能如何解决此类任务的计算模型。我们将视觉和听觉信息简化为时变的有限维信号。我们引入受控的泄漏积分器作为工作记忆,在任务执行的有限时间过程中保留感官信息。我们在基于证据的决策框架内提出我们的模型,其中替代计划单元是空间显著性图。我们建议直接从单模态信号计算的时空相似性度量作为推断共同或不同原因的标准。我们提供的模拟结果表明:(1)在要求参与者报告具有任意空间和时间差异的跨模态刺激的共同或不同原因的任务中,根据行为和实验结果验证了我们的模型。(2)预测了在刺激具有不同空间、时间和可靠性特征组合的新实验中的行为。(3)说明了所提出的内部系统的动态变化。这些结果证实了我们的时空相似性度量作为因果推理的可行标准,以及我们的决策框架作为目标选择的可行机制,大脑在跨模态情况下可能会使用这些机制。此外,我们建议类似的方法可以扩展到工作记忆是限制因素的其他认知问题,例如在更多数量的刺激中进行目标选择以及在其他模态组合中进行选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/827a/4917558/925cf98d5262/fncom-10-00062-g0001.jpg

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