人工大脑的时长感知机制提出了注意力牵制的新模式

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ali Tehrani-Saleh;J. Devin McAuley;Christoph Adami
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引用次数: 0

摘要

尽管认知理论已经提出了几种候选框架来解释注意力诱导,但注意力的时间分配的神经基础尚不清楚。在这里,我们通过使用一组 50 个人工大脑所获得的经验证据,提出了一个新的注意力诱导模型。这些人工大脑是在硅学基础上进化而来的,可以完成类似于人类受试者在听觉怪人范式中进行持续时间判断的任务。我们发现,人工大脑显示出与人类听者极为相似的心理测量特征,并在遇到节奏失常的怪音时表现出相似的感知失真模式。对时长失真背后机制的详细分析表明,注意力在音调结束时达到顶峰,这与之前的注意力夹带模型不一致。相反,新的注意力诱导模型强调的是,大脑会增加对刺激信息量大的那些方面的注意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanism of Duration Perception in Artificial Brains Suggests New Model of Attentional Entrainment
While cognitive theory has advanced several candidate frameworks to explain attentional entrainment, the neural basis for the temporal allocation of attention is unknown. Here we present a new model of attentional entrainment guided by empirical evidence obtained using a cohort of 50 artificial brains. These brains were evolved in silico to perform a duration judgment task similar to one where human subjects perform duration judgments in auditory oddball paradigms. We found that the artificial brains display psychometric characteristics remarkably similar to those of human listeners and exhibit similar patterns of distortions of perception when presented with out-of-rhythm oddballs. A detailed analysis of mechanisms behind the duration distortion suggests that attention peaks at the end of the tone, which is inconsistent with previous attentional entrainment models. Instead, the new model of entrainment emphasizes increased attention to those aspects of the stimulus that the brain expects to be highly informative.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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