Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, U.S.A.
Department of Psychology, Michigan State University, East Lansing, MI 48824, U.S.A.
Neural Comput. 2024 Sep 17;36(10):2170-2200. doi: 10.1162/neco_a_01699.
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.
虽然认知理论已经提出了几个候选框架来解释注意的同步,但注意的时间分配的神经基础尚不清楚。在这里,我们提出了一个新的注意同步模型,该模型是由使用 50 个人工大脑组成的队列获得的经验证据指导的。这些大脑在计算机中进化,以执行类似于人类在听觉异类范式中进行持续时间判断的任务。我们发现,人工大脑表现出与人类听众非常相似的心理物理特征,并在呈现不合拍异类时表现出相似的感知扭曲模式。对时长扭曲背后的机制进行详细分析表明,注意在音调结束时达到峰值,这与之前的注意同步模型不一致。相反,新的同步模型强调对大脑期望高度信息丰富的刺激方面的注意力增加。