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The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of attention in head-fixed mice

机译:虚拟环境搜寻任务可快速固定头部固定小鼠的训练和注意力的单次试验指标

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摘要

Attention – the flexible allocation of processing resources based on behavioural demands – is essential to survival. Mouse research offers unique tools to dissect the underlying pathways, but is hampered by the difficulty of accurately measuring attention in mice. Current attention tasks for mice face several limitations: Binary (hit/miss), temporally imprecise metrics, behavioural confounds and overtraining. Thus, despite the increasing scope of neuronal population measurements, insights are limited without equally precise behavioural measures. Here we present a virtual-environment task for head-fixed mice based on ‘foraging-like’ navigation. The task requires animals to discriminate gratings at orientation differences from 90° to 5°, and can be learned in only 3–5 sessions (<550 trials). It yields single-trial, non-binary metrics of response speed and accuracy, which generate secondary metrics of choice certainty, visual acuity, and most importantly, of sustained and cued attention – two attentional components studied extensively in humans. This allows us to examine single-trial dynamics of attention in mice, independently of confounds like rule learning. With this approach, we show that C57/BL6 mice have better visual acuity than previously measured, that they rhythmically alternate between states of high and low alertness, and that they can be prompted to adopt different performance strategies using minute changes in reward contingencies.
机译:注意-根据行为需求灵活分配处理资源-对于生存至关重要。小鼠研究提供了剖析潜在途径的独特工具,但由于难以准确测量小鼠的注意力而受到阻碍。当前对小鼠的注意力任务面临几个限制:二进制(命中/缺失),时间上不精确的指标,行为混杂和过度训练。因此,尽管神经元群体测量的范围不断扩大,但是如果没有同样精确的行为方法,见识也会受到限制。在这里,我们基于“觅食式”导航为头戴式鼠标提供了一个虚拟环境任务。这项任务要求动物辨别出从90°到5°方向不同的光栅,并且只能在3-5次练习中学习(<550次试验)。它产生响应速度和准确性的单项,非二进制度量,生成选择确定性,视敏度以及最重要的是持续和暗示注意的次要度量,这是人类广泛研究的两个注意组成部分。这使我们能够独立于规则学习等混淆因素,检查小鼠的单次试验注意力动态。通过这种方法,我们显示出C57 / BL6小鼠的视敏度比以前测得的更好,它们在高和低警觉状态之间有节奏地交替变化,并且可以通过奖励意外情况的微小变化来提示他们采用不同的性能策略。

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