The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, 2751, Australia.
School of Psychology, University of New South Wales, Sydney, NSW, Australia.
Sci Rep. 2024 May 20;14(1):11499. doi: 10.1038/s41598-024-62135-7.
The rapid transformation of sensory inputs into meaningful neural representations is critical to adaptive human behaviour. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive. Here we show that movement trajectories can be used to measure emerging neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging data via representational similarity analysis (RSA), we show that movement trajectories track the unfolding of stimulus- and category-wise neural representations along key dimensions of the human visual system. We demonstrate that time-resolved representational structures derived from movement trajectories overlap with those derived from M/EEG (albeit delayed) and those derived from fMRI in functionally-relevant brain areas. Our findings highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.
感觉输入向有意义的神经表示的快速转换对于适应性人类行为至关重要。虽然非侵入性神经影像学方法是研究神经表示的事实上的方法，但它们仍然昂贵、不易获得、耗时且具有限制性。在这里，我们表明运动轨迹可以用于以精细的时间分辨率测量新兴的神经表示。通过结合在线计算机鼠标跟踪和通过表示相似性分析 (RSA) 公开的神经影像学数据，我们表明运动轨迹沿着人类视觉系统的关键维度追踪刺激和类别特异性神经表示的展开。我们证明，从运动轨迹中得出的随时间变化的表示结构与从 M/EEG 中得出的（尽管有延迟）和从功能上相关的大脑区域中的 fMRI 中得出的表示结构重叠。我们的研究结果强调了运动轨迹的丰富性以及 RSA 框架揭示和比较其信息内容的强大功能，为更好地理解人类感知开辟了新途径。
