内嗅网格般的代码和时间锁定的网络动态:我们如何在人群中导航
![Modified navigation task. a The task projected participants into a first-person perspective within a virtual reality (VR) environment while they were asked to observe and subsequently re-trace the paths of a demonstrator. The movement area was marked with a red circle on the sandy desert plane and was surrounded by an observation area (solid and dashed circles in bird’s eye and street views, respectively). During observation (left panel), participants were placed directly behind the demonstrator’s starting point, were not able to move, and viewed the demonstrator walking through the circular arena (movement trajectory schematically indicated). During navigation (right panel), the demonstrator disappeared and the participant was projected onto the same starting position to re-trace the previously observed path. b Timeline of one example trial during the modified navigation task (s, seconds). A performance threshold of 20 virtual meters (vm) determined the feedback that participants received on each trial (i.e., happy emoji, cumulative distance error ≤ 20 vm; sad emoji, cumulative distance error > 20 vm). c Example path of demonstrator and participant obtained from observation and navigation periods, respectively. A trial comprised three random path segments that each started and ended at the edge of the movement area (i.e., participants observed/walked paths from one edge to the other and were not able to stop and turn within the movement area). We then calculated the distance error per path segment, yielding the cumulative distance error per trial. d Average cumulative distance error within the sample ( N = 58). Error bars reflect the standard error of the mean, s.e.m. e Permutation distribution obtained from simulating the paths of a random agent 5000 times (“Methods”). Chance level was calculated as the 5th percentile of the distribution, yielding a value of 39.3 vm (red line). The dashed line indicates the value of the observed group mean (17.57 vm, shown in d ). Credit: Nature Communications (2023). DOI: 10.1038/s41467-023-35819-3 我们如何在人群中穿行](https://scx1.b-cdn.net/csz/news/800a/2023/how-we-navigate-throug.jpg)
维也纳大学的科学家们现在首次证明,网格细胞不仅能帮助我们在复杂的环境中找到自己的路径,还能帮助我们分析其他人的行动。他们的新研究自然通讯还提出了一种可能导致痴呆患者迷失方向的机制的解释。
无论你是在拥挤的步行区穿行,还是在团队游戏中朝着目标努力,在这两种情况下,重要的是不仅要考虑自己的动作,还要考虑他人的动作。这些导航和定位过程由大脑细胞登记我们的当前位置我们从哪里来,往哪里去,朝哪个方向看。
通过他们的共同活动,他们创造了我们周围环境的“地图”。这些单元的一种特殊类型是网格单元内嗅皮层颞叶是大脑中部的一个小区域。它们的功能就像大脑自己的GPS,因为它们不仅代表我们在空间中的位置,而且还可以将其与同一空间中的其他点联系起来。
由维也纳大学心理学院的伊莎贝拉·瓦格纳和克劳斯·拉姆领导的科学家们研究的问题是,这些网格细胞是否也参与了在这张地图上绘制其他人的运动。为了这个目的,科学家们测试了参与者,他们要么自己在一个虚拟环境或者观察另一个人的运动,而他们的大脑活动被测量功能性磁共振成像(fMRI)。
他们发现大脑的活动观察他人时记录的活动与网格细胞的活动相当。此外,该团队还能够证明,这种活动是与导航过程相关的更大的大脑区域网络的一部分。然而,有趣的是,实验对象越善于跟随他人的脚步,这个网络就越不活跃。瓦格纳解释说:“我们将其解释为网格细胞的效率更高,这可能会降低使用更大的大脑网络的必要性。”
研究结果表明,网格细胞属于一个更大的网络大脑除其他方面外,协调导航进程的区域。然而,这个网络特别受衰老过程的影响,特别是受痴呆症的影响。
瓦格纳解释说:“网格细胞的功能随着年龄和痴呆症而下降。结果,人们再也找不到路了,他们的方向也被削弱了。”该小组的进一步研究现在致力于解决是否网状细胞还涉及到对他人的认知——这方面在晚期痴呆症中经常受损。
更多信息:伊莎贝拉·c·瓦格纳等人,内嗅网格状代码和时间锁定网络动态跟踪其他人在空间中导航,自然通讯(2023)。DOI: 10.1038 / s41467 - 023 - 35819 - 3