University of Tübingen.
J Cogn Neurosci. 2014 May;26(5):1000-12. doi: 10.1162/jocn_a_00545. Epub 2013 Dec 17.
In everyday situations, quantitative rules, such as "greater than/less than," need to be applied to a multitude of magnitude comparisons, be they sensory, spatial, temporal, or numerical. We have previously shown that rules applied to different magnitudes are encoded in the lateral PFC. To investigate if and how other frontal lobe areas also contribute to the encoding of quantitative rules applied to multiple magnitudes, we trained monkeys to switch between "greater than/less than" rules applied to either line lengths (spatial magnitudes) or dot numerosities (discrete numerical magnitudes). We recorded single-cell activity from the dorsal premotor cortex (dPMC) and cingulate motor cortex (CMA) and compared it with PFC activity. We found the largest proportion of quantitative rule-selective cells in PFC (24% of randomly selected cells), whereas neurons in dPMC and CMA rarely encoded the rule (6% of the cells). In addition, rule selectivity of individual cells was highest in PFC neurons compared with dPMC and CMA neurons. Rule-selective neurons that simultaneously represented the "greater than/less than" rules applied to line lengths and numerosities ("rule generalists") were exclusively present in PFC. In dPMC and CMA, however, neurons primarily encoded rules applied to only one of the two magnitude types ("rule specialists"). Our data suggest a special involvement of PFC in representing quantitative rules at an abstract level, both in terms of the proportion of neurons engaged and the coding capacities.
在日常生活情境中,需要将“大于/小于”等定量规则应用于大量的大小比较,无论是感觉、空间、时间还是数字。我们之前已经表明,应用于不同大小的规则是在外侧前额叶皮层编码的。为了研究其他额叶区域是否以及如何有助于对应用于多个大小的定量规则进行编码,我们训练猴子在“大于/小于”规则之间切换,这些规则适用于线长度(空间大小)或点数量(离散数值大小)。我们记录了背侧运动前皮层 (dPMC) 和扣带运动皮层 (CMA) 的单细胞活动,并将其与 PFC 活动进行了比较。我们发现 PFC 中具有最大比例的定量规则选择性细胞(随机选择的细胞中 24%),而 dPMC 和 CMA 中的神经元很少编码规则(细胞的 6%)。此外,与 dPMC 和 CMA 神经元相比,PFC 神经元的个体规则选择性最高。同时表示适用于线长度和数量的“大于/小于”规则的规则选择性神经元(“规则通才”)仅存在于 PFC 中。然而,在 dPMC 和 CMA 中,神经元主要编码仅适用于两种大小类型之一的规则(“规则专家”)。我们的数据表明,PFC 特别参与以抽象水平表示定量规则,无论是参与神经元的比例还是编码能力。