Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany; Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo 05508-000, Brazil.
Neuroimage. 2022 Nov 15;262:119561. doi: 10.1016/j.neuroimage.2022.119561. Epub 2022 Aug 13.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
几项最近的研究调查了导致感知和行为报告的认知过程的节奏性质。这些研究使用了不同的方法，目前尚未就一般标准达成一致。在这里，我们提出了一种测试和定量比较这些方法的方法。我们从一个典型的实验中模拟了行为数据，并使用几种方法分析了这些数据。我们应用了文献中发现的主要方法，即正弦拟合、离散傅里叶变换 (DFT) 和最小二乘谱 (LSS)。DFT 和 LSS 既可以应用于平均精度时间历程，也可以应用于单个试验。在规则采样而不是不规则采样的情况下，LSS 在数学上与 DFT 等效——这更为常见。LSS 还提供了一种可能性，可以考虑影响节奏强度的权重因素，例如唤醒度。统计推断要么在被调查的样本（固定效应）上进行，要么在模拟参与者的总体（随机效应）上进行。使用 False Discovery Rate、Bonferroni 或基于最大值的方法对频率进行多次比较。为了进行定量比较，我们计算了被调查分析方法和统计方法的灵敏度、特异性和 D-prime。在所研究的参数范围内，与基于平均精度时间历程的方法相比，单个试验方法具有更高的灵敏度和 D-prime。对于更高频率的模拟节奏，这种效果进一步增加。如果有一个额外的（可观察）因素影响检测性能，将该因素作为 LSS 的权重添加可以进一步提高灵敏度和 D-prime。对于多重比较校正，基于最大值的方法提供了最高的特异性和 D-prime，紧随其后的是 Bonferroni 方法。在给定固定的总试验次数的情况下，当试验分布在更多的参与者中时，随机效应方法的 D-prime 更高，尽管这给每个参与者的试验次数较少。最后，我们提出了使用阻尼正弦振荡器代替简单正弦函数的想法，以进一步提高对重置事件后观察到的行为节奏的拟合。
