一种潜在振荡器测量模型，用于模拟视频中情绪表达得分的动态变化
A Latent Oscillator Measurement Model to Simulate Emotional-Expression Score Dynamics in Video
AI总结：
提出潜在振荡器测量模型（LOMM）模拟视频情绪得分动态，分离潜在过程与测量误差，实验表明其生成数据优于基线，并能有效检验分析方法的维度恢复能力。
AI中文摘要：
面部表情分类器将视频转换为多变量时间序列得分，这些得分受到分类器、视频和录制条件带来的测量误差的影响。经验得分序列无法确定得分通道是否反映了较少数量的潜在表达过程，也无法确定分析是否能恢复这些过程。我们引入了潜在振荡器测量模型（LOMM），这是一个数据生成模型，将潜在动态、时变活动以及因子分析观测模型分离开来。潜在过程是阻尼、无阻尼或放大的线性振荡器。LOMM生成有界得分或连续指标。研究1使用来自100个MAFW视频得分的四折交叉拟合来校准LOMM，并在留出视频上评估生成的序列。LOMM的中位合理性和覆盖率分别为0.970和0.920，而校准的静态逻辑正态生成器分别为0.510和0.370。研究2测试了动态探索性图分析（DynEGA）、静态EGA、GraphicalVAR和GIMME是否能从LOMM生成的连续指标中恢复已知的维度结构。在每片段100个观测值下，将失败或超时的拟合计为不正确，正确维度恢复率对于DynEGA为0.939，静态EGA为0.884，GraphicalVAR为0.777，GIMME为0.176。将常见的固定初始化替换为稳定维度的独立平稳起始和放大维度的有界独立起始，降低了DynEGA、静态EGA和GraphicalVAR的恢复率。LOMM提供了一种受控测试，用于在得分模式被心理学解释之前，检验分析是否能恢复指定的潜在结构。
英文摘要：
Facial-expression classifiers convert video into multivariate time series of scores with measurement error from classifiers, videos, and recording conditions. Empirical score series cannot establish whether the score channels reflect a smaller set of latent expressive processes or whether an analysis would recover those processes. We introduce the Latent Oscillator Measurement Model (LOMM), a data-generating model that separates latent dynamics, time-varying activity, and a factor-analytic observation model. The latent processes are damped, undamped, or amplifying linear oscillators. LOMM generates bounded scores or continuous indicators. Study 1 used four-fold cross-fitting with scores from 100 MAFW videos to calibrate LOMM and evaluate generated series on held-out videos. Median plausibility and coverage were 0.970 and 0.920 for LOMM, versus 0.510 and 0.370 for a calibrated static logistic-normal generator. Study 2 tested whether Dynamic Exploratory Graph Analysis (DynEGA), static EGA, GraphicalVAR, and GIMME recovered a known dimensional structure from continuous indicators generated by LOMM. At 100 observations per clip, with failed or timed-out fits counted as incorrect, correct-dimension recovery was 0.939 for DynEGA, 0.884 for static EGA, 0.777 for GraphicalVAR, and 0.176 for GIMME. Replacing the common fixed initialization with independent stationary starts for stable dimensions and bounded independent starts for amplifying dimensions reduced recovery for DynEGA, static EGA, and GraphicalVAR. LOMM provides a controlled test of whether an analysis recovers aspecified latent structure before score patterns are interpreted psychologically.
