Continuous-time deconvolutional regression for psycholinguistic modeling
Continuous-time deconvolutional regression for psycholinguistic modeling
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DOI：
10.1016/j.cognition.2021.104735
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发表时间：
2021-07-21
期刊：
影响因子：
3.4
通讯作者：
Schuler, William
中科院分区：
文献类型：
--
作者：
Shain, Cory;Schuler, William
心理语言学实验中刺激的影响会随着时间的推移而扩散，因为人类对语言的反应不是瞬间的。通常用于分析心理语言学数据的线性模型是无法解释这种现象，由于强烈的时间独立性假设，而现有的去卷积方法估计扩散时间结构模型时间离散，因此不能直接应用于自然语言刺激事件（词）具有可变的持续时间。根据连续时间去卷积回归（CDR）可以解决这些问题的证据，（Shain &舒勒，2018），这篇文章激发了CDR在许多实验环境中的使用，验证了它的一些数学特性，并根据经验评估了各种实验混淆的影响（噪声、多重共线性和脉冲响应误指定）、超参数设置和响应类型（行为和fMRI）。结果表明，CDR（1）在各种超参数配置上产生高度一致的估计，（2）即使在不利的训练条件下，也能忠实地恢复合成数据的数据生成模型，（3）在应用于自然阅读和fMRI数据时，优于广泛使用的统计方法。此外，使用CDR测试科学假设的程序进行了定义和演示，并提出了CDR建模的最佳实践。结果支持使用CDR分析心理语言学时间序列，特别是在一个自然主义的实验范式。
The influence of stimuli in psycholinguistic experiments diffuses across time because the human response to language is not instantaneous. The linear models typically used to analyze psycholinguistic data are unable to account for this phenomenon due to strong temporal independence assumptions, while existing deconvolutional methods for estimating diffuse temporal structure model time discretely and therefore cannot be directly applied to natural language stimuli where events (words) have variable duration. In light of evidence that continuous-time deconvolutional regression (CDR) can address these issues (Shain & Schuler, 2018), this article motivates the use of CDR for many experimental settings, exposits some of its mathematical properties, and empirically evaluates the influence of various experimental confounds (noise, multicollinearity, and impulse response misspecification), hyperparameter settings, and response types (behavioral and fMRI). Results show that CDR (1) yields highly consistent estimates across a variety of hyperparameter configurations, (2) faithfully recovers the data-generating model on synthetic data, even under adverse training conditions, and (3) outperforms widely-used statistical approaches when applied to naturalistic reading and fMRI data. In addition, procedures for testing scientific hypotheses using CDR are defined and demonstrated, and empirically-motivated best-practices for CDR modeling are proposed. Results support the use of CDR for analyzing psycholinguistic time series, especially in a naturalistic experimental paradigm.