Tang Bohao, Levine Michael, Adamek Jack H, Wodka Ericka L, Caffo Brian S, Ewen Joshua B
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Kennedy Krieger Institute, Baltimore, MD, United States.
Front Psychol. 2023 Feb 24;14:1060525. doi: 10.3389/fpsyg.2023.1060525. eCollection 2023.
We used a large convenience sample ( = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa. To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids "universality and uniqueness" tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development.
我们使用了来自西蒙斯助力自闭症研究（SPARK）数据集的一个大型便利样本（n = 22,223）来评估自闭症核心症状的因果解释理论。具体而言，所收集的数据项支持对那些将语言能力改变假定为社交退缩原因的理论进行检验，以及对与这些语言理论相竞争的替代理论进行检验。我们使用这个大型数据集得出的结果与自这些理论首次提出以来几十年里该领域的发展趋势相一致，即支持原发性社交退缩（在某些自闭症病例中）是语言发展改变的原因，而非相反。为实现上述实证目标，我们采用了一种高度受理论约束的方法，这种方法不同于当前数据驱动的建模趋势，但与理论驱动心理学最近的复兴相一致。除了对理论阐述进行仔细的解释和形式化之外，我们还为这类未来研究提出了三条原则：具体化、量化和整合。具体化是指用来自自闭症研究内外的已有数据、更精细的模型和更真实的测量方法以及纵向数据收集来约束模型。量化是指使用心理因果关系的连续测量方法以及加权图。这种方法避免了那种认为单一认知差异可能导致异质且复杂行为表型的“普遍性和独特性”测试。在单个模型中整合多条解释路径有助于该领域研究单一行为特征或多个行为特征的多种促成因素。它还允许跨多个当前诊断以及典型发展阶段整合解释理论。
