Department of Psychology, New York University, New York, NY 10003.
Department of Psychology, Columbia University, New York, NY 10027.
Proc Natl Acad Sci U S A. 2023 Mar 28;120(13):e2120288120. doi: 10.1073/pnas.2120288120. Epub 2023 Mar 23.
Over 40 y of accumulated research has detailed associations between neuroimaging signals measured during a memory encoding task and later memory performance, across a variety of brain regions, measurement tools, statistical approaches, and behavioral tasks. But the interpretation of these subsequent memory effects (SMEs) remains unclear: if the identified signals reflect cognitive and neural mechanisms of memory encoding, then the underlying neural activity must be causally related to future memory. However, almost all previous SME analyses do not control for potential confounders of this causal interpretation, such as serial position and item effects. We collect a large fMRI dataset and use an experimental design and analysis approach that allows us to statistically adjust for nearly all known exogenous confounding variables. We find that, using standard approaches without adjustment, we replicate several univariate and multivariate subsequent memory effects and are able to predict memory performance across people. However, we are unable to identify any signal that reliably predicts subsequent memory after adjusting for confounding variables, bringing into doubt the causal status of these effects. We apply the same approach to subjects' judgments of learning collected following an encoding period and show that these behavioral measures of mnemonic status do predict memory after adjustments, suggesting that it is possible to measure signals near the time of encoding that reflect causal mechanisms but that existing neuroimaging measures, at least in our data, may not have the precision and specificity to do so.
四十多年来的积累研究详细描述了在记忆编码任务中测量的神经影像学信号与之后的记忆表现之间的关联,涉及多种大脑区域、测量工具、统计方法和行为任务。但是,这些后续记忆效应(SMEs)的解释仍然不清楚:如果所确定的信号反映了记忆编码的认知和神经机制,那么潜在的神经活动必须与未来的记忆有因果关系。然而,几乎所有之前的 SME 分析都没有控制这种因果关系解释的潜在混杂因素,例如序列位置和项目效应。我们收集了一个大型 fMRI 数据集,并使用实验设计和分析方法,使我们能够统计调整几乎所有已知的外生混杂变量。我们发现,在没有调整的情况下使用标准方法,我们复制了几个单变量和多变量的后续记忆效应,并能够预测个体之间的记忆表现。然而,在调整混杂变量后,我们无法确定任何可靠地预测后续记忆的信号,这使这些效应的因果地位受到质疑。我们将相同的方法应用于被试在编码期后收集的学习判断,并表明这些记忆状态的行为测量在调整后确实可以预测记忆,这表明有可能在接近编码时间测量反映因果机制的信号,但至少在我们的数据中,现有的神经影像学测量可能没有足够的精度和特异性来做到这一点。