Shinkareva Svetlana V, Gao Chuanji, Wedell Douglas
Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC 29201 USA.
Affect Sci. 2020 Nov 25;1(4):237-246. doi: 10.1007/s42761-020-00023-9. eCollection 2020 Dec.
Hedonic valence describes the pleasantness or unpleasantness of psychological states elicited by stimuli and is conceived as a fundamental building block of emotional experience. Multivariate pattern analysis approaches contribute to the study of valence representation by allowing identification of valence from distributed patterns of activity. However, the issue of construct validity arises in that there is always a possibility that classification results from a single study are driven by factors other than valence, such as the idiosyncrasies of the stimuli. In this work, we identify valence across participants from six different fMRI studies that used auditory, visual, or audiovisual stimuli, thus increasing the likelihood that classification is driven by valence and not by the specifics of the experimental paradigm of a particular study. The studies included a total of 93 participants and differed on stimuli, task, trial duration, number of participants, and scanner parameters. In a leave-one-study-out cross-validation procedure, we trained the classifiers on fMRI data from five studies and predicted valence, positive or negative, for each of the participants in the left-out study. Whole-brain classification demonstrated a reliable distinction between positive and negative valence states (72% accuracy). In a searchlight analysis, the representation of valence was localized to the right postcentral and supramarginal gyri, left superior frontal and middle frontal cortices, and right pregenual anterior cingulate and superior medial frontal cortices. The demonstrated cross-study classification of valence enhances the construct validity and generalizability of the findings from the combined studies.
享乐效价描述了由刺激引发的心理状态的愉悦或不悦程度,并被视为情感体验的基本组成部分。多变量模式分析方法通过允许从分布式活动模式中识别效价,为效价表征的研究做出了贡献。然而,结构效度问题随之出现,因为单一研究的分类结果总是有可能受到效价以外的因素驱动,比如刺激的特质。在这项研究中,我们从六项不同的功能磁共振成像(fMRI)研究的参与者中识别效价,这些研究使用了听觉、视觉或视听刺激,从而增加了分类是由效价而非特定研究的实验范式细节驱动的可能性。这些研究总共包括93名参与者,在刺激、任务、试验时长、参与者数量和扫描仪参数方面存在差异。在留一研究法交叉验证程序中,我们使用五项研究的fMRI数据训练分类器,并对留出研究中的每位参与者的效价(积极或消极)进行预测。全脑分类显示出积极和消极效价状态之间的可靠区分(准确率72%)。在一项探照灯分析中,效价表征定位于右侧中央后回和缘上回、左侧额上回和额中回、以及右侧膝前扣带回和额内侧上回。所展示的效价跨研究分类增强了联合研究结果的结构效度和可推广性。