Distributed Patterns of Reactivation Predict Vividness of Recollection

Abstract

Abstract■ According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection. ■

Multiscale Integration of Contextual Information During a Naturalistic Task

“…The second analysis directly contrasted similarity between first halves and HC videos and first halves and NC videos. Surprisingly, and in contrast to previous findings using similar designs (Bird et al 2015;St-Laurent et al 2015;Chen et al 2016), we observed few significant effects in these analyses. Further details of the methods and results for these analyses can be found in the Supplementary Material.…”

“…Though the use of naturalistic video stimuli has proven effective in circumventing some of the associated problems (Zacks et al 2001;Hasson et al 2008;van Kesteren et al 2010;Bird et al 2015;St-Laurent et al 2015), one limitation of the use of such stimuli in fMRI experiments is the difficulty in creating matched sets over which to perform contrasts. The inherent complexity of naturalistic videos and the many dimensions across which they may differ render it difficult to confidently ascribe differences in BOLD response to particular aspects of the stimulus.…”

Distinct neural mechanisms underlie the success, precision, and vividness of episodic memory

“…In addition to the dissociation between objective measures of memory retrieval involving the hippocampus and AnG, our subjective measure of memory performance, retrieval vividness, correlated with precuneus activity, consistent with recent findings linking activity in dorsomedial brain areas with how vividly video clips were recalled (St-Laurent et al, 2015). In line with a proposed involvement in vividness judgments, previous evidence has found greater connectivity between prefrontal cortex and precuneus during metacognitive decisions concerning retrieved memories (Baird et al, 2013), supporting a role for medial parietal regions in assessments of subjective memory quality.…”

“…However, increasing research has additionally considered subjective or metacognitive measures of memory performance, such as confidence (Yonelinas, 1994) or the vividness with which retrieved memories are experienced (Kuhl and Chun, 2014; St-Laurent et al, 2015). Research has found that subjective and objective measures of memory performance can diverge (Chua et al, 2012; Harlow and Yonelinas, 2016).…”

No evidence of somatotopic place of articulation feature mapping in motor cortex during passive speech perception

“…This involves training a pattern classifier on one condition (i.e., speech miming) and testing the fitted model on another condition (i.e., speech perception). The cross-decoding approach has been used successfully in vision literature, for example, to reveal overlapping patterns of activation between perceptual data and visual imagery (Stokes, Thompson, Cusack, & Duncan, 2009;St-Laurent, Abdi, & Buchsbaum, 2015). If speech miming and speech perception share a representational code in PMC, then the PoA classifier trained on speech miming data should be able to reliably classify PoA in the speech perception data.…”