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.

Major theories for explaining the organization of semantic memory in the human brain are premised on the often-observed dichotomous dissociation between living and nonliving objects. Evidence from neuroimaging has been interpreted to suggest that this distinction is reflected in the functional topography of the ventral vision pathway as lateral-to-medial activation gradients. Recently, we observed that similar activation gradients also reflect differences among living stimuli consistent with the semantic dimension of graded animacy. Here, we address whether the salient dichotomous distinction between living and nonliving objects is actually reflected in observable measured brain activity or whether previous observations of a dichotomous dissociation were the illusory result of stimulus sampling biases. Using fMRI, we measured neural responses while participants viewed 10 animal species with high to low animacy and two inanimate categories. Representational similarity analysis of the activity in ventral vision cortex revealed a main axis of variation with high-animacy species maximally different from artifacts and with the least animate species closest to artifacts. Although the associated functional topography mirrored activation gradients observed for animate–inanimate contrasts, we found no evidence for a dichotomous dissociation. We conclude that a central organizing principle of human object vision corresponds to the graded psychological property of animacy with no clear distinction between living and nonliving stimuli. The lack of evidence for a dichotomous dissociation in the measured brain activity challenges theories based on this premise.

When we have a rich and vivid memory for a past experience, it often feels like we are transported back in time to witness once again this event. Indeed, a perfect memory would exactly mimic the experiential quality of direct sensory perception. We used fMRI and multivoxel pattern analysis to map and quantify the similarity between patterns of activation evoked by direct perception of a diverse set of short video clips and the vivid remembering, with closed eyes, of these clips. We found that the patterns of distributed brain activation during vivid memory mimicked the patterns evoked during sensory perception. Using whole-brain patterns of activation evoked by perception of the videos, we were able to accurately classify brain patterns that were elicited when participants tried to vividly recall those same videos. A discriminant analysis of the activation patterns associated with each video revealed a high degree (explaining over 80% of the variance) of shared representational similarity between perception and memory. These results show that complex, multifeatured memory involves a partial reinstatement of the whole pattern of brain activity that is evoked during initial perception of the stimulus.

We used fMRI to assess the neural correlates of autobiographical, semantic, and episodic memory retrieval in healthy young and older adults. Participants were tested with an event-related paradigm in which retrieval demand was the only factor varying between trials. A spatio-temporal partial least square analysis was conducted to identify the main patterns of activity characterizing the groups across conditions. We identified brain regions activated by all three memory conditions relative to a control condition. This pattern was expressed equally in both age groups and replicated previous findings obtained in a separate group of younger adults. We also identified regions whose activity differentiated among the different memory conditions. These patterns of differentiation were expressed less strongly in the older adults than in the young adults, a finding that was further confirmed by a barycentric discriminant analysis. This analysis showed an age-related dedifferentiation in autobiographical and episodic memory tasks but not in the semantic memory task or the control condition. These findings suggest that the activation of a common memory retrieval network is maintained with age, whereas the specific aspects of brain activity that differ with memory content are more vulnerable and less selectively engaged in older adults. Our results provide a potential neural mechanism for the well-known age differences in episodic/autobiographical memory, and preserved semantic memory, observed when older adults are compared with younger adults.

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These “brain-reading” analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.

Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency “line drawings” of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.