Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex

Mack, Michael L.; Preston, Alison R.

doi:10.1016/j.neuroimage.2015.12.015

Cited by 75 publications

(99 citation statements)

References 85 publications

(165 reference statements)

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“…That said, when we decoded prediction strength directly from the hippocampus, it was not related to univariate hippocampal outcome responses. This null result may simply reflect the difficulty of decoding predictions from the hippocampus (Mack and Preston, 2016). Alternatively, it is possible that the DMN plays an important role in transforming or processing hippocampal predictions before they are fed back to the hippocampus.…”

Section: Discussionmentioning

confidence: 99%

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes

2016

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The hippocampus is thought to compare predicted events with current perceptual input, generating a mismatch signal when predictions are violated. However, most prior studies have only inferred when predictions occur without measuring them directly. Moreover, an important but unresolved question is whether hippocampal mismatch signals are modulated by the degree to which predictions differ from outcomes. Here, we conducted a human fMRI study in which subjects repeatedly studied various word-picture pairs, learning to predict particular pictures (outcomes) from the words (cues). After initial learning, a subset of cues was paired with a novel, unexpected outcome, whereas other cues continued to predict the same outcome. Critically, when outcomes changed, the new outcome was either "near" to the predicted outcome (same visual category as the predicted picture) or "far" from the predicted outcome (different visual category). Using multivoxel pattern analysis, we indexed cue-evoked reactivation (prediction) within neocortical areas and related these trial-by-trial measures of prediction strength to univariate hippocampal responses to the outcomes. We found that prediction strength positively modulated hippocampal responses to unexpected outcomes, particularly when unexpected outcomes were close, but not identical, to the prediction. Hippocampal responses to unexpected outcomes were also associated with a tradeoff in performance during a subsequent memory test: relatively faster retrieval of new (updated) associations, but relatively slower retrieval of the original (older) associations. Together, these results indicate that hippocampal mismatch signals reflect a comparison between active predictions and current outcomes and that these signals are most robust when predictions are similar, but not identical, to outcomes.

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Section: Discussionmentioning

confidence: 99%

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes

2016

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“…Two notable exceptions have shown performance-related reinstatement of specific memory content in hippocampus, perirhinal cortex (PRc), and parahippocampal cortex (PHc) (Mack & Preston, 2016; Staresina, Henson, Kriegeskorte, & Alink, 2012). In one of these studies, PHc encoding patterns for word-scene associations were reinstated when participants recalled the correct scene in response to its associated word cue (Staresina et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In one of these studies, PHc encoding patterns for word-scene associations were reinstated when participants recalled the correct scene in response to its associated word cue (Staresina et al, 2012). A more recent study (Mack & Preston, 2016) combined high-resolution fMRI with a multivariate decoding approach designed to index retrieval of specific items. Mack and colleagues provided evidence for reinstatement of specific faces in PRc and specific scenes in hippocampus during recall, with the fidelity of MTL reinstatement predicting the speed of memory decisions.…”

Section: Introductionmentioning

confidence: 99%

Medial temporal lobe reinstatement of content-specific details predicts source memory

Liang

Preston

2017

Cortex

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Leading theories propose that when remembering past events, medial temporal lobe (MTL) structures reinstate the neural patterns that were active when those events were initially encoded. Accurate reinstatement is hypothesized to support detailed recollection of memories, including their source. While several studies have linked cortical reinstatement to successful retrieval, indexing reinstatement within the MTL network and its relationship to memory performance has proved challenging. Here, we addressed this gap in knowledge by having participants perform an incidental encoding task, during which they visualized people, places, and objects in response to adjective cues. During a surprise memory test, participants saw studied and novel adjectives and indicated the imagery task they performed for each adjective. A multivariate pattern classifier was trained to discriminate the imagery tasks based on functional magnetic resonance imaging (fMRI) responses from hippocampus and MTL cortex at encoding. The classifier was then tested on MTL patterns during the source memory task. We found that MTL encoding patterns were reinstated during successful source retrieval. Moreover, when participants made source misattributions, errors were predicted by reinstatement of incorrect source content in MTL cortex. We further observed a gradient of content-specific reinstatement along the anterior-posterior axis of hippocampus and MTL cortex. Within anterior hippocampus, we found that reinstatement of person content was related to source memory accuracy, whereas reinstatement of place information across the entire hippocampal axis predicted correct source judgments. Content-specific reinstatement was also graded across MTL cortex, with PRc patterns evincing reactivation of people and more posterior regions, including PHc, showing evidence for reinstatement of places and objects. Collectively, these findings provide key evidence that source recollection relies on reinstatement of past experience within the MTL network.

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“…The neural similarities themselves can be visualized by applying multidimensional scaling to further understand the properties of the space (Davis et al, 2014). RSA has been useful in a number of other endeavors, such as understanding the role of various brain areas in reinstating past experiences (Tompary et al, 2016; Mack and Preston, 2016). …”

Section: Introductionmentioning

confidence: 99%

What the success of brain imaging implies about the neural code

Guest

Love

2017

eLife

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The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI’s limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI’s successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI.DOI: http://dx.doi.org/10.7554/eLife.21397.001

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Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex

Cited by 75 publications

References 85 publications

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes

Medial temporal lobe reinstatement of content-specific details predicts source memory

What the success of brain imaging implies about the neural code

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