Assistant Professor, Department of Psychology
Ph.D. Stanford University
Postdoctoral Fellowship, Yale University
Research Interests: Cognitive Neuroscience, Memory, Cognitive Control, fMRI Methods
Overview: I am interested in how our perceptual experiences are transformed into memories and how we recreate and selectively recall these experiences. Research in my lab makes use of behavioral and neuroimaging methods (primarily fMRI) with an emphasis on applying machine learning algorithms and multivariate pattern analyses to neuroimaging data in order to understand how memories are represented and transformed in distributed patterns of brain activity.
Some of the specific topics my lab addresses include: What are the cognitive and neural mechanisms that cause forgetting? How is competition between memories signaled and resolved in the brain during retrieval? How do we reduce interference between memories during encoding? Addressing these questions involves understanding the interactions and relative contributions of fronto-parietal cortex and medial temporal lobe structures.
Experience-dependent hippocampal pattern differentiation prevents interference during subsequent learning.
Nat Commun. 2016 Apr 06;7:11066
Authors: Favila SE, Chanales AJ, Kuhl BA
The hippocampus is believed to reduce memory interference by disambiguating neural representations of similar events. However, there is limited empirical evidence linking representational overlap in the hippocampus to memory interference. Likewise, it is not fully understood how learning influences overlap among hippocampal representations. Using pattern-based fMRI analyses, we tested for a bidirectional relationship between memory overlap in the human hippocampus and learning. First, we show that learning drives hippocampal representations of similar events apart from one another. These changes are not explained by task demands to discriminate similar stimuli and are fully absent in visual cortical areas that feed into the hippocampus. Second, we show that lower representational overlap in the hippocampus benefits subsequent learning by preventing interference between similar memories. These findings reveal targeted experience-dependent changes in hippocampal representations of similar events and provide a critical link between memory overlap in the hippocampus and behavioural expressions of memory interference.
PMID: 27925613 [PubMed - in process]
Hippocampal mismatch signals are modulated by the strength of neural predictions and their simi- larity to outcomes.
J Neurosci. 2016 Nov 7;:
Authors: Long NM, Lee H, Kuhl BA
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 directly measuring them. 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). Following initial learning, a subset of cues were 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 multi-voxel 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.
SIGNIFICANCE STATEMENT: Although the hippocampus is widely thought to signal 'mismatches' between memory-based predictions and out- comes, previous research has not directly linked hippocampal mismatch signals to neural measures of prediction strength. Here, we show that hippocampal mismatch signals increase as a function of the strength of predictions in neocortical regions. This increase in hippocampal mismatch signals was particularly robust when outcomes were similar, but not identical, to predictions. These results indicate that hippocampal mismatch signals are driven by both the active generation of predictions as well as the similarity between predictions and outcomes.
PMID: 27821577 [PubMed - as supplied by publisher]