Model-based fMRI analysis of memory

Recent advances in Model-based fMRI approaches enable researchers to investigate hypotheses about the time course and latent structure in data that were previously inaccessible. Cognitive models, especially when validated on multiple datasets, allow for additional constraints to be marshalled when interpreting neuroimaging data. Models can be related to BOLD response in a variety of ways, such as constraining the cognitive model by neural data, interpreting the neural data in light of behavioural fit, or simultaneously accounting for both neural and behavioural data. Using cognitive models as a lens on fMRI data is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach

A neurocognitive model for predicting the fate of individual memories

One goal of cognitive science is to build theories of mentalfunction that predict individual behavior. In this project wefocus on predicting, for individual participants, which specificitems in a list will be remembered at some point in the future.If you want to know if an individual will remember something,one commonsense approach is to give them a quiz or test suchthat a correct answer likely indicates later memory for an item.In this project we attempt to predict later memory without ex-plicit assessments by jointly modeling both neural and behav-ioral data in a computational cognitive model which capturesthe dynamics of memory acquisition and decay. In this paper,we lay out a novel hierarchical Bayesian approach for com-bining neural and behavioral data and present results showinghow fMRI signals recorded during the study phase of a mem-ory task can improve our ability to predict (in held-out data)which items will be remembered or forgotten 72 hours later

A neurocognitive model for predicting the fate of individual memories

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without explicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later

A neurocognitive model for predicting the fate of individual memories

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without explicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later