Abstract：
:Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Turner BM,Forstmann BU,Wagenmakers EJ,Brown SD,Sederberg PB,Steyvers Mdoi
10.1016/j.neuroimage.2013.01.048subject
Has Abstractpub_date
2013-05-15 00:00:00pages
193-206eissn
1053-8119issn
1095-9572pii
S1053-8119(13)00095-5journal_volume
72pub_type
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