Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort
Date
2017
Authors
Vassena, Eliana
Holroyd, Clay B.
Alexander, William H.
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers in Neuroscience
Abstract
In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework.
Description
Keywords
anterior cingulate cortex (ACC), effort, prediction error, computational models of ACC, computational modeling, effortful control
Citation
Vassena, E.; Holroyd, C.B.; & Alexander, W.H. (2017). Computational models of anterior cingulate cortex: At the crossroads between prediction and effort. Frontiers in Neuroscience, 11, article 316. doi: 10.3389/fnins.2017.00316