Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

被引:83
|
作者
Vassena, Eliana [1 ,2 ]
Holroyd, Clay B. [3 ]
Alexander, William H. [2 ]
机构
[1] Radboud Univ Nijmegen, Donders Ctr Cognit Neuroimaging, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands
[2] Univ Ghent, Dept Expt Psychol, Ghent, Belgium
[3] Univ Victoria, Dept Psychol, Victoria, BC, Canada
来源
FRONTIERS IN NEUROSCIENCE | 2017年 / 11卷
基金
欧盟地平线“2020”;
关键词
anterior cingulate cortex (ACC); effort; prediction error; computational models of ACC; computational modeling; effortful control; MEDIAL PREFRONTAL CORTEX; COGNITIVE CONTROL; FRONTAL-CORTEX; REWARD PREDICTION; NEURONAL-ACTIVITY; ERROR LIKELIHOOD; DISTINCT REGIONS; DECISION-MAKING; NEURAL BASIS; CONFLICT;
D O I
10.3389/fnins.2017.00316
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
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收藏
页数:9
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