Literature DB >> 18387587

Dorsal anterior cingulate cortex integrates reinforcement history to guide voluntary behavior.

Clay B Holroyd1, Michael G H Coles.   

Abstract

Two competing types of theory have been proposed about the function of dorsal anterior cingulate cortex (dACC): evaluative theories hold that dACC monitors ongoing behavior to detect errors or conflict, whereas response selection theories hold that dACC is directly involved in the decision making process. In particular, one response selection theory proposes that dACC utilizes reward prediction error signals carried by the midbrain dopamine system to decide which of several competing motor control systems should be given control over the motor system (Holroyd and Coles, 2002). The theory further proposes that the impact of these dopamine signals on dACC determines the amplitude of a component of the event-related brain potential called the error-related negativity (ERN). In the present study, we applied this theory to a decision making problem that requires participants to select between two response options in which an erroneous choice is not clearly defined. Rather, the reward received for a particular response evolves in relation to the individual's previous behavior. We adapted a computational model associated with the theory to simulate human performance and the ERN in the task, and tested the predictions of the model against empirical ERP data. Our results indicate that ERN amplitude reflects the subjective value attributed by each participant to their response options as derived from their recent reward history. This finding is consistent with the position that dACC integrates the recent history of reinforcements to guide voluntary choice behavior, as opposed to evaluating behaviors per se.

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Mesh:

Year:  2007        PMID: 18387587     DOI: 10.1016/j.cortex.2007.08.013

Source DB:  PubMed          Journal:  Cortex        ISSN: 0010-9452            Impact factor:   4.027


  59 in total

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Authors:  Elizabeth C Finger; Abigail A Marsh; Karina S Blair; Marguerite E Reid; Courtney Sims; Pamela Ng; Daniel S Pine; R James R Blair
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Review 4.  Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis.

Authors:  Henry W Chase; Poornima Kumar; Simon B Eickhoff; Alexandre Y Dombrovski
Journal:  Cogn Affect Behav Neurosci       Date:  2015-06       Impact factor: 3.282

5.  When is an error not a prediction error? An electrophysiological investigation.

Authors:  Clay B Holroyd; Olave E Krigolson; Robert Baker; Seung Lee; Jessica Gibson
Journal:  Cogn Affect Behav Neurosci       Date:  2009-03       Impact factor: 3.282

6.  Single dose of a dopamine agonist impairs reinforcement learning in humans: evidence from event-related potentials and computational modeling of striatal-cortical function.

Authors:  Diane L Santesso; A Eden Evins; Michael J Frank; Erika C Schetter; Ryan Bogdan; Diego A Pizzagalli
Journal:  Hum Brain Mapp       Date:  2009-07       Impact factor: 5.038

7.  Beyond valence and magnitude: a flexible evaluative coding system in the brain.

Authors:  Ruolei Gu; Zhihui Lei; Lucas Broster; Tingting Wu; Yang Jiang; Yue-Jia Luo
Journal:  Neuropsychologia       Date:  2011-10-14       Impact factor: 3.139

Review 8.  Neurocomputational mechanisms of reinforcement-guided learning in humans: a review.

Authors:  Michael X Cohen
Journal:  Cogn Affect Behav Neurosci       Date:  2008-06       Impact factor: 3.282

9.  Better than expected or as bad as you thought? The neurocognitive development of probabilistic feedback processing.

Authors:  Wouter van den Bos; Berna Güroğlu; Bianca G van den Bulk; Serge A R B Rombouts; Eveline A Crone
Journal:  Front Hum Neurosci       Date:  2009-12-01       Impact factor: 3.169

10.  Different methods to define utility functions yield similar results but engage different neural processes.

Authors:  Marcus Heldmann; Bodo Vogt; Hans-Jochen Heinze; Thomas F Münte
Journal:  Front Behav Neurosci       Date:  2009-10-30       Impact factor: 3.558

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