Medial prefrontal cortex as an action-outcome predictor
TLDR
It is shown that a simple model based on standard learning rules can simulate and unify an unprecedented range of known effects in mPFC, and suggests a new view of the medial prefrontal cortex, as a region concerned with learning and predicting the likely outcomes of actions, whether good or bad.Abstract:
The medial prefrontal cortex (mPFC) and especially anterior cingulate cortex is central to higher cognitive function and many clinical disorders, yet its basic function remains in dispute. Various competing theories of mPFC have treated effects of errors, conflict, error likelihood, volatility and reward, using findings from neuroimaging and neurophysiology in humans and monkeys. No single theory has been able to reconcile and account for the variety of findings. Here we show that a simple model based on standard learning rules can simulate and unify an unprecedented range of known effects in mPFC. The model reinterprets many known effects and suggests a new view of mPFC, as a region concerned with learning and predicting the likely outcomes of actions, whether good or bad. Cognitive control at the neural level is then seen as a result of evaluating the probable and actual outcomes of one's actions.read more
Citations
More filters
Journal ArticleDOI
Mediofrontal negativity signals unexpected omission of aversive events
TL;DR: ERP and skin conductance response (SCR) to the unexpected omission of electric shocks during Pavlovian aversive conditioning evidenced a stronger negative frontocentral ERP component after unexpected, relative to expected, shock-omission.
Journal ArticleDOI
Mediofrontal negativity signals unexpected timing of salient outcomes
Sara Garofalo,Sara Garofalo,Christopher Timmermann,Christopher Timmermann,Simone Battaglia,Martin E. Maier,Martin E. Maier,Giuseppe di Pellegrino,Giuseppe di Pellegrino +8 more
TL;DR: Mediofrontal ERP signals of prediction error were observed for outcomes occurring at unexpected times but were specific for salient (shock-associated), as compared with neutral, outcomes, which suggest a critical role of timing and salience information in prediction error signaling.
Journal ArticleDOI
Common and distinct brain activity associated with risky and ambiguous decision-making.
Ranjita Poudel,Michael C. Riedel,Taylor Salo,Jessica S. Flannery,Lauren D. Hill-Bowen,Simon B. Eickhoff,Angela R. Laird,Matthew T. Sutherland +7 more
TL;DR: Meta-analyses suggest a dissociation of brain regions linked with risky- and ambiguous-DM reflecting possible differential functionality and highlight brain alterations potentially contributing to poor decision-making in the context of substance use disorders.
Journal ArticleDOI
Linked networks for learning and expressing location-specific threat
Benjamin Suarez-Jimenez,James A. Bisby,Aidan J. Horner,John A. King,Daniel S. Pine,Neil Burgess +5 more
TL;DR: In this paper, the authors examined fMRI brain activity while participants navigated a virtual environment in which flowers appeared and were picked, and found that picking flowers in the danger zone (one-half of the environment) predicted an electric shock to the wrist (or bee sting) while flowers in a safe zone never predicted shock; and household objects served as controls for neutral spatial memory.
Book ChapterDOI
Disruptive behavior disorders: Taking an RDoC(ish) approach
TL;DR: This chapter outlines four functional processes and the behavioral implications of dysfunction within these processes and briefly considers how dysfunction in one might increase the risk for the development of rather different behavioral problems that have been previously associated with rather different disorders.
References
More filters
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Technical Note : \cal Q -Learning
Chris Watkins,Peter Dayan +1 more
TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Journal ArticleDOI
Conflict monitoring and cognitive control.
TL;DR: Two computational modeling studies are reported, serving to articulate the conflict monitoring hypothesis and examine its implications, including a feedback loop connecting conflict monitoring to cognitive control, and a number of important behavioral phenomena.
Journal ArticleDOI
Effects of noise letters upon the identification of a target letter in a nonsearch task
TL;DR: In this paper, a 1-sec tachistoscopic exposure, Ss responded with a right or left leverpress to a single target letter from the sets H and K or S and C. The target always appeared directly above the fixation cross.
Journal ArticleDOI
The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.
TL;DR: This paper presented a unified account of two neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a generic error-processing system associated with the anterior cingulate cortex.