Medial prefrontal cortex as an action-outcome predictor

By William H. Alexander, Joshua W. Brown. Nature Neuroscience, 2011

924 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0181338ece5c27471b204939acdf915517e2d16e

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In Cognitive Neuroscience

Citations in the corpus

Neurophysiology of performance monitoring and adaptive behavior. (2014, background): “More recently, a predicted response-outcome model of pMFC function has been suggested (3).”
A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, background): “ ict is resolved. The full computational role of dACC is far from resolved and likely to be more complex than con ict detection and control (see, e.g. Holroyd and Coles, 2002; Botvinick et al., 2004; Alexander and Brown, 2011). However, there remains substantial evidence that con ict and response-ou”
The expected value of control: An integrative theory of anterior cingulate cortex function (2013, positive): “…elevated dACC activity in response to surprising outcomes (Cavanagh et al., 2012; Landmann et al., 2007; Nee et al., 2011; Wessel et al., 2012) and, more generally, following unanticipated shifts in task contingencies (Alexander and Brown, 2011; Behrens et al., 2007; Bland and Schaefer, 2011).”
The role of prefrontal cortex in cognitive control and executive function (2021, methodology): “By contrast, the Predicted Response Outcome model [117] uses as its basis unsigned prediction errors of any type, whether appetitive or aversive outcomes, and whether negative (unexpected omission of expected outcome) or positive (unexpected occurrence of outcome) “surprise,” over multiple time-scal”
Emotion and the Prefrontal Cortex: An Integrative Review (2017, methodology): “(2004) Review/Meta-analysis aMCC Performance monitoring Alexander and Brown (2011) Model aMCC Action-outcome learning Ullsperger et al.”
Cognitive Control Predicts Use of Model-based Reinforcement Learning (2014, background): “…Barch, Carter, & Cohen, 2001), and neural data on either function suggest the involvement of nearby (or overlapping) prefrontal structures (Economides, Guitart-Masip, Kurth-Nelson, & Dolan, 2014; Holroyd & Yeung, 2012; Alexander & Brown, 2011; Rushworth, Noonan, Boorman, Walton, & Behrens, 2011).”
Cognitive Control (2012, neutral): “Yet , another relevant definition presented in [44, p. 1] is as follows: ‘‘Cognitive control at the neural level is seen as a result of evaluating the probable and actual outcomes of one’s actions.’’”
The feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization (2014, methodology): “Further evidence comes also from a functional model of the ACC by Alexander and Brown (2011), which successfully simulated the FRN deflection based on surprise-signals.”
The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers (2014, background): “Recent work has extended this idea, suggesting that the ACC may be responsive more broadly to uncertainty, surprise, or both (Alexander & Brown, 2011; Ide, Shenoy, Angela, & Chiang-shan, 2013; Rushworth & Behrens, 2008).”
Switching between internal and external modes: A multiscale learning principle (2017, background): “These differences between expectation and outcome can also be used to build models of the environment, thought to be used by the basal ganglia and medial prefrontal cortex to predict outcomes and select actions (Alexander & Brown, 2011; Daw, Gershman, Seymour, Dayan, & Dolan, 2011).”
Event-related potential studies of outcome processing and feedback-guided learning (2012, methodology): “Also, a recent computational model (Alexander and Brown, 2011) has been able to simulate the FRN under the assumption that the mPFC is activated by surprising events, regardless of valence.”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Finally, the anterior cingulate cortex is supposed to play integrative and modulatory roles across hierarchies, given its multidimensional sensitivity to errors and rewards and its linkage to the motivation of extended behaviours [100] and action outcome predictions [4].”
Error awareness and the error-related negativity: evaluating the first decade of evidence (2012, background): “, 2004), reflecting a learning signal from the dopaminergic midbrain (Holroyd and Coles, 2002), representing the likelihood of an error on a given trial (Brown and Braver, 2005), or signaling the unsigned reward prediction error, or “surprise” of a given response (Alexander and Brown, 2011; Hayden e”