Roles of the Lateral Habenula and Anterior Cingulate Cortex in Negative Outcome Monitoring and Behavioral Adjustment in Nonhuman Primates

Kawai, Takashi; Yamada, Hiroshi; Sato, Nobuya; Takada, Masahiko; Matsumoto, Masayuki

doi:10.1016/j.neuron.2015.09.030

Cited by 96 publications

(106 citation statements)

References 63 publications

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“…Instead, macaques with mPFC lesions exhibit a slight deficit in the ability to maintain the correct response following a reversal (Chudasama et al, 2013). This also fits with a recent neurophysiology study showing the neurons in the anterior cingulate cortex track rewarded and unrewarded choices over multiple trials during reversal learning (Kawai et al, 2015). Thus, the mPFC may have a more circumscribed role in reversal than the OFC, one that primarily manifests in tasks with a high demand on attention and performance monitoring; consistent with ideas about the involvement of the mPFC in stimulus detection, timing, and error detection (Laubach et al, 2015).…”

Section: Neural Substrates Of Reversalsupporting

confidence: 90%

The neural basis of reversal learning: An updated perspective

et al. 2017

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Reversal learning paradigms are among the most widely used tests of cognitive flexibility and have been used as assays, across species, for altered cognitive processes in a host of neuropsychiatric conditions. Based on recent studies in humans, non-human primates, and rodents, the notion that reversal learning tasks primarily measure response inhibition, has been revised. In this review, we describe how cognitive flexibility is measured by reversal learning and discuss new definitions of the construct validity of the task that are serving as an heuristic to guide future research in this field. We also provide an update on the available evidence implicating certain cortical and subcortical brain regions in the mediation of reversal learning, and an overview of the principle neurotransmitter systems involved.

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Section: Neural Substrates Of Reversalsupporting

confidence: 90%

The neural basis of reversal learning: An updated perspective

et al. 2017

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“…Although we have cast proactive control as one of several functions of ACC (Kawai et al, 2015), we do not claim that proactive control is solely driven by ACC, as indeed there is evidence that proactive control can be exerted by a network of other regions (Marini et al, 2016;MacDonald, Cohen, Stenger, & Carter, 2000). Perhaps the closest computational model to the PRO-control model is a recent model of dual control processes (Ziegler et al, 2014).…”

Section: Proactive Versus Reactive Mechanisms Of Controlmentioning

confidence: 86%

“…The PRO-control model provides a new degree of mechanistic clarity to the notions of proactive and reactive control signals, which have been explored both theoretically (Aron, 2011;Braver et al, 2007;De Pisapia & Braver, 2006) and empirically (Marini, Demeter, Roberts, Chelazzi, & Woldorff, 2016;Kawai, Yamada, Sato, Takada, & Matsumoto, 2015). Although we have cast proactive control as one of several functions of ACC (Kawai et al, 2015), we do not claim that proactive control is solely driven by ACC, as indeed there is evidence that proactive control can be exerted by a network of other regions (Marini et al, 2016;MacDonald, Cohen, Stenger, & Carter, 2000).…”

Section: Proactive Versus Reactive Mechanisms Of Controlmentioning

confidence: 90%

Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making

Brown

Alexander

2017

Journal of Cognitive Neuroscience

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Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales. It then uses those cost signals as a basis to bias decisions to minimize losses while maximizing gains. The model simulates both proactive and reactive control signals and accounts for a variety of empirical findings regarding value-based decision-making.

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“…The win-stay and lose-shift results in particular suggest that the LHb is critical for monitoring the current behavior on a trial by trial basis to inform future decisions. In monkeys, neurons recorded from the LHb in a saccade based version of probabilistic reversal learning showed short latency responses to outcomes that were not modulated by the number of consecutive reward experiences (either positive or negative) that the monkey had experienced (Kawai et al 2015). These neural results support the hypothesis that the LHb is sensitive to trial by trial changes in outcome expectations, an idea that is consistent with those found from both reversal learning and subjective decision making inactivation studies outlined above.…”

Section: Lhb Contributions To Behavioral Flexibilitymentioning

confidence: 99%

“…Both outcomes and the cues that predict them are signaled in the LHb and seem to represent valence on a trial by trial basis (Kawai et al 2015). Other studies have suggested that the LHb monitors specific behaviors in complex environments to guide choices (Stopper and Floresco 2014; Baker et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Control of behavioral flexibility by the lateral habenula

Baker

Mizumori

2017

Pharmacology Biochemistry and Behavior

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The ability to rapidly switch behaviors in dynamic environments is fundamental to survival across species. Recognizing when an ongoing behavioral strategy should be replaced by an alternative one requires the integration of a diverse number of cues both internal and external to the organism including hunger, stress, or the presence of reward predictive cues. Increasingly sophisticated behavioral paradigms coupled with state of the art electrophysiological and pharmacological approaches have delineated a brain circuit involved in behavioral flexibility. However, how diverse contextual cues are integrated to influence strategy selection on a trial by trial basis remains largely unknown. One promising candidate for integration of internal and external cues to determine whether an ongoing behavioral strategy is appropriate is the lateral habenula (LHb). The LHb receives input from many brain areas that signal both internal and external environmental contexts and in turn projects to areas involved in behavioral monitoring and plasticity. This review examines how these connections, combined with recent pharmacological and electrophysiological results reveal a critical role for the LHb in behavioral flexibility in dynamic environments. This proposed role extends the known contributions of the LHb to motivated behaviors and suggests that the fundamental role of the LHb in these behaviors goes beyond signaling rewards and punishments to dopaminergic systems.

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Roles of the Lateral Habenula and Anterior Cingulate Cortex in Negative Outcome Monitoring and Behavioral Adjustment in Nonhuman Primates

Cited by 96 publications

References 63 publications

The neural basis of reversal learning: An updated perspective

The neural basis of reversal learning: An updated perspective

Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making

Control of behavioral flexibility by the lateral habenula

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