Influences of Rewarding and Aversive Outcomes on Activity in Macaque Lateral Prefrontal Cortex

Kobayashi, Shunsuke; Nomoto, Kensaku; Watanabe, Masataka; Hikosaka, Okihide; Schultz, Wolfram; Sakagami, Masamichi

doi:10.1016/j.neuron.2006.08.031

Cited by 98 publications

(85 citation statements)

References 39 publications

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“…This type of neuron may be more concerned with differentiating competitive from noncompetitive situations than with differentiating between positive and negative outcomes. Similar valenceindependent, outcome-related neuronal activities were reported previously in LPFC (Kobayashi et al, 2006). Also, previous studies reported that LPFC neurons show contextdependent activities (White and Wise, 1999;Wallis et al, 2001;Watanabe et al, 2002;Amemori and Sawaguchi, 2006a,b;IchiharaTakeda and Funahashi, 2008;Kennerley and Wallis, 2009), which are related to coding cognitive and/or motivational context information.…”

Section: Discussionsupporting

confidence: 83%

Prefrontal Neurons Represent Winning and Losing during Competitive Video Shooting Games between Monkeys

Hosokawa

Watanabe

2012

J. Neurosci.

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Humans and animals must work to support their survival and reproductive needs. Because resources are limited in the natural environment, competition is inevitable, and competing successfully is vitally important. However, the neuronal mechanisms of competitive behavior are poorly studied. We examined whether neurons in the lateral prefrontal cortex (LPFC) showed response sensitivity related to a competitive game. In this study, monkeys played a video shooting game, either competing with another monkey or the computer, or playing alone without a rival. Monkeys performed more quickly and more accurately in the competitive than in the noncompetitive games, indicating that they were more motivated in the competitive than in the noncompetitive games. LPFC neurons showed differential activity between the competitive and noncompetitive games showing winning-and losing-related activity. Furthermore, activities of prefrontal neurons differed depending on whether the competition was between monkeys or between the monkey and the computer. These results indicate that LPFC neurons may play an important role in monitoring the outcome of competition and enabling animals to adapt their behavior to increase their chances of obtaining a reward in a socially interactive environment.

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Section: Discussionsupporting

confidence: 83%

Prefrontal Neurons Represent Winning and Losing during Competitive Video Shooting Games between Monkeys

Hosokawa

Watanabe

2012

J. Neurosci.

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“…In sum, the expected value of engaging different neuronal populations, dedicated to specific tasks, might be topographically represented in frontostriatal circuits. In primate prefrontal cortex or striatum, certain neuronal activities have been reported to express both expected reward and various task dimensions, such as target location (Watanabe, 1996;Leon and Shadlen, 1999;Kobayashi et al, 2006) or movement direction (Lauwereyns et al, 2002;Matsumoto et al, 2003;Samejima et al, 2005;Pasquereau et al, 2007). However, the topographical organization of neuronal populations encoding expected values, and their connection with the corresponding effectors, remain to be established.…”

Section: Discussionmentioning

confidence: 99%

Brain Hemispheres Selectively Track the Expected Value of Contralateral Options

Palminteri¹,

Boraud²,

Lafargue³

et al. 2009

J. Neurosci.

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A main focus in economics is on binary choice situations, in which human agents have to choose between two alternative options. The classical view is that decision making consists of valuating each option, comparing the two expected values, and selecting the higher one. Some neural correlates of option values have been described in animals, but little is known about how they are represented in the human brain: are they integrated into a single center or distributed over different areas? To address this issue, we examined whether the expected values of two options, which were cued by visual symbols and chosen with either the left or right hand, could be distinguished using functional magnetic resonance imaging. The two options were linked to monetary rewards through probabilistic contingencies that subjects had to learn so as to maximize payoff. Learning curves were fitted with a standard computational model that updates, on a trial-by-trial basis, the value of the chosen option in proportion to a reward prediction error. Results show that during learning, left and right option values were specifically expressed in the contralateral ventral prefrontal cortex, regardless of the upcoming choice. We therefore suggest that expected values are represented in a distributed manner that respects the topography of the brain systems elicited by the available options.

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“…For example, fMRI meta-analyses suggest that brain areas that code for both rewards and punishments are the exception rather than the norm (Bartra, McGuire, & Kable, 2013;Garrison, Erdeniz, & Done, 2013). Similarly, single cell studies show that neurons that fire in response to reward or punishment only are markedly more common than those that raise their firing in response to rewards and reduce it in response to punishments (Kobayashi et al, 2006). Furthermore, it has been argued that the dissociation of reward and punishment is neurochemically instantiated, with dopamine coding reward and serotonin coding punishment (Daw, Kakade, & Dayan, 2002).…”

Section: Introductionmentioning

confidence: 99%

Principal components analysis of reward prediction errors in a reinforcement learning task

Sambrook

Goslin

2016

NeuroImage

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Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330 ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found.

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Influences of Rewarding and Aversive Outcomes on Activity in Macaque Lateral Prefrontal Cortex

Cited by 98 publications

References 39 publications

Prefrontal Neurons Represent Winning and Losing during Competitive Video Shooting Games between Monkeys

Prefrontal Neurons Represent Winning and Losing during Competitive Video Shooting Games between Monkeys

Brain Hemispheres Selectively Track the Expected Value of Contralateral Options

Principal components analysis of reward prediction errors in a reinforcement learning task

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