Contributions of Orbitofrontal and Lateral Prefrontal Cortices to Economic Choice and the Good-to-Action Transformation

Cai, Xiaopan; Padoa-Schioppa, Camillo

doi:10.1016/j.neuron.2014.01.008

Cited by 132 publications

(177 citation statements)

References 51 publications

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“…Previous work (Padoa-Schioppa andAssad, 2006, 2008) had already ruled out several possible variables (e.g., other value, value difference, total value, etc.) and found that variables offer value, chosen value, and chosen juice explained the vast majority of taskrelated neural responses.…”

Section: Methodsmentioning

confidence: 99%

“…Future work shall examine this issue more directly. Importantly, neurons encoding the weighted choice could still provide the input to the good-toaction transformation (Cai and Padoa-Schioppa, 2014), because the activity of cells associated with the chosen juice, even if weighted by the probability, is still higher than the activity of cells associated with the other, nonchosen juice.…”

Section: Other Cell Groups and Open Questionsmentioning

confidence: 99%

“…We conducted a series of analyses to assess whether the encoding of different variables was categorical using the same approach as in a previous study (Padoa-Schioppa, 2013 In principle, each neuron could encode the same variable in different time windows. Alternatively, the same neuron could encode different variables in different time windows.…”

mentioning

confidence: 99%

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Integration of Multiple Determinants in the Neuronal Computation of Economic Values

Raghuraman¹,

Padoa-Schioppa

2014

J. Neurosci.

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Economic goods may vary on multiple dimensions (determinants).A central conjecture in decision neuroscience is that choices between goods are made by comparing subjective values computed through the integration of all relevant determinants. Previous work identified three groups of neurons in the orbitofrontal cortex (OFC) of monkeys engaged in economic choices: (1) offer value cells, which encode the value of individual offers; (2) chosen value cells, which encode the value of the chosen good; and (3) chosen juice cells, which encode the identity of the chosen good. In principle, these populations could be sufficient to generate a decision. Critically, previous work did not assess whether offer value cells (the putative input to the decision) indeed encode subjective values as opposed to physical properties of the goods, and/or whether offer value cells integrate multiple determinants. To address these issues, we recorded from the OFC while monkeys chose between risky outcomes. Confirming previous observations, three populations of neurons encoded the value of individual offers, the value of the chosen option, and the value-independent choice outcome. The activity of both offer value cells and chosen value cells encoded values defined by the integration of juice quantity and probability. Furthermore, both populations reflected the subjective risk attitude of the animals. We also found additional groups of neurons encoding the risk associated with a particular option, the risky nature of the chosen option, and whether the trial outcome was positive or negative. These results provide substantial support for the conjecture described above and for the involvement of OFC in good-based decisions.

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Section: Methodsmentioning

confidence: 99%

Section: Other Cell Groups and Open Questionsmentioning

confidence: 99%

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Integration of Multiple Determinants in the Neuronal Computation of Economic Values

Raghuraman¹,

Padoa-Schioppa

2014

J. Neurosci.

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“…Indeed, monkey orbitofrontal neurons code the chosen value and map it onto action activity in dorsolateral prefrontal cortex to initiate the action ("good-to-action transformation," Ref. 81). …”

Section: Object Value Versus Action Valuementioning

confidence: 99%

Neuronal Reward and Decision Signals: From Theories to Data

Schultz

2015

Physiological Reviews

921

765

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LSchultz W. Neuronal Reward and Decision Signals: From Theories to Data. Physiol Rev 95: 853-951, 2015. Published June 24, 2015 doi:10.1152/physrev.00023.2014.-Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

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“…Finally, neurons in parietal cortex, and possibly other areas involved in attention, signal the abstract value or importance of objects and individuals in the local environment, in order to shape the adaptive allocation of attention to others. Notably, current evidence suggests that social and non-social information remains segregated from perception through evaluation and may only be integrated during the process of making decisions and planning action ( [75]; but see [94]). These circuits provide the foundation upon which more complex social behaviours, like cooperation and alliance formation, are developed and elaborated.…”

Section: Mechanisms Mediating the Evaluation Of Social Partners' Valumentioning

confidence: 99%

Adaptations for social cognition in the primate brain

Platt

Seyfarth

Cheney

2016

Phil. Trans. R. Soc. B

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One contribution of 18 to a theme issue 'The evolution of cooperation based on direct fitness benefits'. Studies of the factors affecting reproductive success in group-living monkeys have traditionally focused on competitive traits, like the acquisition of high dominance rank. Recent research, however, indicates that the ability to form cooperative social bonds has an equally strong effect on fitness. Two implications follow. First, strong social bonds make individuals' fitness interdependent and the 'free-rider' problem disappears. Second, individuals must make adaptive choices that balance competition and cooperation-often with the same partners. The proximate mechanisms underlying these behaviours are only just beginning to be understood. Recent results from cognitive and systems neuroscience provide us some evidence that many social and nonsocial decisions are mediated ultimately by abstract, domain-general neural mechanisms. However, other populations of neurons in the orbitofrontal cortex, striatum, amygdala and parietal cortex specifically encode the type, importance and value of social information. Whether these specialized populations of neurons arise by selection or through developmental plasticity in response to the challenges of social life remains unknown. Many brain areas are homologous and show similar patterns of activity in human and nonhuman primates. In both groups, cortical activity is modulated by hormones like oxytocin and by the action of certain genes that may affect individual differences in behaviour. Taken together, results suggest that differences in cooperation between the two groups are a matter of degree rather than constituting a fundamental, qualitative distinction.

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Contributions of Orbitofrontal and Lateral Prefrontal Cortices to Economic Choice and the Good-to-Action Transformation

Cited by 132 publications

References 51 publications

Integration of Multiple Determinants in the Neuronal Computation of Economic Values

Integration of Multiple Determinants in the Neuronal Computation of Economic Values

Neuronal Reward and Decision Signals: From Theories to Data

Adaptations for social cognition in the primate brain

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