An Agent Independent Axis for Executed and Modeled Choice in Medial Prefrontal Cortex

Nicolle, Antoinette; Klein-Flügge, Miriam C.; Hunt, Laurence T.; Vlaev, Ivo; Dolan, Raymond J.; Behrens, Timothy E.J.

doi:10.1016/j.neuron.2013.07.030

Cited by 45 publications

(73 citation statements)

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“…This idea does not necessarily contradict the idea that self-relevance is a major factor distinguishing VMPFC and DMPFC (41) because selfrelevant information is often most significant and motivating (19,42). Our results suggest that the VMPFC is tightly associated with subjective valuation regardless of the choice's beneficiary (30), whereas the DMPFC is involved in more general other-specific processing commonly required for other-regarding choices invariant across individuals (22-24, 40, 43, 44).…”

Section: Discussioncontrasting

confidence: 42%

“…Specifically, we obtained a sagittal view of the statistical parametric map from the aforementioned parametric modulation analysis with a lenient threshold of P < 0.05 uncorrected and then selected five equally spaced coordinates spanning the ventral-to-dorsal extent of the MPFC (Fig. 3A), similar to a previous approach (30). The parameter estimates of the neural activation associated with chosen value at the time of stimulus presentation were extracted from the ROIs for each individual.…”

Section: Functional Magnetic Resonance Imaging Resultsmentioning

confidence: 99%

“…Linear contrasts of regression coefficients for the parametric modulators of value and for RPE were computed and subjected to a random-effects group-level analysis using one-way ANOVA with condition (i.e., SELF, BOTH, and OTHER) as a repeated-measures factor. We quantified spatial gradients in self-and other-regarding value representations within the MPFC by extracting parameter estimates from five anatomical ROIs (spheres with a 4-mm radius) along the midline axis from the VMPFC to the DMPFC within the activation cluster correlating with the value parameters (30). For each ROI, we extracted the value parameter estimates for each condition, and then entered them into a repeated-measures one-way ANOVA.…”

Section: Methodsmentioning

confidence: 99%

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Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality

Sul

Tobler

Hein

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

127

112

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Despite the importance of valuing another person's welfare for prosocial behavior, currently we have only a limited understanding of how these values are represented in the brain and, more importantly, how they give rise to individual variability in prosociality. In the present study, participants underwent functional magnetic resonance imaging while performing a prosocial learning task in which they could choose to benefit themselves and/or another person. Choice behavior indicated that participants valued the welfare of another person, although less so than they valued their own welfare. Neural data revealed a spatial gradient in activity within the medial prefrontal cortex (MPFC), such that ventral parts predominantly represented self-regarding values and dorsal parts predominantly represented other-regarding values. Importantly, compared with selfish individuals, prosocial individuals showed a more gradual transition from selfregarding to other-regarding value signals in the MPFC and stronger MPFC-striatum coupling when they made choices for another person rather than for themselves. The present study provides evidence of neural markers reflecting individual differences in human prosociality.R anging from small acts of kindness in daily life to self-sacrificing altruism under life-threatening situations, we often observe large individual differences in how humans value another person's welfare. This differential valuation process seems to be the key to understanding various human prosocial behaviors, which are fundamental to the sustainability of human society (1). The underlying neural mechanisms and their relationship to individual differences in prosociality remain unclear, however.Perhaps the most powerful way of assessing how an outcome is valued is to use an instrumental learning paradigm that examines whether the occurrence of a response increases when it is followed by that outcome (2). The mechanisms underlying this type of learning have been described more formally with a computational model, known as the advantage learning model (3-5), which has been used successfully to reveal the neuroanatomical substrates of subjective valuation (3,4,6). Previous research has further refined the neurobiological model of reinforcement learning by emphasizing the specific roles played by the medial frontal cortex and the striatum; the medial frontal cortex computes the value of the chosen action, whereas the striatum processes reward prediction errors during reinforcement learning (4, 6-10).Unlike our current understanding of the valuation process for self-regarding choices (3, 6-12), it is much less clear whether learning also can be driven by other-regarding values, and whether this other-regarding valuation relies on the same mechanisms of reinforcement learning as those used for self. Moreover, despite the rapidly accumulating research on reward processing in social domains (13-19), the question remains of how neural representation of self-regarding vs. other-regarding values is related to individual differen...

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Section: Discussioncontrasting

confidence: 42%

Section: Functional Magnetic Resonance Imaging Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality

Sul

Tobler

Hein

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

127

112

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“…We used two SDs below the mean of this distribution of summed absolute differences as a cut-off to look for "significantly" good human to monkey matches. (21), (B) more abstract reward-guided decision making (28), (C) cost-benefit valuation (27), (D) imagining the reward outcomes of others (40), and (E) self-valuation and depression (42), could all be linked to macaque brain regions (Right) via similarities in their coupling patterns (Center: blue, macaque; red, human). However, a human brain area (D) associated with reward outcome imagination did not correspond in a simple way with any area in the macaque.…”

Section: Resultsmentioning

confidence: 99%

“…A dorsal medial frontal area in humans, area 9, was linked to decision making when the outcome had to be imagined or modeled in some way (30,40), and its activity coupling pattern resembled area 9 (79) in the monkey medial frontal cortex. A slightly more ventral area, d32, resembled the region active when decisions have to be made about whether rewards are worth the cost of foraging (10).…”

Section: Discussionmentioning

confidence: 99%

Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex

Neubert

Mars

Sallet

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

359

388

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Reward-guided decision-making depends on a network of brain regions. Among these are the orbitofrontal and the anterior cingulate cortex. However, it is difficult to ascertain if these areas constitute anatomical and functional unities, and how these areas correspond between monkeys and humans. To address these questions we looked at connectivity profiles of these areas using resting-state functional MRI in 38 humans and 25 macaque monkeys. We sought brain regions in the macaque that resembled 10 human areas identified with decision making and brain regions in the human that resembled six macaque areas identified with decision making. We also used diffusion-weighted MRI to delineate key human orbital and medial frontal brain regions. We identified 21 different regions, many of which could be linked to particular aspects of reward-guided learning, valuation, and decision making, and in many cases we identified areas in the macaque with similar coupling profiles.orbitofrontal cortex | anterior cingulate cortex | decision making | resting state functional connectivity | comparative anatomy A s humans we make decisions by taking into account different types of information, weighing our options carefully, and eventually coming to a conclusion. We then learn from witnessing the outcome of our decisions. Human functional MRI (fMRI) has had a major impact on elucidating the neural networks mediating decision making and learning, but key insights can only be obtained in neural recording, stimulation, and focal lesion studies conducted in animal models, such as the macaque. Combining insights from human fMRI and animal studies is, however, not straightforward because there is uncertainty about basic issues, such as anatomical and functional correspondences between species (1). For example, although there are many reports of decision value-related activity in the human ventromedial prefrontal cortex (vmPFC) (2, 3), it is unclear whether they can be related to reports of reward-related activity either on the ventromedial surface of the frontal lobe (4, 5), in the adjacent medial orbitofrontal sulcus (6), or indeed to any macaque brain area. It is claimed that some areas implicated in rewardguided decision making and learning, such as parts of anterior cingulate cortex (ACC), are not found in macaques (7), but such theories have never been formally tested.In addition, there is uncertainty about the basic constituent components of decision-making and learning circuits. To return to the example of the vmPFC, although this region is often contrasted with similarly large subdivisions of the frontal cortex, such as the lateral orbitofrontal cortex (lOFC) and ACC (8), it is unclear whether, and if so how, it should be decomposed into further subdivisions. Moreover, there are sometimes fundamental disagreements about how brain areas contribute to decision making and learning. For example, it has been claimed both that the ACC does (9-11) and does not (12) contribute to reward-based decision making and that it is concerned with dist...

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Common and distinct neural correlates of self‐serving and prosocial dishonesty

Pornpattananangkul

Zhen

2018

Human Brain Mapping

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People often anticipate certain benefits when making dishonest decisions. In this article, we aim to dissociate the neural-cognitive processes of (1) dishonest decisions that focus on overall benefits of being dishonest (regardless of whether the benefits are self-serving or prosocial) from (2) those that distinguish between self-serving and prosocial benefits. Thirty-one participants had the opportunity to maximize their monetary benefits by voluntarily making dishonest decisions while undergoing functional magnetic resonance imaging (fMRI). In each trial, the monetary benefit of being dishonest was either self-serving or prosocial. Behaviorally, we found dissociable patterns of dishonest decisions: some participants were dishonest for overall benefits, while others were primarily dishonest for self-serving (compared with prosocial) benefits. When provided an opportunity to be dishonest for either self-serving or prosocial benefits, participants with a stronger overall tendency to be dishonest had stronger vmPFC activity, as well as stronger functional connectivity between the vmPFC and dlPFC. Furthermore, vmPFC activity was associated with decisions to be dishonest both when the benefits of being dishonest were self-serving and prosocial. Conversely, high self-serving-biased participants had stronger striatum activity and stronger functional connectivity between the striatum and middle-mPFC when they had a chance to be dishonest for self-serving (compared with prosocial) benefits. Altogether, we showed that activity in (and functional connectivity between) regions in the valuation (e.g., vmPFC and Str) and executive control (e.g., dlPFC and mmPFC) systems play a key role in registering the social-related goal of dishonest decisions.

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An Agent Independent Axis for Executed and Modeled Choice in Medial Prefrontal Cortex

Cited by 45 publications

References 0 publications

Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality

Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality

Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex

Common and distinct neural correlates of self‐serving and prosocial dishonesty

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