The Decision Value Computations in the vmPFC and Striatum Use a Relative Value Code That is Guided by Visual Attention

Lim, Seung-Lark; O’Doherty, John P.; Rangel, Antonio

doi:10.1523/jneurosci.1246-11.2011

Cited by 306 publications

(329 citation statements)

References 41 publications

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“…Human vmPFC activity has been linked to subjective values of objects and choices (20)(21)(22). Positive correlation between activity and the values of chosen options and negative correlations with values of rejected options suggests a role in decision making (21) or attentional selection (23). However, there is also evidence the vmPFC tracks values of items even in the absence of any decision or when watching somebody else choosing (24,25).…”

Section: Resultsmentioning

confidence: 99%

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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.

351

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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Section: Resultsmentioning

confidence: 99%

“…A region near area 14m has been linked to decisions or attentional selection of choices (21,23,29,30). In comparison with the ACC and OFC it was, in both species, distinguished by strong positive coupling with hypothalamus, ventral striatum, and amygdala (75).…”

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.

351

388

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“…There is an abundance of evidence to support the claim that the perceptual process could be implemented in visually responsive neurons of the FEF, the LIP, and/ or the SC (24,28,30,(65)(66)(67). Similarly, many recent studies have indicated that the economic decision process takes place in various regions of the frontal and parietal cortices (68)(69)(70)(71), including the orbitofrontal cortex (OFC) (72)(73)(74), the ventro-medial prefrontal cortex (vmPFC) (75,76), and the amygdala (77). Connections between putative perceptual decision-making areas (FEF, LIP, and/or SC) and putative economic decision-making areas (OFC, vmPFC, and/or amygdala) abound, creating the possibility that value information is shared directly between these processes.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

Towal¹,

Mormann

Koch

2013

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

222

245

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Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a driftdiffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions.search | multiple targets | eye tracking | preference | attention O ne important goal of neuroscience and economics is to understand the computational mechanisms that underlie decision making between multiple alternatives. Interestingly, this goal has proceeded along two seemingly parallel paths that consider either perceptual decision making, namely decisions about perceptual properties of alternatives, or economic decision making, which considers the value of alternatives (1). Although these types of decisions can be constructed to be mutually exclusive in the laboratory, in more natural contexts, decisions nearly always involve perceptual decisions about how to sample information and value-based decisions about which alternatives are more valuable. Importantly, however, very little attention has been paid to how perceptual and economic decision processes may interact. One recent paper reported a visual saliency bias where, independent of consumer preferences, visually salient options are more likely to be chosen than less salient alternatives. It is not clear, however, which mechanism gives rise to this effect or how perceptual processes interact with economic choices (2).Choices and reaction times during perceptual decision making have been accurately modeled by stochastic accumulator models such as the drift-diffusion model (DDM) (3...

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“…At the neural level, the relative values among different options needed to accumulate evidence is reflected by activity in the VMPFC (Hare et al, 2011;Lim et al, 2011) and striatum (Jocham et al, 2011). Moreover, scalp EEG, intracranial recordings, and brain stimulation evidence indicates that the dorsal mediofrontal cortex communicates with the STN to modulate the decision threshold, particularly under conditions of choice conflict (Frank et al, 2007b;Cavanagh et al, 2011;Zaghloul et al, 2012;Green et al, 2013;Zavala et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning

et al. 2015

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The Decision Value Computations in the vmPFC and Striatum Use a Relative Value Code That is Guided by Visual Attention

Cited by 306 publications

References 41 publications

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

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

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning

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