The valuation of food is a fundamental component of our decision-making. Yet, little is known about how value signals for food and other rewards are constructed by the brain. Utilizing a food-based decision task in human participants, we found that subjective values can be predicted from beliefs about constituent nutritive attributes of food: protein, fat, carbohydrate and vitamin content. Multivariate analyses on fMRI data demonstrated that while food value is represented in patterns of neural activity in both medial and lateral parts of the orbitofrontal cortex (OFC), only the lateral OFC represents the elemental nutritive attributes. Effective connectivity analyses further indicate that information about the nutritive attributes represented in the lateral OFC is integrated within the medial OFC to compute an overall value. These findings provide a mechanistic account for the construction of food-value from its constituent nutrients.

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Little is known about how the brain computes the perceived aesthetic value of complex stimuli such as visual art. Here, we used computational methods in combination with functional neuroimaging to provide evidence that the aesthetic value of a visual stimulus is computed in a hierarchical manner via a weighted integration over both low and high level stimulus features contained in early and late visual cortex, extending into parietal and lateral prefrontal cortices. Feature representations in parietal and lateral prefrontal cortex may in turn be utilized to produce an overall aesthetic value in the medial prefrontal cortex. Such brain-wide computations are not only consistent with a feature-based mechanism for value construction, but also resemble computations performed by a deep convolutional neural network. Our findings thus shed light on the existence of a general neurocomputational mechanism for rapidly and flexibly producing value judgements across an array of complex novel stimuli and situations.

Humans, like many other animals, pre-empt danger by moving to locations that maximize their success at escaping future threats. We test the idea that spatial margin of safety (MOS) decisions, a form of pre-emptive avoidance, results in participants placing themselves closer to safer locations when facing more unpredictable threats. Using multivariate pattern analysis on fMRI data collected while subjects engaged in MOS decisions with varying attack location predictability, we show that while the hippocampus encodes MOS decisions across all types of threat, a vmPFC anterior-posterior gradient tracked threat predictability. The posterior vmPFC encoded for more unpredictable threat and showed functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal, possibly via predictable positive outcomes, while the posterior vmPFC drives prospective danger signals.Staying in close proximity to safety is a key antipredator behavior as it increases the likelihood of the organism's future escape success1. One metric used by behavioral ecologists to measure this safety behavior is called spatial margin of safety, where prey will adopt locations that prevent lethal predatory attack1-3 . In turn, this provides the prey with a safety net, while also reducing stress, energy consumption and promotes increased focus on other survival behaviors, such as foraging and copulation. Humans appear to use safety distance in similar ways. For example, when human subjects are placed close to a safety exit, measures of fear decrease and when under threat, and the sight of safety signals reduces fear and fear reinstatement5-7.Here , we test the idea that when subjects are pre-empting threats of varying attack location probabilities, subjects will vary their spatial margin of safety (MOS) decisions depending on predictability. We propose that MOS decisions involve prospective spatial planning, which involves estimating safety by calculating the predator's attack locations4 . Further, we examine how pre-emptive MOS decisions are instantiated in human defensive circuits9,10.In the natural world, prey encounter predators that attack with varying degrees of uncertainty.Uncertainty is often determined by the likelihood of attack and the distribution of distances at which the threat will attack. For example, uncertainty alerts the prey that information about the predator's impending attack location is unknown, thereby resulting in increased anxiety and movement towards safety5. Thus, pre-empting predation via close spatial MOS, safeguards against the unpredictable spatial and temporal movements of the predator6 . Consequently, the ability to predict a predator's attack location will in turn shape the prey's MOS calculations, whereby uncertain threats will result in low risk behaviors and smaller spatial radius from a refuge at the expense of forg...

No abstract

Pavlovian learning depends on multiple and parallel associations leading to distinct classes of conditioned responses that vary in their flexibility following changes in the value of an associated outcome. Here, we aimed to differentiate brain areas involved in learning and encoding associations that are sensitive to changes in the value of an outcome from those that are not sensitive to such changes. To address this question, we combined a Pavlovian learning task with outcome devaluation, eye-tracking and functional magnetic resonance imaging. We used computational modeling to identify brain regions involved in learning stimulus-reward associations and stimulus-stimulus associations, by testing for brain areas correlating with reward-prediction errors and state-prediction errors, respectively. We found that, contrary to theoretical predictions about reward prediction errors being exclusively model-free, voxels correlating with reward prediction errors in the ventral striatum and subgenual anterior cingulate cortex were sensitive to devaluation. On the other hand, brain areas correlating with state prediction errors were found to be devaluation insensitive. In a supplementary analysis, we distinguished brain regions encoding predictions about outcome taste identity from those involved in encoding predictions about its expected spatial location. A subset of regions involved in taste identity predictions were devaluation sensitive while those involved in encoding predictions about spatial location were devaluation insensitive. These findings provide insights into the role of multiple associative mechanisms in the brain in mediating Pavlovian conditioned behavior - illustrating how distinct neural pathways can in parallel produce both devaluation sensitive and devaluation insensitive behaviors.

Humans, like many other animals, pre-empt danger by moving to locations that maximize their success at escaping future threats. Here, we test the idea that spatial margin of safety (MOS) decisions, a form of prospective avoidance, result in participants placing themselves closer to safer locations when facing more unpredictable threats. Using multivariate pattern analysis on fMRI data collected while subjects engaged in MOS decisions with varying attack location predictability, we show that while the hippocampus encodes MOS decisions across all types of threat, a vmPFC anterior-posterior gradient tracked threat predictability. The posterior vmPFC encoded for more unpredictable threat and showed functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal, possibly via predictable positive outcomes, while the posterior vmPFC drives prospective danger signals.