Appetitive and aversive goal values are encoded in the medial orbitofrontal cortex at the time of decision making

Serotonin selectively modulates reward value in human decision-making

Establishing a function for the neuromodulator serotonin in human decision-making has proved remarkably difficult because if its complex role in reward and punishment processing. In a novel choice task where actions led concurrently and independently to the stochastic delivery of both money and pain, we studied the impact of decreased brain serotonin induced by acute dietary tryptophan depletion. Depletion selectively impaired both behavioral and neural representations of reward outcome value, and hence the effective exchange rate by which rewards and punishments were compared. This effect was computationally and anatomically distinct from a separate effect on increasing outcome-independent choice perseveration. Our results provide evidence for a surprising role for serotonin in reward processing, while illustrating its complex and multifarious effects.

What are the Odds? The Neural Correlates of Active Choice during Gambling

Gambling is a widespread recreational activity and requires pitting the values of potential wins and losses against their probability of occurrence. Neuropsychological research showed that betting behavior on laboratory gambling tasks is highly sensitive to focal lesions to the ventromedial prefrontal cortex (vmPFC) and insula. In the current study, we assessed the neural basis of betting choices in healthy participants, using functional magnetic resonance imaging of the Roulette Betting Task. In half of the trials, participants actively chose their bets; in the other half, the computer dictated the bet size. Our results highlight the impact of volitional choice upon gambling-related brain activity: Neural activity in a distributed network - including key structures of the reward circuitry (midbrain, striatum) - was higher during active compared to computer-dictated bet selection. In line with neuropsychological data, the anterior insula and vmPFC were more activated during self-directed bet selection, and responses in these areas were differentially modulated by the odds of winning in the two choice conditions. In addition, responses in the vmPFC and ventral striatum were modulated by the bet size. Convergent with electrophysiological research in macaques, our results further implicate the inferior parietal cortex (IPC) in the processing of the likelihood of potential outcomes: Neural responses in the IPC bilaterally reflected the probability of winning during bet selection. Moreover, the IPC was particularly sensitive to the odds of winning in the active-choice condition, when the processing of this information was required to guide bet selection. Our results indicate an important role of the IPC in human decision-making under risk and help to integrate neuropsychological data of risk-taking following vmPFC and insula damage with models of choice derived from human neuroimaging and monkey electrophysiology.

Dopamine and performance in a reinforcement learning task: evidence from Parkinson's disease

The role dopamine plays in decision-making has important theoretical, empirical and clinical implications. Here, we examined its precise contribution by exploiting the lesion deficit model afforded by Parkinson’s disease. We studied patients in a two-stage reinforcement learning task, while they were ON and OFF dopamine replacement medication. Contrary to expectation, we found that dopaminergic drug state (ON or OFF) did not impact learning. Instead, the critical factor was drug state during the performance phase, with patients ON medication choosing correctly significantly more frequently than those OFF medication. This effect was independent of drug state during initial learning and appears to reflect a facilitation of generalization for learnt information. This inference is bolstered by our observation that neural activity in nucleus accumbens and ventromedial prefrontal cortex, measured during simultaneously acquired functional magnetic resonance imaging, represented learnt stimulus values during performance. This effect was expressed solely during the ON state with activity in these regions correlating with better performance. Our data indicate that dopamine modulation of nucleus accumbens and ventromedial prefrontal cortex exerts a specific effect on choice behaviour distinct from pure learning. The findings are in keeping with the substantial other evidence that certain aspects of learning are unaffected by dopamine lesions or depletion, and that dopamine plays a key role in performance that may be distinct from its role in learning.

Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence

CONTEXT

Negative symptoms are a core feature of schizophrenia, but their pathogenesis remains unclear. Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence.

OBJECTIVE

To test a reinforcement learning account suggesting that negative symptoms result from a failure in the representation of the expected value of rewards coupled with preserved loss-avoidance learning.

DESIGN

Participants performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in reward or in loss avoidance. Following training, participants indicated their valuation of the stimuli in a transfer test phase. Computational modeling was used to distinguish between alternative accounts of the data.

SETTING

A tertiary care research outpatient clinic.

PATIENTS

In total, 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated in the study. Patients were divided into a high-negative symptom group and a low-negative symptom group.

MAIN OUTCOME MEASURES

The number of choices leading to reward or loss avoidance, as well as performance in the transfer test phase. Quantitative fits from 3 different models were examined.

RESULTS

Patients in the high-negative symptom group demonstrated impaired learning from rewards but intact loss-avoidance learning and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer test phase. Model fits revealed that patients in the high-negative symptom group were better characterized by an "actor-critic" model, learning stimulus-response associations, whereas control subjects and patients in the low-negative symptom group incorporated expected value of their actions ("Q learning") into the selection process.

CONCLUSIONS

Negative symptoms in schizophrenia are associated with a specific reinforcement learning abnormality: patients with high-negative symptoms do not represent the expected value of rewards when making decisions but learn to avoid punishments through the use of prediction errors. This computational framework offers the potential to understand negative symptoms at a mechanistic level.

Empathic choice involves vmPFC value signals that are modulated by social processing implemented in IPL

Empathic decision-making involves making choices on behalf of others in order to maximize their well-being. Examples include the choices that parents make for their children, as well as the decisions of a politician trying to make good choices on behalf of his constituency. We investigated the neurobiological and computational basis of empathic choice using a human fMRI task in which subjects purchased DVDs for themselves with their own money, or DVDs for others with the other's money. We found that empathic choices engage the same regions of ventromedial prefrontal cortex that are known to compute stimulus values, and that these value signals were modulated by activity from a region of inferior parietal lobule (IPL) known to play a critical role in social processes such as empathy. We also found that the stimulus value signals used to make empathic choices were computed using a mixture of self-simulation and other-simulation processes, and that activity in IPL encoded a variable measuring the distance between the other's and self preferences, which provides a hint for how the mixture of self- and other-simulation might be implemented.

Does the orbitofrontal cortex signal value?

The orbitofrontal cortex (OFC) has long been implicated in associative learning. Early work by Mishkin and Rolls showed that the OFC was critical for rapid changes in learned behavior, a role that was reflected in the encoding of associative information by orbitofrontal neurons. Over the years, new data-particularly neurophysiological data-have increasingly emphasized the OFC in signaling actual value. These signals have been reported to vary according to internal preferences and judgments and to even be completely independent of the sensory qualities of predictive cues, the actual rewards, and the responses required to obtain them. At the same time, increasingly sophisticated behavioral studies have shown that the OFC is often unnecessary for simple value-based behavior and instead seems critical when information about specific outcomes must be used to guide behavior and learning. Here, we review these data and suggest a theory that potentially reconciles these two ideas, value versus specific outcomes, and bodies of work on the OFC.

The orbitofrontal cortex and the computation of subjective value: consolidated concepts and new perspectives

Remarkable progress has been made in recent years toward understanding the functions of the orbitofrontal cortex (OFC). The finding that neurons in this area encode the subjective value monkeys assign to different goods while making choices has been confirmed and extended by numerous studies using both primate neurophysiology and human imaging. Moreover, new lesion studies demonstrated that subjective values computed in the OFC are causally and specifically related to choice behavior. Importantly, values in the OFC are attached to goods, not to actions or to spatial locations. Furthermore, subjective values appear to be computed in this area even if the situation does not require a choice. In the light of this growing body of work, we propose that the primary function of the OFC is the computation of good identities and subjective values in an abstract representation. In this view, OFC neurons compute the subjective value of a good whenever that good is behaviorally relevant.

Contributions of the ventromedial prefrontal cortex to goal-directed action selection

In this article, it will be argued that one of the key contributions of the ventromedial prefrontal cortex (vmPFC) to goal-directed action selection lies both in retrieving the value of goals that are the putative outcomes of the decision process and in establishing a relative preference ranking for these goals by taking into account the value of each of the different goals under consideration in a given decision-making scenario. These goal-value signals are then suggested to be used as an input into the on-line computation of action values mediated by brain regions outside of the vmPFC, such as parts of the parietal cortex, supplementary motor cortex, and dorsal striatum. Collectively, these areas can be considered to be constituent elements of a multistage decision process whereby the values of different goals must first be represented and ranked before the value of different courses of action available for the pursuit of those goals can be computed.

A neuropsychological approach to understanding risk-taking for potential gains and losses

Affective neuroscience has helped guide research and theory development in judgment and decision-making by revealing the role of emotional processes in choice behavior, especially when risk is involved. Evidence is emerging that qualitatively and quantitatively different processes may be involved in risky decision-making for gains and losses. We start by reviewing behavioral work by Kahneman and Tversky (1979) and others, which shows that risk-taking differs for potential gains and potential losses. We then turn to the literature in decision neuroscience to support the gain versus loss distinction. Relying in part on data from a new task that separates risky decision-making for gains and losses, we test a neural model that assigns unique mechanisms for risky decision-making involving potential losses. Included are studies using patients with lesions to brain areas specified as important in the model and studies with healthy individuals whose brains are scanned to reveal activation in these and other areas during risky decision-making. In some cases, there is evidence that gains and losses are processed in different regions of the brain, while in other cases the same region appears to process risk in a different manner for gains and losses. At a more general level, we provide strong support for the notion that decisions involving risk-taking for gains and decisions involving risk-taking for losses represent different psychological processes. At a deeper level, we present mounting evidence that different neural structures play different roles in guiding risky choices in these different domains. Some structures are differentially activated by risky gains and risky losses while others respond uniquely in one domain or the other. Taken together, these studies support a clear functional dissociation between risk-taking for gains and risk-taking for losses, and further dissociation at the neural level.

Correlation between Ventromedial Prefrontal Cortex Activation to Food Aromas and Cue-driven Eating: An fMRI Study

Food aromas are signals associated with both food's availability and pleasure. Previous research from this laboratory has shown that food aromas under fasting conditions evoke robust activation of medial prefrontal brain regions thought to reflect reward value (Bragulat, et al. 2010). In the current study, eighteen women (eleven normal-weight and seven obese) underwent a two-day imaging study (one after being fed, one while fasting). All were imaged on a 3T Siemens Trio-Tim scanner while sniffing two food (F; pasta and beef) odors, one non-food (NF; Douglas fir) odor, and an odorless control (CO). Prior to imaging, participants rated hunger and perceived odor qualities, and completed the Dutch Eating Behavior Questionnaire (DEBQ) to assess "Externality" (the extent to which eating is driven by external food cues). Across all participants, both food and non-food odors (compared to CO) elicited large blood oxygenation level dependent (BOLD) responses in olfactory and reward-related areas, including the medial prefrontal and anterior cingulate cortex, bilateral orbitofrontal cortex, and bilateral piriform cortex, amygdala, and hippocampus. However, food odors produced greater activation of medial prefrontal cortex, left lateral orbitofrontal cortex and inferior insula than non-food odors. Moreover, there was a significant correlation between the [F > CO] BOLD response in ventromedial prefrontal cortex and "Externality" sub-scale scores of the DEBQ, but only under the fed condition; no such correlation was present with the [NF > CO] response. This suggests that in those with high Externality, ventromedial prefrontal cortex may inappropriately valuate external food cues in the absence of internal hunger.

Comparing apples and oranges: using reward-specific and reward-general subjective value representation in the brain

The ability of human subjects to choose between disparate kinds of rewards suggests that the neural circuits for valuing different reward types must converge. Economic theory suggests that these convergence points represent the subjective values (SVs) of different reward types on a common scale for comparison. To examine these hypotheses and to map the neural circuits for reward valuation we had food and water-deprived subjects make risky choices for money, food, and water both in and out of a brain scanner. We found that risk preferences across reward types were highly correlated; the level of risk aversion an individual showed when choosing among monetary lotteries predicted their risk aversion toward food and water. We also found that partially distinct neural networks represent the SVs of monetary and food rewards and that these distinct networks showed specific convergence points. The hypothalamic region mainly represented the SV for food, and the posterior cingulate cortex mainly represented the SV for money. In both the ventromedial prefrontal cortex (vmPFC) and striatum there was a common area representing the SV of both reward types, but only the vmPFC significantly represented the SVs of money and food on a common scale appropriate for choice in our data set. A correlation analysis demonstrated interactions across money and food valuation areas and the common areas in the vmPFC and striatum. This may suggest that partially distinct valuation networks for different reward types converge on a unified valuation network, which enables a direct comparison between different reward types and hence guides valuation and choice.

Beyond valence and magnitude: a flexible evaluative coding system in the brain

Outcome evaluation is a cognitive process that plays an important role in our daily lives. In most paradigms utilized in the field of experimental psychology, outcome valence and outcome magnitude are the two major features investigated. The classical "independent coding model" suggests that outcome valence and outcome magnitude are evaluated by separate neural mechanisms that may be mapped onto discrete event-related potential (ERP) components: feedback-related negativity (FRN) and the P3, respectively. To examine this model, we presented outcome valence and magnitude sequentially rather than simultaneously. The results reveal that when only outcome valence or magnitude is known, both the FRN and the P3 encode that outcome feature; when both aspects of outcome are known, the cognitive functions of the two components dissociate: the FRN responds to the information available in the current context, while the P3 pattern depends on outcome presentation sequence. The current study indicates that the human evaluative system, indexed in part by the FRN and the P3, is more flexible than previous theories suggested.

The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention

There is a growing consensus in behavioral neuroscience that the brain makes simple choices by first assigning a value to the options under consideration and then comparing them. Two important open questions are whether the brain encodes absolute or relative value signals, and what role attention might play in these computations. We investigated these questions using a human fMRI experiment with a binary choice task in which the fixations to both stimuli were exogenously manipulated to control for the role of visual attention in the valuation computation. We found that the ventromedial prefrontal cortex and the ventral striatum encoded fixation-dependent relative value signals: activity in these areas correlated with the difference in value between the attended and the unattended items. These attention-modulated relative value signals might serve as the input of a comparator system that is used to make a choice.

Cross-species studies of orbitofrontal cortex and value-based decision-making

Recent work has emphasized the role that orbitofrontal cortex (OFC) has in value-based decision-making. However, it is also clear that a number of discrepancies have arisen when comparing the findings from animal models to those from humans. Here, we examine several possibilities that might explain these discrepancies, including anatomical difference between species, the behavioral tasks used to probe decision-making and the methodologies used to assess neural function. Understanding how these differences affect the interpretation of experimental results will help us to better integrate future results from animal models. This will enable us to fully realize the benefits of using multiple approaches to understand OFC function.

[Food behaviour and obesity: insights from decision neuroscience]

Neuroimaging allows to estimate brain activity when individuals are doing something. The location and intensity of this estimated activity provides information on the dynamics and processes that guide choice behaviour and associated actions that should be considered a complement to behavioural studies. Decision neuroscience therefore sheds new light on whether the brain evaluates and compares alternatives when decisions are made, or if other processes are at stake. This work helped to demonstrate that the situations faced by individuals (risky, uncertain, delayed in time) do not all have the same (behavioural) complexity, and are not underlined by activity in the cerebral networks. Taking into account brain dynamics of people (suffering from obesity or not) when making food consumption decisions might allow for improved strategies in public health prevention, far from the rational choice theory promoted by neoclassical economics.

Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice

Attention is thought to play a key role in the computation of stimulus values at the time of choice, which suggests that attention manipulations could be used to improve decision-making in domains where self-control lapses are pervasive. We used an fMRI food choice task with non-dieting human subjects to investigate whether exogenous cues that direct attention to the healthiness of foods could improve dietary choices. Behaviorally, we found that subjects made healthier choices in the presence of health cues. In parallel, stimulus value signals in ventromedial prefrontal cortex were more responsive to the healthiness of foods in the presence of health cues, and this effect was modulated by activity in regions of dorsolateral prefrontal cortex. These findings suggest that the neural mechanisms used in successful self-control can be activated by exogenous attention cues, and provide insights into the processes through which behavioral therapies and public policies could facilitate self-control.

The hippocampus is functionally connected to the striatum and orbitofrontal cortex during context dependent decision making

Many of our everyday actions are only appropriate in certain situations and selecting the appropriate behavior requires that we use current context and previous experience to guide our decisions. The current study examined hippocampal functional connectivity with prefrontal and striatal regions during a task that required participants to make decisions based on the contextual retrieval of overlapping sequential representations. Participants learned four sequences comprised of six faces each. An overlapping condition was created by having two sequences with two identical faces as the middle images. A non-overlapping condition contained two sequences that did not share any faces between them. Hippocampal functional connectivity was assessed during the presentation period and at the critical choice, where participants had to make a contextually dependent decision. The left hippocampus showed significantly increased functional connectivity with dorsal and ventral striatum and anterior cingulate cortex during the presentation period of the overlapping compared to the non-overlapping condition after participants knew the sequences. At the critical choice point of the overlapping condition, the left hippocampus showed stronger functional connectivity with the orbitofrontal cortex. These functional connectivity results suggest that the hippocampus may play a role in decision making by predicting the possibilities of what might come next, allowing orbitofrontal and striatal regions to evaluate the expected choice options in order to make the correct action at the choice point.

Neurobiology of value integration: when value impacts valuation

Everyday choice options have advantages (positive values) and disadvantages (negative values) that need to be integrated into an overall subjective value. For decades, economic models have assumed that when a person evaluates a choice option, different values contribute independently to the overall subjective value of the option. However, human choice behavior often violates this assumption, suggesting interactions between values. To investigate how qualitatively different advantages and disadvantages are integrated into an overall subjective value, we measured the brain activity of human subjects using fMRI while they were accepting or rejecting choice options that were combinations of monetary reward and physical pain. We compared different subjective value models on behavioral and neural data. These models all made similar predictions of choice behavior, suggesting that behavioral data alone are not sufficient to uncover the underlying integration mechanism. Strikingly, a direct model comparison on brain data decisively demonstrated that interactive value integration (where values interact and affect overall valuation) predicts neural activity in value-sensitive brain regions significantly better than the independent mechanism. Furthermore, effective connectivity analyses revealed that value-dependent changes in valuation are associated with modulations in subgenual anterior cingulate cortex-amygdala coupling. These results provide novel insights into the neurobiological underpinnings of human decision making involving the integration of different values.

Frontal cortex and reward-guided learning and decision-making

Reward-guided decision-making and learning depends on distributed neural circuits with many components. Here we focus on recent evidence that suggests four frontal lobe regions make distinct contributions to reward-guided learning and decision-making: the lateral orbitofrontal cortex, the ventromedial prefrontal cortex and adjacent medial orbitofrontal cortex, anterior cingulate cortex, and the anterior lateral prefrontal cortex. We attempt to identify common themes in experiments with human participants and with animal models, which suggest roles that the areas play in learning about reward associations, selecting reward goals, choosing actions to obtain reward, and monitoring the potential value of switching to alternative courses of action.

Apathy blunts neural response to money in Parkinson's disease

Apathy, defined as a primary deficit in motivation and manifested by the simultaneous diminution in the cognitive and emotional concomitants of goal-directed behavior, is a common and debilitating non-motor symptom of Parkinson's disease (PD). Despite the high prevalence and clinical significance of apathy, little is known about its pathophysiology, and in particular how apathy relates to alterations in the neural circuitry underpinning the cognitive and emotional components of goal-directed behavior. Here, we examined the neural coding of reward cues in patients with PD, with or without clinically significant levels of apathy, during performance of a spatial search task during H(2) (15)O PET (positron emission tomography) functional neuroimaging. By manipulating search outcome (money reward vs valueless token), while keeping the actions of the participants constant, we examined the influence of apathy on the neural coding of money reward cues. We found that apathy was associated with a blunted response to money in the ventromedial prefrontal cortex, amygdala, striatum, and midbrain, all part of a distributed neural circuit integral to the representation of the reward value of stimuli and actions, and the influence of reward cues on behavior. Disruption of this circuitry potentially underpins the expression of the various manifestations of apathy in PD, including reduced cognitive, emotional, and behavioral facets of goal-directed behavior.

Dynamic construction of stimulus values in the ventromedial prefrontal cortex

Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150–250 ms, 400–550 ms, and 700–800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions.

Obesity-Related Differences between Women and Men in Brain Structure and Goal-Directed Behavior

Gender differences in the regulation of body-weight are well documented. Here, we assessed obesity-related influences of gender on brain structure as well as performance in the Iowa Gambling Task. This task requires evaluation of both immediate rewards and long-term outcomes and thus mirrors the trade-off between immediate reward from eating and the long-term effect of overeating on body-weight. In women, but not in men, we show that the preference for salient immediate rewards in the face of negative long-term consequences is higher in obese than in lean subjects. In addition, we report structural differences in the left dorsal striatum (i.e., putamen) and right dorsolateral prefrontal cortex for women only. Functionally, both regions are known to play complimentary roles in habitual and goal-directed control of behavior in motivational contexts. For women as well as men, gray matter volume correlates positively with measures of obesity in regions coding the value and saliency of food (i.e., nucleus accumbens, orbitofrontal cortex) as well as in the hypothalamus (i.e., the brain's central homeostatic center). These differences between lean and obese subjects in hedonic and homeostatic control systems may reflect a bias in eating behavior toward energy-intake exceeding the actual homeostatic demand. Although we cannot infer from our results the etiology of the observed structural differences, our results resemble neural and behavioral differences well known from other forms of addiction, however, with marked differences between women and men. These findings are important for designing gender-appropriate treatments of obesity and possibly its recognition as a form of addiction.

Infrequent, task-irrelevant monetary gains and losses engage dorsolateral and ventrolateral prefrontal cortex

Decision making is commonly conceived to reflect the interplay of mutually antagonistic systems: executive processes must inhibit affective information to make adaptive choices. Consistent with this interpretation, prior studies have shown that the dorsolateral prefrontal cortex (dlPFC) is activated by executive processing and deactivated during emotional processing, with the reverse pattern found within the ventrolateral prefrontal cortex (vlPFC). To evaluate whether this pattern generalizes to other affective stimuli--here, monetary rewards--we modified the emotional oddball task to use behaviorally irrelevant reward stimuli, while matching analysis methods and task parameters to those of previous research. Contrary to the double-dissociation model advanced for emotional stimuli, we found that monetary stimuli produced activations within both the dlPFC and the vlPFC. This suggests that monetary stimuli are treated like affective stimuli by vlPFC but like task-relevant target stimuli by dlPFC. Our results suggest differential functional roles in affective and executive processing for these brain regions: the dlPFC supports contingency processing, while the vlPFC evaluates affective or conceptual information.

Social and monetary reward learning engage overlapping neural substrates

Learning to make choices that yield rewarding outcomes requires the computation of three distinct signals: stimulus values that are used to guide choices at the time of decision making, experienced utility signals that are used to evaluate the outcomes of those decisions and prediction errors that are used to update the values assigned to stimuli during reward learning. Here we investigated whether monetary and social rewards involve overlapping neural substrates during these computations. Subjects engaged in two probabilistic reward learning tasks that were identical except that rewards were either social (pictures of smiling or angry people) or monetary (gaining or losing money). We found substantial overlap between the two types of rewards for all components of the learning process: a common area of ventromedial prefrontal cortex (vmPFC) correlated with stimulus value at the time of choice and another common area of vmPFC correlated with reward magnitude and common areas in the striatum correlated with prediction errors. Taken together, the findings support the hypothesis that shared anatomical substrates are involved in the computation of both monetary and social rewards.

Hypothetical and real choice differentially activate common valuation areas

Hypothetical reports of intended behavior are commonly used to draw conclusions about real choices. A fundamental question in decision neuroscience is whether the same type of valuation and choice computations are performed in hypothetical and real decisions. We investigated this question using functional magnetic resonance imaging while human subjects made real and hypothetical choices about purchases of consumer goods. We found that activity in common areas of the orbitofrontal cortex and the ventral striatum correlated with behavioral measures of the stimulus value of the goods in both types of decision. Furthermore, we found that activity in these regions was stronger in response to the stimulus value signals in the real choice condition. The findings suggest that the difference between real and hypothetical choice is primarily attributable to variations in the value computations of the medial orbitofrontal cortex and the ventral striatum, and not attributable to the use of different valuation systems, or to the computation of stronger stimulus value signals in the hypothetical condition.

Value encoding in single neurons in the human amygdala during decision making

A growing consensus suggests that the brain makes simple choices by assigning values to the stimuli under consideration and then comparing these values to make a decision. However, the network involved in computing the values has not yet been fully characterized. Here, we investigated whether the human amygdala plays a role in the computation of stimulus values at the time of decision making. We recorded single neuron activity from the amygdala of awake patients while they made simple purchase decisions over food items. We found 16 amygdala neurons, located primarily in the basolateral nucleus that responded linearly to the values assigned to individual items.