Antonio Rangel

A framework for studying the neurobiology of value-based decision making

Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.

Marketing actions can modulate neural representations of experienced pleasantness

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.

Addiction and Cue-Triggered Decision Processes

We propose a model of addiction based on three premises: (i) use among addicts is frequently a mistake; (ii) experience sensitizes an individual to environmental cues that trigger mistaken usage; (iii) addicts understand and manage their susceptibilities. We argue that these premises find support in evidence from psychology, neuroscience, and clinical practice. The model is tractable and generates a plausible mapping between behavior and the characteristics of the user, substance, and environment. It accounts for a number of important patterns associated with addiction, gives rise to a clear welfare standard, and has novel implications for policy.

Neural computations associated with goal-directed choice

In goal-directed decision-making, animals choose between actions that are associated with different reward outcomes (e.g., foods) and with different costs (e.g., effort). Rapid advances have been made over the past few years in our understanding of the computations associated with goal-directed choices, and of how those computations are implemented in the brain. We review some important findings, with an emphasis on computational models, human fMRI, and monkey neurophysiology studies.

Value Computations in Ventral Medial Prefrontal Cortex during Charitable Decision Making Incorporate Input from Regions Involved in Social Cognition

Little is known about the neural networks supporting value computation during complex social decisions. We investigated this question using functional magnetic resonance imaging while subjects made donations to different charities. We found that the blood oxygenation level-dependent signal in ventral medial prefrontal cortex (VMPFC) correlated with the subjective value of voluntary donations. Furthermore, the region of the VMPFC identified showed considerable overlap with regions that have been shown to encode for the value of basic rewards at the time of choice, suggesting that it might serve as a common valuation system during decision making. In addition, functional connectivity analyses indicated that the value signal in VMPFC might integrate inputs from networks, including the anterior insula and posterior superior temporal cortex, that are thought to be involved in social cognition.

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.

Informatic parcellation of the network involved in the computation of subjective value

Understanding how the brain computes value is a basic question in neuroscience. Although individual studies have driven this progress, meta-analyses provide an opportunity to test hypotheses that require large collections of data. We carry out a meta-analysis of a large set of functional magnetic resonance imaging studies of value computation to address several key questions. First, what is the full set of brain areas that reliably correlate with stimulus values when they need to be computed? Second, is this set of areas organized into dissociable functional networks? Third, is a distinct network of regions involved in the computation of stimulus values at decision and outcome? Finally, are different brain areas involved in the computation of stimulus values for different reward modalities? Our results demonstrate the centrality of ventromedial prefrontal cortex (VMPFC), ventral striatum and posterior cingulate cortex (PCC) in the computation of value across tasks, reward modalities and stages of the decision-making process. We also find evidence of distinct subnetworks of co-activation within VMPFC, one involving central VMPFC and dorsal PCC and another involving more anterior VMPFC, left angular gyrus and ventral PCC. Finally, we identify a posterior-to-anterior gradient of value representations corresponding to concrete-to-abstract rewards.

Neural evidence for inequality-averse social preferences

A popular hypothesis in the social sciences is that humans have social preferences to reduce inequality in outcome distributions because it has a negative impact on their experienced reward. Although there is a large body of behavioural and anthropological evidence consistent with the predictions of these theories, there is no direct neural evidence for the existence of inequality-averse preferences. Such evidence would be especially useful because some behaviours that are consistent with a dislike for unequal outcomes could also be explained by concerns for social image or reciprocity, which do not require a direct aversion towards inequality. Here we use functional MRI to test directly for the existence of inequality-averse social preferences in the human brain. Inequality was created by recruiting pairs of subjects and giving one of them a large monetary endowment. While both subjects evaluated further monetary transfers from the experimenter to themselves and to the other participant, we measured neural responses in the ventral striatum and ventromedial prefrontal cortex, two areas that have been shown to be involved in the valuation of monetary and primary rewards in both social and non-social contexts. Consistent with inequality-averse models of social preferences, we find that activity in these areas was more responsive to transfers to others than to self in the ‘high-pay’ subject, whereas the activity of the ‘low-pay’ subject showed the opposite pattern. These results provide direct evidence for the validity of this class of models, and also show that the brain’s reward circuitry is sensitive to both advantageous and disadvantageous inequality.

Appetitive and Aversive Goal Values Are Encoded in the Medial Orbitofrontal Cortex at the Time of Decision Making

An essential feature of choice is the assignment of goal values (GVs) to the different options under consideration at the time of decision making. This computation is done when choosing among appetitive and aversive items. Several groups have studied the location of GV computations for appetitive stimuli, but the problem of valuation in aversive contexts at the time of decision making has been ignored. Thus, although dissociations between appetitive and aversive components of value signals have been shown in other domains such as anticipatory and outcome values, it is not known whether appetitive and aversive GVs are computed in similar brain regions or in separate ones. We investigated this question using two different functional magnetic resonance imaging studies while human subjects placed real bids in an economic auction for the right to eat/avoid eating liked/disliked foods. We found that activity in a common area of the medial orbitofrontal cortex and the dorsolateral prefrontal cortex correlated with both appetitive and aversive GVs. These findings suggest that these regions might form part of a common network.

Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions.

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.