Parsing cultural impacts on regret and risk in Iran, China and the United Kingdom

Value-based choices are influenced both by powerful counterfactuals, such as regret, and also by risk in potential outcomes. Culture can profoundly affect how humans perceive and act in the world, but it remains unknown how regret in value-based choice and key aspects of risk-taking may differ between cultures. Here our computational approach provides precise and independent metrics, grounded in extensive neurobiological evidence, for the influences of risk and regret on choice. We test for commonalities and differences across three diverse cultures: Iran, China and the UK. Including Iran matters because cross-cultural work on value-based choice is lacking for this key 21(st) Century culture, and also because patterns across the three cultures arbitrates between explanations for differences. We find commonalities, with regret influencing choice across cultures and no consistent cultural difference seen. However, for risk, unlike in both Chinese and Westerners' choices, Iranians are risk-seeking findings consistent across two task variants and further explained by Iranians showing less subjective impact of negative, but not positive, outcomes of risky choices. Our computational approach dissects cultural impacts on two key neurobiologically-grounded quantities in value-based choice, showing that neuroscientific accounts cannot a priori isolate such quantities from culture in the cognitive processes underlying choice

Distinct encoding of risk and value in economic choice between multiple risky options.

Neural encoding of value-based stimuli is suggested to involve representations of summary statistics, including risk and expected value (EV). A more complex, but ecologically more common, context is when multiple risky options are evaluated together. However, it is unknown whether encoding related to option evaluation in these situations involves similar principles. Here we employed fMRI during a task that parametrically manipulated EV and risk in two simultaneously presented lotteries, both of which contained either gains or losses. We found representations of EV in medial prefrontal cortex and anterior insula, an encoding that was dependent on which option was chosen (i.e. chosen and unchosen EV) and whether the choice was over gains or losses. Parietal activity reflected whether the riskier or surer option was selected, whilst activity in a network of regions that also included parietal cortex reflected both combined risk and difference in risk for the two options. Our findings provide support for the idea that summary statistics underpin a representation of value-based stimuli, and further that these summary statistics undergo distinct forms of encoding

Computation and representation in decision making and emotion

This thesis deals with three components of an organism’s interactions with its environment: learning, decision making, and emotions. In a series of 5 studies, I detail relationships between these processes, and investigate the representation and computations whereby they are achieved. In the first experiment I show how subjective wellbeing is influenced by one’s own rewards and expectations, but also those of other people. Furthermore, I find that parameter estimates of empathy predict decision-making in a distinct test of economic generosity. In my second study, I ask how stressful experiences modulate subsequent learning, detailing a specific impairment in action-learning under stress which also manifests itself in altered pupillary responses. In the third, I use a hierarchical model of learning to show that subjective uncertainty in aversive contexts predicts several dimensions of acute stress responses. Furthermore, I find that individuals who show greater uncertainty-tuning in their stress responses are better at predicting the presence of threat. In the final pair of studies I ask how decision variables for value-based choice are represented in the brain. I describe the combination of quality and quantity into value estimates in humans, revealing a central role for the Anterior Cingulate Cortex in value integration using functional magnetic resonance imaging. I next characterize the neural code for value in non-human primate frontal cortex, using single-neuron data from collaborators. These two studies provide convergent evidence that the value code may be more diverse and non-linear than previously reported, potentially conferring the ability to incorporate uncertainty signals directly in the activity of value coding neurons

Neuronal Mechanisms of Decision Making in the Prefrontal Cortex

This thesis examines several aspects of decision computations which are critical for understanding the processes by which decisions are made. It will show that subjects engaged covert attention to bias both saccadic and choice processes during simple decision making tasks even when these stimuli were novel. This saccadic behaviour was overridden when one presented stimulus is relatively more novel than the other implying the existence of separate value comparison circuits in the brain which deal with making value based decisions about attention and choice respectively. Even when the task was made more complex by introducing multiple decision variables this phenomenon of covert attention was maintained. This thesis will demonstrate that subjects controlled both the amount and manner of information gathering during decisions. This behaviour showed features of a confirmation bias. Single cell neuronal recordings were performed while subjects executed a multiattribute decision making task. ACC neurons represented action values and different populations of OFC neurons encoded attribute and attentional values. These neurons did not just reflect value (i.e. an input into a decision process) but instead evolved their coding to represent final choice thereby implying the existence of a parallel decision making circuit which compares value in different frames of reference. Information gathering strategy was also computed in the same frames of reference implying the existence of a common value comparison system which simultaneously drives both choice and information gathering. At the outcome of the decision ACC neurons encoded both categorical reward outcome and positive prediction errors. vmPFC neurons encoded prediction errors while OFC and ACC neurons encode fictive value when rewards were withheld. Finally frame of reference specific computations were observed in LPFC and OFC. The results in this thesis therefore provide novel insight into the role of valuation circuitry during value based decision making and outcome monitoring