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Donahue and Lee identify prefrontal neurons that integrate task-relevant information about past and current stimulus features and past action outcomes across trials during a probabilistic reversal task. The activity of these neurons is sensitive to past rewards and is predictive of imminent behavioral choices, suggesting that they dynamically contribute to the selection of actions that maximize reward during decision making under uncertainty. Neurons in the dorsolateral prefrontal cortex (DLPFC) encode a diverse array of sensory and mnemonic signals, but little is known about how this information is dynamically routed during decision making. We analyzed the neuronal activity in the DLPFC of monkeys performing a probabilistic reversal task where information about the probability and magnitude of reward was provided by the target color and numerical cues, respectively. The location of the target of a given color was randomized across trials and therefore was not relevant for subsequent choices. DLPFC neurons encoded signals related to both task-relevant and irrelevant features, but only task-relevant mnemonic signals were encoded congruently with choice signals. Furthermore, only the task-relevant signals related to previous events were more robustly encoded following rewarded outcomes. Thus, multiple types of neural signals are flexibly routed in the DLPFC so as to favor actions that maximize reward.

Although human and animal behaviors are largely shaped by reinforcement and punishment, choices in social settings are also influenced by information about the knowledge and experience of other decision-makers. During competitive games, monkeys increased their payoffs by systematically deviating from a simple heuristic learning algorithm and thereby countering the predictable exploitation by their computer opponent. Neurons in the dorsomedial prefrontal cortex (dmPFC) signaled the animal’s recent choice and reward history that reflected the computer’s exploitative strategy. The strength of switching signals in the dmPFC also correlated with the animal’s tendency to deviate from the heuristic learning algorithm. Therefore, the dmPFC might provide control signals for overriding simple heuristic learning algorithms based on the inferred strategies of the opponent.

A fundamental but rarely contested assumption in economics and neuroeconomics is that decision-makers compute subjective values of risky options by multiplying functions of reward probability and magnitude. By contrast, an additive strategy for valuation allows flexible combination of reward information required in uncertain or changing environments. We hypothesized that the level of uncertainty in the reward environment should determine the strategy used for valuation and choice. To test this hypothesis, we examined choice between risky options in humans and rhesus macaques across three tasks with different levels of uncertainty. We found that whereas humans and monkeys adopted a multiplicative strategy under risk when probabilities are known, both species spontaneously adopted an additive strategy under uncertainty when probabilities must be learned. Additionally, the level of volatility influenced relative weighting of certain and uncertain reward information, and this was reflected in the encoding of reward magnitude by neurons in the dorsolateral prefrontal cortex. Comparing the behaviour of humans and monkeys, Farashahi et al. show that both species take uncertainty into account when weighing reward value and probability. Both species switch to a more flexible strategy for weighing information during learning.

Reinforcement has long been thought to require striatal synaptic plasticity. Indeed, direct striatal manipulations such as self-stimulation of direct-pathway projection neurons (dMSNs) are sufficient to induce reinforcement within minutes. However, it’s unclear what role, if any, is played by downstream circuitry. Here, we used dMSN self-stimulation in mice as a model for striatum-driven reinforcement and mapped the underlying circuitry across multiple basal ganglia nuclei and output targets. We found that mimicking the effects of dMSN activation on downstream circuitry, through optogenetic suppression of basal ganglia output nucleus substantia nigra reticulata (SNr) or activation of SNr targets in the brainstem or thalamus, was also sufficient to drive rapid reinforcement. Remarkably, silencing motor thalamus—but not other selected targets of SNr—was the only manipulation that reduced dMSN-driven reinforcement. Together, these results point to an unexpected role for basal ganglia output to motor thalamus in striatum-driven reinforcement.

A fundamental but rarely contested assumption in economics and neuroeconomics is that decision-makers compute subjective values of risky options by multiplying functions of reward probability and magnitude. In contrast, an additive strategy for valuation allows flexible combination of reward information required in uncertain or changing environments. We hypothesized that the level of uncertainty in the reward environment should determine the strategy used for valuation and choice. To test this hypothesis, we examined choice between risky options in humans and monkeys across three tasks with different levels of uncertainty. We found that whereas humans and monkeys adopted a multiplicative strategy under risk when probabilities are known, both species spontaneously adopted an additive strategy under uncertainty when probabilities must be learned. Additionally, the level of volatility influenced relative weighting of certain and uncertain reward information and this was reflected in the encoding of reward magnitude by neurons in the dorsolateral prefrontal cortex.

The striatum is thought to play a central role in action selection and reinforcement, and optogenetic experiments suggest differential roles for direct- and indirect-pathway medium spiny neurons (dMSNs and iMSNs). However, the encoding of value-related information in dMSNs and iMSNs during adaptive decision-making is not well understood. We trained mice on a dynamic foraging task where they had to learn the value of different options based on their recent history of choices and outcomes. Single-cell calcium imaging in dorsomedial striatum revealed that dMSNs and iMSNs were oppositely modulated by the updated value of the different options. Additionally, we found that iMSNs were more active as animals slowed between trials, likely reflecting ongoing changes in motivational state. Together, our results demonstrate that co-activation of dMSNs and iMSNs during action initiation does not simply encode action identity, but instead reflects pathway-specific encoding of movement, motivation, and value information necessary for adaptive decision-making.

In this thesis, I describe results from three different experiments that together provide insight into how different cortical regions encode information related to decision making and uncertainty. In the first experiment, I investigate the role of cortical signals in a set of tasks designed to study the exploration/exploitation dilemma, a classic problem that animals face when making decisions under uncertainty. I analyzed single-cell data from awake, behaving monkeys in four different cortical regions: the supplementary eye field (SEF), the dorsolateral prefrontal cortex (DLPFC), the lateral intraparietal area (LIP), and the anterior cingulate cortex (ACC). I find evidence that only the SEF plays a unique role in exploration. In the second set of experiments, I turned my focus to how signals for decision-making are integrated in the DLPFC through the use of a novel probabilistic reversal task. In this task, animals had to track reward probabilities in a task-relevant color dimension while ignoring events related to a task-irrelevant spatial dimension. I describe properties in single neurons that are related to how the DLPFC selects relevant information while filtering out irrelevant information. In the final set of experiments, I used a modified version of the reversal task and studied how neurons in DLPFC, ACC, and orbitofrontal cortex (OFC) integrate information when environmental uncertainty is systematically manipulated. Consistent with theoretical predictions, I find that the animals integrate information from their prior experience to a greater degree when uncertainty is high. Together, these three experiments shed light on how single neurons in cortex may process information about uncertainty to ultimately help animals make decisions that maximize reward.

Wildfire smoke, consisting primarily of organic aerosols, has profound impacts on air quality, climate and human health. Wildfire organic aerosol evolves over long-time photochemical oxidation due to the formation and ageing of secondary organic aerosol, which substantially changes its magnitude and properties. However, there are large uncertainties in the long-time ageing of wildfire organic aerosol because of the distinct ageing behaviours of the complex organic emissions. Here we developed an oxidation model that simulates the ageing of wildfire organic emissions in the full volatility range on a precursor level and integrated insights from single-species ageing and wildfire emissions ageing experiments and field plume observations to constrain the long-time ageing of wildfire organic aerosol. The model captured the enhancement of organic aerosol mass (2–8 times) and oxygen-to-carbon ratio (1–4 times) in the wildfire ageing experiments. It also reconciled a long-standing discrepancy between field and laboratory observations of the magnitude of secondary organic aerosol formation. The model indicated large emissions-driven variations in precursor contributions to secondary organic aerosol, which further evolve with long-time ageing. The estimated global wildfire secondary organic aerosol production (139 ± 34 Tg per year) was much higher than previous studies omitting or under-constraining long-time ageing. The amount of secondary organic aerosol produced from wildfire emissions is much higher than previously thought, according to model simulations of evolution of individual species of organic aerosol over time.

Secondary organic aerosol (SOA) formed from the atmospheric oxidation of nonmethane organic gases (NMOG) is a major contributor to atmospheric aerosol mass. Emissions and smog chamber experiments were performed to investigate SOA formation from gasoline vehicles, diesel vehicles, and biomass burning. About 10–20% of NMOG emissions from these major combustion sources are not routinely speciated and therefore are currently misclassified in emission inventories and chemical transport models. The smog chamber data demonstrate that this misclassification biases model predictions of SOA production low because the unspeciated NMOG produce more SOA per unit mass than the speciated NMOG. We present new source-specific SOA yield parameterizations for these unspeciated emissions. These parameterizations and associated source profiles are designed for implementation in chemical transport models. Box model calculations using these new parameterizations predict that NMOG emissions from the top six combustion sources form 0.7 Tg y ⁻¹ of first-generation SOA in the United States, almost 90% of which is from biomass burning and gasoline vehicles. About 85% of this SOA comes from unspeciated NMOG, demonstrating that chemical transport models need improved treatment of combustion emissions to accurately predict ambient SOA concentrations.

The Multiple Chamber Aerosol Chemical Aging Study (MUCHACHAS) tested the hypothesis that hydroxyl radical (OH) aging significantly increases the concentration of first-generation biogenic secondary organic aerosol (SOA). OH is the dominant atmospheric oxidant, and MUCHACHAS employed environmental chambers of very different designs, using multiple OH sources to explore a range of chemical conditions and potential sources of systematic error. We isolated the effect of OH aging, confirming our hypothesis while observing corresponding changes in SOA properties. The mass increases are consistent with an existing gap between global SOA sources and those predicted in models, and can be described by a mechanism suitable for implementation in those models.