Decoding the neural substrates of reward-related decision making with functional MRI

Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we measured brain activity using functional MRI in a group of subjects while they performed a simple reward-based decision-making task: probabilistic reversal-learning. We recorded brain activity from nine distinct regions of interest previously implicated in decision making and separated out local spatially distributed signals in each region from global differences in signal. Using a multivariate analysis approach, we determined the extent to which global and local signals could be used to decode subjects' subsequent behavioral choice, based on their brain activity on the preceding trial. We found that subjects' decisions could be decoded to a high level of accuracy on the basis of both local and global signals even before they were required to make a choice, and even before they knew which physical action would be required. Furthermore, the combined signals from three specific brain areas (anterior cingulate cortex, medial prefrontal cortex, and ventral striatum) were found to provide all of the information sufficient to decode subjects' decisions out of all of the regions we studied. These findings implicate a specific network of regions in encoding information relevant to subsequent behavioral choice

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

Temporal isolation of neural processes underlying face preference decisions

Decisions about whether we like someone are often made so rapidly from first impressions that it is difficult to examine the engagement of neural structures at specific points in time. Here, we used a temporally extended decision-making paradigm to examine brain activation with functional MRI (fMRI) at sequential stages of the decision-making process. Activity in reward-related brain structures—the nucleus accumbens (NAC) and orbitofrontal cortex (OFC)—was found to occur at temporally dissociable phases while subjects decided which of two unfamiliar faces they preferred. Increases in activation in the OFC occurred late in the trial, consistent with a role for this area in computing the decision of which face to choose. Signal increases in the NAC occurred early in the trial, consistent with a role for this area in initial preference formation. Moreover, early signal increases in the NAC also occurred while subjects performed a control task (judging face roundness) when these data were analyzed on the basis of which of those faces were subsequently chosen as preferred in a later task. The findings support a model in which rapid, automatic engagement of the NAC conveys a preference signal to the OFC, which in turn is used to guide choice

Neural correlates of mentalizing-related computations during strategic interactions in humans

Competing successfully against an intelligent adversary requires the ability to mentalize an opponent's state of mind to anticipate his/her future behavior. Although much is known about what brain regions are activated during mentalizing, the question of how this function is implemented has received little attention to date. Here we formulated a computational model describing the capacity to mentalize in games. We scanned human subjects with functional MRI while they participated in a simple two-player strategy game and correlated our model against the functional MRI data. Different model components captured activity in distinct parts of the mentalizing network. While medial prefrontal cortex tracked an individual's expectations given the degree of model-predicted influence, posterior superior temporal sulcus was found to correspond to an influence update signal, capturing the difference between expected and actual influence exerted. These results suggest dissociable contributions of different parts of the mentalizing network to the computations underlying higher-order strategizing in humans

Neural computations underlying action-based decision making in the human brain

Action-based decision making involves choices between different physical actions to obtain rewards. To make such decisions the brain needs to assign a value to each action and then compare them to make a choice. Using fMRI in human subjects, we found evidence for action-value signals in supplementary motor cortex. Separate brain regions, most prominently ventromedial prefrontal cortex, were involved in encoding the expected value of the action that was ultimately taken. These findings differentiate two main forms of value signals in the human brain: those relating to the value of each available action, likely reflecting signals that are a precursor of choice, and those corresponding to the expected value of the action that is subsequently chosen, and therefore reflecting the consequence of the decision process. Furthermore, we also found signals in the dorsomedial frontal cortex that resemble the output of a decision comparator, which implicates this region in the computation of the decision itself

Neuronal Distortions of Reward Probability without Choice

Reward probability crucially determines the value of outcomes. A basic phenomenon, defying explanation by traditional decision theories, is that people often overweigh small and underweigh large probabilities in choices under uncertainty. However, the neuronal basis of such reward probability distortions and their position in the decision process are largely unknown. We assessed individual probability distortions with behavioral pleasantness ratings and brain imaging in the absence of choice. Dorsolateral frontal cortex regions showed experience dependent overweighting of small, and underweighting of large, probabilities whereas ventral frontal regions showed the opposite pattern. These results demonstrate distorted neuronal coding of reward probabilities in the absence of choice, stress the importance of experience with probabilistic outcomes and contrast with linear probability coding in the striatum. Input of the distorted probability estimations to decision-making mechanisms are likely to contribute to well known inconsistencies in preferences formalized in theories of behavioral economics

Studies on growth rates in pigs and the effect of birth weight

End of project reportThe purpose of this study was to assess some environmental and management factors that affect growth performance on commercial pig units. In experiment 1, a survey was carried out on 22 pig units of known growth performance in south-west Ireland to compare management factors between those showing poor and good growth rates. Low growth rate appears to be due to the cumulative effect of a combination of factors. Experiment 2 was conducted to determine the effects of providing an additional feeder on performance of weaned piglets. No benefits were recorded. Feed consumed from the additional feeder was a replacement for feed that otherwise would have been consumed from the control hopper feeder. Experiment 3 was designed to determine if pig performance and efficiency of growth were affected by weight at birth and at weaning. Lightweight pigs showed inferior growth performance up to the finisher period. Although they compensated some of the inferior growth towards the time of slaughter, they never reached the weights of the heavy birth-weight animals. Males were either significantly heavier or tended to be heavier than females throughout. There was no significant difference between the sexes in the number of days to slaughter. Light and heavy pigs did not differ in the levels of IGF-1 in their blood plasma; however lightweight pigs had significantly lower IgG preweaning. Experiment 4 aimed to determine whether piglet birth weight influenced growth performance, plasma IGF-1 concentrations and muscle fibre characteristics at day 42 of life. At slaughter (Day 42) light birth weight pigs were significantly (P < 0.001) lighter. Plasma IGF-1 concentration was lower by 28% (P=0.06) in light pigs. Muscle fibre cross sectional area and total fibre number were not significantly different between groups. This study should be repeated with bigger numbers

Energy-using appliances and energy-saving features: Determinants of ownership in Ireland

Energy usage and energy efficiency are of increasing concern in Ireland. Regression analyses on a large household micro-dataset reveal that those homes that have more energy-saving features are also likely to have a high 'potential energy use'. Statistically significant dwelling features include location, value and dwelling type, while household features such as income, age, period of residency, social status and tenure type are also important