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

Access courses as a site of engagement: a research project

This research project was funded by the Greater Manchester Strategic Alliance and Aimhigher Research Network North West. A database of Access students was held at the University of Salford that included students from 1998-2006. The names of the students were gathered by the Access Unit from their Enrichment Programme over the period. Ethical approval for the research was sought from the IRIS Director and advice on the Data Protection Act sought from the manager responsible within the university. The database contained information on name, age, address, telephone contact, gender, ethnicity, college and Access course attended. There were approximately 6000 entries on the database. “Access to higher education courses offer a route into higher education (HE) for those who do not have the educational qualifications which are usually required for entry. These courses provide the underpinning knowledge and skills needed for university-level study, and lead to the award of the Access to HE qualification, which is of an equivalent standard to Level 3 qualifications, such as A levels.” UCAS website. Individuals can study a range of courses in different subject areas such as health, science or humanities. Access courses can be studied over one year as a full time course or over two-three years as a part time course. The starting point for the study is the view that to enrol on an Access to HE course means that a major decision or turning point in an adult’s life has taken place and that the individual wants to change direction. This change of direction is important and suggests that individuals may have missed an opportunity earlier in their lives or do not wish to continue in the same employment situation or in the case of many women who are carers their circumstances have changed. The engagement in learning is an agentic act on the part of the individual that may be prompted by others in the immediate family or friends. However, a necessary aspect of this engagement is the provision of Access courses as a means to enter higher education or change employment

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 Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain

Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric–psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms

The application of low crude protein wheat-soyabean diets to growing and finishing pigs: 2. The effects on nutrient digestibility, nitrogen excretion, faecal volatile fatty acid concentration and ammonia emission from boars

peer-reviewedThis study received financial support from Telltech Ltd. (Wicklow, Ireland) and Enterprise Ireland (Dublin, Ireland).Diets containing 132, 152, 183 and 206 g/kg crude protein (CP) were fed to growing and finishing boars to evaluate the effect on nutrient digestibility, N balance, faecal volatile fatty acids (VFA) and ammonia-N (NH3–N) emission. Dietary CP concentration was adjusted by altering the ratio of wheat:soyabean meal. Lysine, threonine, tryptophan and total sulphur-containing amino acids were included in all diets at concentrations equivalent to that in the highest CP diet. All diets were formulated to provide 9.7 MJ/kg of net energy. Urine and faeces were collected from 16 boars (4 boars per treatment) housed in metabolism crates. Collections were performed at 72, 80 and 87 kg live weight. NH3–N emission was measured over 10 days using a laboratory scale procedure. Reducing the concentration of dietary CP decreased N intake (linear, P < 0.01), the excretion of urinary N, ammoniacal N and total N (linear, P < 0.001; cubic, P < 0.001) and the emission of NH3–N (linear, P < 0.001; cubic, P < 0.01). Total N excretion and NH3–N emission decreased 8.7% and 10.1% per 10 g/kg reduction in dietary CP concentration between 205.6 and 131.9 g/kg, respectively. There was no interaction between dietary CP concentration and collection period. N balance differed between the collection periods and less NH3–N was emitted at 87 kg than at 72 kg. Decreasing dietary CP reduced faecal VFA concentration (linear, P < 0.05) and the molar proportions of acetic and butyric acids (quadratic, P < 0.01).Enterprise Irelan

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

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

Dissociating the Role of the Orbitofrontal Cortex and the Striatum in the Computation of Goal Values and Prediction Errors

To make sound economic decisions, the brain needs to compute several different value-related signals. These include goal values that measure the predicted reward that results from the outcome generated by each of the actions under consideration, decision values that measure the net value of taking the different actions, and prediction errors that measure deviations from individuals' previous reward expectations. We used functional magnetic resonance imaging and a novel decision-making paradigm to dissociate the neural basis of these three computations. Our results show that they are supported by different neural substrates: goal values are correlated with activity in the medial orbitofrontal cortex, decision values are correlated with activity in the central orbitofrontal cortex, and prediction errors are correlated with activity in the ventral striatum

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