Cortical substrates for exploratory decisions in humans

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.     

Dietary overlap of snow leopard and other carnivores in the Pamirs of Northwestern China

Information about competition between carnivore species for food within high altitude regions is limited.Data collected from the Taxkorgan Nature Reserve,China revealed important interactions between snow leopard(Panthera uncia),grey wolf(Canis lupus),red fox(Vulpes vulpes)and their prey species,including domestic livestock.Sixty-four line transects were conducted in order to identify field signs of habitat occupancy and collect scats for diet analysis.High dietary overlap was observed between all three carnivore species:snow leopard and red fox(Pianka’s index=0.96),red fox and grey wolf(Pianka’s index=0.90),snow leopard and grey wolf(Pianka’s index=0.87).Snow leopard and grey wolf showed significant predation on livestock(36.8%for snow leopard and 29.4%for grey wolf in diet composition).As a pioneering exploration of the endangered snow leopard and its relationship with other species within the alpine ecosystem under livestock grazing pressure,this study contributes a greater understanding of the relationship within carnivore guild in the Pamirs whilst providing implications for conservation planning and project implementation activities in China.     

The Canonical Analysis of Distance

Canonical Variate Analysis (CVA) is one of the most useful of multivariate methods. It is concerned with separating between and within group variation among N samples from K populations with respect to p measured variables. Mahalanobis distance between the K group means can be represented as points in a (K - 1) dimensional space and approximated in a smaller space, with the variables shown as calibrated biplot axes. Within group variation may also be shown, together with circular confidence regions and other convex prediction regions, which may be used to discriminate new samples. This type of representation extends to what we term Analysis of Distance (AoD), whenever a Euclidean inter-sample distance is defined. Although the N × N distance matrix of the samples, which may be large, is required, eigenvalue calculations are needed only for the much smaller K × K matrix of distances between group centroids. All the ancillary information that is attached to a CVA analysis is available in an AoD analysis. We outline the theory and the R programs we developed to implement AoD by presenting two examples.     

Canonical Analysis: Ranks,Ratios and Fits

Measurements of p variables for n samples are collected into a n×p matrix X, where the samples belong to one of k groups. The group means are separated by Mahalanobis distances. CVA optimally represents the group means of X in an r-dimensional space. This can be done by maximizing a ratio criterion (basically one- dimensional) or, more flexibly, by minimizing a rank-constrained least-squares fitting criterion (which is not confined to being one-dimensional but depends on defining an appropriate Mahalanobis metric). In modern n < p problems, where W is not of full rank, the ratio criterion is shown not to be coherent but the fit criterion, with an attention to associated metrics, readily generalizes. In this context we give a unified generalization of CVA, introducing two metrics, one in the range space of W and the other in the null space of W, that have links with Mahalanobis distance. This generalization is computationally efficient, since it requires only the spectral decomposition of a n×n matrix.     

2010 BP Oil Spill and the Systemic Construct of the Gulf Coast Shrimp Supply Chain

On April 20, 2010, the blast on the rig Deepwater Horizon, and the ensuing disaster known as the BP oil spill, has disrupted the Gulf Coast Shrimp supply chain. Six elements of the systemic supply and demand side of that supply chain are identified and discussed. On the supply side are shrimpers, processors, wholesalers, retailers, and restaurants. On the demand side is the consumer. Qualitative investigative methods shed light upon the systemic practice within the gulf coast shrimp industry in the United States.