Experiments have suggested that decisions from experience differ from decisions from description. In experience-based decisions, the decision makers often fail to maximise their payoffs. Previous authors have ascribed the effect of underweighting of rare outcomes to this deviation from maximisation. In this paper, I re-examine and provide further analysis on the effect with an experiment that involves a series of simple binary choice gambles. In the current experiment, decisions that bear small consequences are repeated hundreds of times, feedback on the consequence of each decision is provided immediately, and decision outcomes are accumulated. The participants have to learn about the outcome distributions through sampling, as they are not explicitly provided with prior information on the payoff structure. The current results suggest that the “hot stove effect” is stronger than suggested by previous research and is as important as the payoff variability effect and the effect of underweighting of rare outcomes in analysing decisions from experience in which the features of gambles must be learned through a sampling process.

The density recovery profile is a plot of the spatial density of a set of points as a function of the distance of each of those points from all the others. It is based upon a two-dimensional point autocorrelogram. If the points are randomly distributed, then the profile is flat, with a value equal to the mean spatial density. Thus, any deviation from this value indicates that the presence of the object represented by the point alters the probability of encountering nearby objects of the same set. Increased value near an object indicates clustering, decreased value near an object indicates anticlustering. The method appears to be unique in its ability to provide quantitative measures of the anticlustered state. Two examples are presented. The first is based upon a sample of the distribution of the somata of starburst amacrine cells in the macaque retina; the second is based upon the distribution of the terminal enlargements on the dendrites of a single macaque ganglion cell that projects to the superior colliculus. In both cases, the density recovery profile is initially lower than the mean density, and increases up to the plateau at the value of the mean density. Two useful measures can be derived from this profile: an intensive parameter termed the effective radius, which quantifies the extent of the region of decreased probability and is insensitive to random undersampling of the underlying distribution, and an extensive parameter termed the packing factor, which quantifies the degree of packing possible for a given effective radius, and is insensitive to scaling. An extension of this method, applicable to correlations between two superimposed distributions, and based upon a two-dimensional point cross-correlogram, is also described.

Knowledge of copepod abundance and distribution has been limited, particularly in the Indian Ocean sector, as the use of coarse sampling gear has meant that copepods were frequently lost from the catch. This study analyses samples obtained from Prydz Bay using a fine mesh (300 μm) Rectangular Midwater Trawl (RMT1) net during summer 1992–93. Results demonstrate that a net of mesh 4.5 mm used in previous studies underestimates total copepod abundance by a factor of 38. The abundance of the smaller species has been underestimated the most. New estimates of copepod biomass indicate that copepods represent approximately 27% of krill biomass. Copepod and krill distributions are shown to be discrete at 82.4% dissimilarity. Mean temperature accounted for 33.6% of the variation in copepod distribution while two of the species showed a slight correlation with chlorophyll a pigment data. These results highlight the numerical importance of copepods and the species' distributions in the East Antarctic marine ecosystem.