Rational After All: Changes in Probability Matching Behaviour Across Time in Humans and Monkeys

Probability matching—where subjects given probabilistic in-put respond in a way that is proportional to those input probabilities—has long been thought to be characteristic of primate performance in probability learning tasks in a variety of contexts, from decision making to the learning of linguistic variation in humans. However, such behaviour is puzzling because it is not optimal in a decision theoretic sense; the optimal strategy is to always select the alternative with the highest positive-outcome probability, known as maximising(in decision making) or regularising (in linguistic tasks). While the tendency to probability match seems to depend somewhat on the participants and the task (i.e., infants are less likely to probability match than adults, monkeys probability matchless than humans, and probability matching is less likely in linguistic tasks), existing studies suffer from a range of deficiencies which make it difficult to robustly assess these differences. In this project we present a series of experiments which systematically test the development of probability matching behaviour over time in simple decision making tasks, across species (humans and Guinea baboons), task complexity, and task domain (linguistic vs non-linguistic).

Offline Signature Verification System with Gaussian Mixture Models (GMM)

Gaussian Mixture Models (GMMs) has been proposed for off-line signature verification. The individual Gaussian components are shown to represent some global features such as skewness, kurtosis, etc. that characterize various aspects of a signature, and are effective for modeling its specificity. The learning phase involves the use of Gaussian Mixture Model (GMM) technique to build a reference model for each signature sample of a particular user. The verification phase uses three layers of statistical techniques. The first layer involves computation of GMM-based log-likelihood probability match score, Â second layer performs the mapping of this score into soft boundary ranges of acceptance or rejection through the use of z-score analysis and normalization function, thirdly, threshold is used to arrive at the final decision of accepting or rejecting a given signature sample. The focus of this work is on faster detection of authenticated signature as no vector analysis is done in GMM. From the experimental results, the new features proved to be more robust than other related features used in the earlier systems. The FAR (False Acceptance Rate) and FRR (False Rejection Rate) for the genuine samples is 0.15 and 0.19 respectively.

A Match Made by Modafinil: Probability Matching in Choice Decisions and Spatial Attention

When predicting where a target or reward will be, participants tend to choose each location commensurate with the true underlying probability (i.e., probability match). The strategy of probability matching involves independent sampling of high and low probability locations on separate trials. In contrast, models of probabilistic spatial attention hypothesize that on any given trial attention will either be weighted toward the high probability location or be distributed equally across all locations. Thus, the strategies of probabilistic sampling by choice decisions and spatial attention appear to differ with regard to low-probability events. This distinction is somewhat surprising because similar brain mechanisms (e.g., pFC-mediated cognitive control) are thought to be important in both functions. Thus, the goal of the current study was to examine the relationship between choice decisions and attentional selection within single trials to test for any strategic differences, then to determine whether that relationship is malleable to manipulations of catecholamine-modulated cognitive control with the drug modafinil. Our results demonstrate that spatial attention and choice decisions followed different strategies of probabilistic information selection on placebo, but that modafinil brought the pattern of spatial attention into alignment with that of predictive choices. Modafinil also produced earlier learning of the probability distribution. Together, these results suggest that enhancing cognitive control mechanisms (e.g., through prefrontal cortical function) leads spatial attention to follow choice decisions in selecting information according to rule-based expectations.

SOURCE IDENTIFICATION OF AN OIL SPILL ON TRINIDAD'S EAST COAST

ABSTRACT In April 1986, a large quantity of unrefined crude oil was released into the nearshore marine environment on Trinidad's east coast. The oil was observed to have affected approximately 20 kilometers of coastline. Physical examination of the oil collected revealed that it had the appearance of a light grade crude, was golden brown in color, and had a characteristic gassy odor. Also observed was a mass mortality of the bivalve Donax sp (chip-chip), which inhabits sandy areas of the intertidal zone. Oil was extracted from samples of water, beach sand, and chip-chip collected from selected stations along the affected area. Also collected were samples of oil from possible sources located both offshore and at land-based facilities. Chemical characterization of the oil extracts, using capillary gas chromatography, atomic absorption and fluorescence spectroscopy, and carbon-13 nuclear magnetic resonance spectrometry, yielded data sets that distinguished the oils in the environment from the suspect source oils investigated. Application of statistical pattern recognition techniques, utilizing a hierarchical clustering procedure, to data from both environmental and suspect source samples yielded a high probability match between the spill samples and samples from one of the two oil companies operating in the area.