Objective-To evaluate the eYcacy of a computer based retraining of specific impairments of four diVerent attentional domains in patients with multiple sclerosis. Methods-Twenty two outpatients with multiple sclerosis received consecutively a specific training comprising 12 sessions in each of the two most impaired attention functions. The baseline of attentional deficits, the performance after each training period, and the course of performance in the next nine weeks was assessed by a computerised attention test battery. Additionally, the impact of the training on daily functioning was evaluated with a self rating inventory. Results-Subgroups of patients with multiple sclerosis showing diVerent patterns of attentional impairment could be separated. Significant improvements of performance could almost exclusively be achieved by the specific training programmes. The increase of performance remained stable for at least nine weeks. For quality of life patients reported less attention related problems in everyday situations. Conclusions-In patients with multiple sclerosis it seems worthwhile to assess attentional functions in detail and to train specific attention impairments selectively. (J Neurol Neurosurg Psychiatry 1998;64:455-462)
Salivary alpha-amylase is suggested to be an indirect physiologic correlate of subjective heat pain perception. Future studies should address the role of salivary alpha-amylase depending on the origin of pain, the concerned tissue, and other pain assessment methods.
Quantum decision theory (QDT) is a recently developed theory of decision making based on the mathematics of Hilbert spaces, a framework known in physics for its application to quantum mechanics. This framework formalizes the concept of uncertainty and other effects that are particularly manifest in cognitive processes, which makes it well suited for the study of decision making. QDT describes a decision maker’s choice as a stochastic event occurring with a probability that is the sum of an objective utility factor and a subjective attraction factor. QDT offers a prediction for the average effect of subjectivity on decision makers, the quarter law. We examine individual and aggregated (group) data, and find that the results are in good agreement with the quarter law at the level of groups. At the individual level, it appears that the quarter law could be refined in order to reflect individual characteristics. This article revisits the formalism of QDT along a concrete example and offers a practical guide to researchers who are interested in applying QDT to a dataset of binary lotteries in the domain of gains.
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