The Experts below are selected from a list of 9045 Experts worldwide ranked by ideXlab platform
Raffaello Seri - One of the best experts on this subject based on the ideXlab platform.
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the analytic hierarchy process and the theory of measurement
Management Science, 2010Co-Authors: Michele Bernasconi, Christine Choirat, Raffaello SeriAbstract:The analytic hierarchy process (AHP) is a decision-making procedure widely used in management for establishing priorities in multicriteria decision problems. Underlying the AHP is the theory of ratio-scale measures developed in psychophysics since the middle of the last century. It is, however, well known that classical ratio-scaling approaches have several problems. We reconsider the AHP in the light of the modern theory of measurement based on the so-called separable representations recently axiomatized in Mathematical Psychology. We provide various theoretical and empirical results on the extent to which the AHP can be considered a reliable decision-making procedure in terms of the modern theory of subjective measurement.
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measurement by subjective estimation testing for separable representations
Journal of Mathematical Psychology, 2008Co-Authors: Michele Bernasconi, Christine Choirat, Raffaello SeriAbstract:Abstract Studying how individuals compare two given quantitative stimuli, say d 1 and d 2 , is a fundamental problem. One very common way to address it is through ratio estimation, that is to ask individuals not to give values to d 1 and d 2 , but rather to give their estimates of the ratio p = d 1 / d 2 . Several psychophysical theories (the best known being Stevens’ power-law) claim that this ratio cannot be known directly and that there are cognitive distortions on the apprehension of the different quantities. These theories result in the so-called separable representations [Luce, R. D. (2002). A psychophysical theory of intensity proportions, joint presentations, and matches. Psychological Review, 109, 520–532; Narens, L. (1996). A theory of ratio magnitude estimation. Journal of Mathematical Psychology, 40, 109–788], which include Stevens’ model as a special case. In this paper we propose a general statistical framework that allows for testing in a rigorous way whether the separable representation theory is grounded or not. We conclude in favor of it, but reject Stevens’ model. As a byproduct, we provide estimates of the psychophysical functions of interest.
Roger Ratcliff - One of the best experts on this subject based on the ideXlab platform.
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Psychology and neurobiology of simple decisions
Trends in Neurosciences, 2004Co-Authors: Philip L Smith, Roger RatcliffAbstract:Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in Mathematical Psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data.
Michele Bernasconi - One of the best experts on this subject based on the ideXlab platform.
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the analytic hierarchy process and the theory of measurement
Management Science, 2010Co-Authors: Michele Bernasconi, Christine Choirat, Raffaello SeriAbstract:The analytic hierarchy process (AHP) is a decision-making procedure widely used in management for establishing priorities in multicriteria decision problems. Underlying the AHP is the theory of ratio-scale measures developed in psychophysics since the middle of the last century. It is, however, well known that classical ratio-scaling approaches have several problems. We reconsider the AHP in the light of the modern theory of measurement based on the so-called separable representations recently axiomatized in Mathematical Psychology. We provide various theoretical and empirical results on the extent to which the AHP can be considered a reliable decision-making procedure in terms of the modern theory of subjective measurement.
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measurement by subjective estimation testing for separable representations
Journal of Mathematical Psychology, 2008Co-Authors: Michele Bernasconi, Christine Choirat, Raffaello SeriAbstract:Abstract Studying how individuals compare two given quantitative stimuli, say d 1 and d 2 , is a fundamental problem. One very common way to address it is through ratio estimation, that is to ask individuals not to give values to d 1 and d 2 , but rather to give their estimates of the ratio p = d 1 / d 2 . Several psychophysical theories (the best known being Stevens’ power-law) claim that this ratio cannot be known directly and that there are cognitive distortions on the apprehension of the different quantities. These theories result in the so-called separable representations [Luce, R. D. (2002). A psychophysical theory of intensity proportions, joint presentations, and matches. Psychological Review, 109, 520–532; Narens, L. (1996). A theory of ratio magnitude estimation. Journal of Mathematical Psychology, 40, 109–788], which include Stevens’ model as a special case. In this paper we propose a general statistical framework that allows for testing in a rigorous way whether the separable representation theory is grounded or not. We conclude in favor of it, but reject Stevens’ model. As a byproduct, we provide estimates of the psychophysical functions of interest.
Christopher Summerfield - One of the best experts on this subject based on the ideXlab platform.
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perceptual decision making in rodents monkeys and humans
Neuron, 2017Co-Authors: Timothy D Hanks, Christopher SummerfieldAbstract:Perceptual decision making is the process by which animals detect, discriminate, and categorize information from the senses. Over the past two decades, understanding how perceptual decisions are made has become a central theme in the neurosciences. Exceptional progress has been made by recording from single neurons in the cortex of the macaque monkey and using computational models from Mathematical Psychology to relate these neural data to behavior. More recently, however, the range of available techniques and paradigms has dramatically broadened, and researchers have begun to harness new approaches to explore how rodents and humans make perceptual decisions. The results have illustrated some striking convergences with findings from the monkey, but also raised new questions and provided new theoretical insights. In this review, we summarize key findings, and highlight open challenges, for understanding perceptual decision making in rodents, monkeys, and humans.
Clintin P Davisstober - One of the best experts on this subject based on the ideXlab platform.
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analysis of multinomial models under inequality constraints applications to measurement theory
Journal of Mathematical Psychology, 2009Co-Authors: Clintin P DavisstoberAbstract:Abstract Multinomial random variables are used across many disciplines to model categorical outcomes. Under this framework, investigators often use a likelihood ratio test to determine goodness-of-fit. If the permissible parameter space of such models is defined by inequality constraints, then the maximum likelihood estimator may lie on the boundary of the parameter space. Under this condition, the asymptotic distribution of the likelihood ratio test is no longer a simple χ 2 distribution. This article summarizes recent developments in the constrained inference literature as they pertain to the testing of multinomial random variables, and extends existing results by considering the case of jointly independent mutinomial random variables of varying categorical size. This article provides an application of this methodology to axiomatic measurement theory as a means of evaluating properly operationalized measurement axioms. This article generalizes Iverson and Falmagne’s [Iverson, G. J. & Falmagne, J. C. (1985). Statistical issues in measurement. Mathematical Social Sciences, 10, 131–153] seminal work on the empirical evaluation of measurement axioms and provides a classical counterpart to Myung, Karabatsos, and Iverson’s [Myung, J. I., Karabatsos, G. & Iverson, G. J. (2005). A Bayesian approach to testing decision making axioms. Journal of Mathematical Psychology, 49, 205–225] Bayesian methodology on the same topic.