Showing papers in "Journal of Mathematical Psychology in 2012"

TL;DR: Bayes factors have been advocated as superior to pp-values for assessing statistical evidence in data as mentioned in this paper, and they have been widely used in the literature for assessing power law and skill acquisition.

TL;DR: This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.

TL;DR: This tutorial explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function, and hence that can be used to estimate posterior distributions of parameters for simulation-based models.

TL;DR: In this paper, the authors demonstrate that the standard measurement method is prone to contamination by global fluctuations in performance over the course of an experiment, which can cause either spurious detection of post-error slowing or masking of PES.

TL;DR: The parallels between this problem and that of selective influences in behavioral sciences is established by observing that noncommuting measurements in quantum physics are mutually exclusive and can be treated as different levels of one and the same factor.

TL;DR: In this article, the authors demonstrate statistical properties of the components of the capacity measure and propose a significance test for comparing the capacity coefficient to a baseline measure or two capacity coefficients to each other.

TL;DR: This work introduces differential evolution as a computationally efficient genetic algorithm for proposal generation in the authors' ABC sampler and shows how using this method allows the new ABC algorithm, called ABCDE, to obtain accurate posterior estimates in fewer iterations than kernel-based ABC algorithms and to scale to high-dimensional parameter spaces that have proven difficult for current ABC methods.

TL;DR: A tutorial exposition on the Bayesian approach in analyzing structural equation models (SEMs), which can be regarded as regression models with observed and latent variables, which would encounter serious difficulties when dealing with complicated models and/or data structures.

TL;DR: In this work, a Bayesian framework for the scaling of perception is formulated and logarithmic and related scalings are optimal under expected relative error fidelity and arises as being information theoretically efficient under the constraint of limited representability.

TL;DR: In this paper, a probabilistic approach to inferring the conditional if not - A then B from the disjunction A or B is proposed. But the approach is restricted to the case when A or b is constructively justified.

TL;DR: This article developed and characterized new representations for the maxdiff model for best-worst choice between multi-attribute options; in particular, they state conditions under which the scale value of a multiattribute option is a product of independent ratio scales on each attribute.

TL;DR: The standard signal detection theory (SDT) approach to m -alternative forced choice uses the proportion correct as the outcome variable and assumes that there is no response bias, which simplifies the model and estimation of its parameters.

TL;DR: In this article, the authors argue that non-commutativity is ubiquitous in psychology where almost every interaction with a mental system changes that system in an uncontrollable fashion, and that psychological order effects for sequential measurements are to be expected as a rule.

TL;DR: In this paper, the authors compare and contrast two CCT models for items requiring a dichotomous, true/false answer: the General Condorcet Model (GCM) and the Latent Truth Model (LTM).

TL;DR: A general quantum-like model of decision making is developed based on linear algebra, the von Neumann–Luders projection postulate, Born’s rule, and the quantum representation of the state space of a composite system by the tensor product that generalizes the classical Bayesian inference in a natural way.

TL;DR: This is the first CCT finite-mixture model, and it is named the Multi-Culture GCM (MC-GCM), which is developed axiomatically and a notable property is derived that can suggest the appropriate number of mixtures for a given data set.

TL;DR: Stochastic GRT is extended and its implicated methodology for cognitive/perceptual independence is implicated, to an entire class of parallel systems satisfying very general conditions, and a number of theorems are proven concerning stochastic forms of independence.

TL;DR: This paper presents a framework consisting of definitions and proof techniques for assessing degrees of structure-approximability, and illustrates the use of this framework for a particular intractable cognitive theory, i.e., Thagard and Verbeurgt’s Coherence model, known to be equivalent to harmony maximization in Hopfield networks.

TL;DR: It is shown that in specific cases, modifications of the fitted model yield basic local independence model parameterizations that are not identifiable, which are critical for the interpretation of error rates.

TL;DR: In this paper, different separability conditions that characterize the numerical representability of semi-orders through a real-valued function and a strictly positive threshold are analyzed. But they do not characterize the Scott-suppes representability.

TL;DR: In this paper, the authors used three Kuramoto oscillators to model a continuum of responses, and provided detailed numerical simulations and analysis of the three-oscillator Kuramoto problem.

TL;DR: In this paper, the convex hull of the set of simple lexicographic semi-orders (Davis-Stober, 2010) is defined as a convex polytope.

TL;DR: In this article, the authors provide an axiomatic characterization of a family of criteria for ranking completely uncertain and/or ambiguous decisions, which can be thought of as assigning to every consequence (probability distribution) of a decision an equal probability of occurrence and as comparing decisions on the basis of the expected utility of their consequences for some utility function.

TL;DR: Navarro et al. as mentioned in this paper proposed an improved method for calculating the cumulative first-passage time distribution in Wiener diffusion models with two absorbing barriers, and derived upper bounds for the approximation error resulting from finite truncation of the series, and determined the number of iterations required to limit the error below a prespecified tolerance.

TL;DR: In this article, the authors introduce two new models for decision-making times for a two-choice decision task with no a priori bias, one based on the mean-field Curie-Weiss model of neural computation and the other based on dynamics near an unstable equilibrium under a small noise perturbation.

TL;DR: This tutorial provides an example of a computational model that emulates what human beings see in natural environments and explains why, as well as how, each computation can be done.

TL;DR: In this article, the distortion of a straight line segment (the target) is induced by an array of non-intersecting curvilinear elements (context) stimulus, and the perceptual distortion of the target is modeled as the solution to a minimization problem in the calculus of variations.

TL;DR: In this article, Bahrami, Olsen, Latham, Roepstorff, and Frith study mathematical models of the collaborative solving of a two-choice discrimination task and estimate the difference between the shared performance for a group of observers over a single person performance.

TL;DR: In this article, an axiomatic derivation of a model proposed by Haken, Kelso, and Bunz that describes dynamical aspects of the rhythmic coordination between two limbs is presented.

TL;DR: Do not use the classical DIC when evaluating inequality constrained hypotheses, better use the Prior Information Criterion (PIC), and the PIC is considered a proper model selection tool in the context of evaluating inequality constraint hypotheses.