Psychophysically principled models of visual simple reaction time.

Visual psychophysics has shown that the perceptual representation of a stimulus has complex time-varying properties that depend on the response characteristics of the channel on which it is encoded. A fundamental expression of these properties is the distinction between sustained and transient processing channels. A theoretical and mathematical framework is introduced that allows such properties to be incorporated into fully stochastic models of simple reaction time (RT). These models, the multichannel leaky stochastic integrators, combine a linear filter model of stimulus encoding with an accumulative decision process and yield a stimulus representation described by a time-inhomogeneous Ornstein-Uhlenbeck diffusion process. Methods for obtaining RT distributions for these models are described, together with comparative fits to luminance-increment data obtained under conditions of channel pooling and channel independence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Practical Bayesian inference using mixtures of mixtures

Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human and other animal nervous systems. The usual framework has independent data values yj arising as yj = mu j + xn0 + j, where the means mu j come from some discrete prior G(mu) and the unknown xno + j's and observed xj, j = 1,...,n0, are Gaussian noise terms. A practically important development of the associated statistical methods is the issue of nonnormality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values xj as arising from a Dirichlet process mixture of normals, in addition to modeling the location prior G(mu) as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate them in the context of neurological response analysis.     

Generalized full-information item bifactor analysis.

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of multidimensional item response theory models for an arbitrary mixing of dichotomous, ordinal, and nominal items. The extended item bifactor model also enables the estimation of latent variable means and variances when data from more than 1 group are present. Generalized user-defined parameter restrictions are permitted within or across groups. We derive an efficient full-information maximum marginal likelihood estimator. Our estimation method achieves substantial computational savings by extending Gibbons and Hedeker's (1992) bifactor dimension reduction method so that the optimization of the marginal log-likelihood requires only 2-dimensional integration regardless of the dimensionality of the latent variables. We use simulation studies to demonstrate the flexibility and accuracy of the proposed methods. We apply the model to study cross-country differences, including differential item functioning, using data from a large international education survey on mathematics literacy. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Comparison requirements and attention in identical-nonidentical stimulus discriminations.

Perceptual matching data show several puzzling effects. Particularly problematic are the disparities between the processing rates for same and different stimuli—the fast–same effect—and between the processing rates for two same–different judgment tasks that are related as mirror images—the task effect. Current models have difficulty accounting simultaneously for both effects. Central to these models is a stimulus comparison process that derives relative judgments of sameness and difference from tests of the congruence of stimulus representations. A contrasting view holds that same–different judgments can be modeled as absolute, rather than relative, judgments. This latter view is shown to be supported by experimental data. Reaction times (RTs) for judgments of identical letter strings increase with string length at the same rate whether judgments are based on all the information in the strings or just the information in a single pair of component letters. The data show that stimulus comparisons of the sort described by previous models are not involved in these judgments. An attentional model accounts for the data and for the fast–same and task effects as well. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Budgeting for Facility Maintenance and Repair. II: Multicriteria Process for Model Selection

The second of two in a series, this paper presents an objective process for selecting an appropriate model for facility maintenance and repair budgeting. Three model evaluation criteria relating facility attributes to maintenance and repair costs—consideration of the facility's physical characteristics and environment, building system and component life cycles, and penalty costs brought on by deferral of maintenance—are introduced. The expense of supplying the necessary data for a model's implementation, given the model user's existing database, is presented as a fourth evaluation criterion. The relative merits of 18 available budgeting models are then assessed against these criteria using various multicriteria decision-making tools: Analysis of dominance, with results displayed via a Hasse diagram; lexicographic ordering; and the technique for order preference by similarity to ideal solution. The results of the evaluation demonstrate how the preferred alternative can change in response to a decision-maker's priorities. Conversely, the selection process can provide considerable insight into these models with very little knowledge about the value system or philosophy of the model users and the decision makers.     

Cognitive psychology meets psychometric theory: On the relation between process models for decision making and latent variable models for individual differences.

This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro versus contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed, and person fit in IRT. We discuss the relation between this model and existing models as well as implications for psychology and psychometrics. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Connectionist and diffusion models of reaction time.

Two connectionist frameworks, GRAIN (J. L. McClelland, 1993) and brain-state-in-a-box (J. A. Anderson, 1991), and R. Ratcliff's (1978) diffusion model were evaluated using data from a signal detection task. Dependent variables included response probabilities, reaction times (RTs) for correct and error responses, and shapes of RT distributions. The diffusion model accounted for all aspects of the data, including error RTs that had previously been a problem for all response-time models. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model that was similar to the diffusion model. The findings advance the development of the diffusion model and show that the long tradition of RT research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Anxiety enhances threat processing without competition among multiple inputs: A diffusion model analysis.

Enhanced processing of threatening information is a well established phenomenon among high-anxious individuals. This effect is most reliably shown in situations where 2 or more items compete for processing resources, suggesting that input competition is a critical component of the effect. However, it could be that there are small effects in situations without input competition, but the dependent measures typically used are not sensitive enough to detect them. The present study analyzed data from a noncompetition task, single-string lexical decision, with the diffusion model, a decision process model that provides a more direct measure of performance differences than either response times or accuracy alone. The diffusion model analysis showed a consistent processing advantage for threatening words in high-anxious individuals, whereas traditional comparisons showed no significant differences. These results challenge the view that input competition is necessary for enhanced threat processing. Implications for theories of anxiety are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Framework for Providing Customized Data Representations for Effective and Efficient Interaction with Mobile Computing Solutions on Construction Sites

Conceptual representations of information contained in product and process models are often difficult to use for accessing data when performing engineering tasks. This is especially true if project-management information contained in product and process models needs to be made accessible on a mobile computer on construction sites. To make this information accessible, customized conceptual and visual information representations are needed. For the project-management tasks of progress monitoring and creating and administering punch lists, existing approaches that provide access to relevant project information are ineffective and inefficient in transforming information from product and process models into usable representations. As a result, these applications do not always provide information representations that are of the required structure, granularity, and type. In this paper, we describe a navigational model framework, which is an approach that effectively and efficiently creates and manages different views of information contained in product and process models. We validated this framework by implementing a prototype system and testing it through a designed set of experiments. The use cases for these experiments were established in an extensive study on the information and data collection needs on construction sites.     

A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables.

Uncorrectable skew and heteroscedasticity are among the "lemons" of psychological data, yet many important variables naturally exhibit these properties. For scales with a lower and upper bound, a suitable candidate for models is the beta distribution, which is very flexible and models skew quite well. The authors present maximum-likelihood regression models assuming that the dependent variable is conditionally beta distributed rather than Gaussian. The approach models both means (location) and variances (dispersion) with their own distinct sets of predictors (continuous and/or categorical), thereby modeling heteroscedasticity. The location submodel link function is the logit and thereby analogous to logistic regression, whereas the dispersion submodel is log linear. Real examples show that these models handle the independent observations case readily. The article discusses comparisons between beta regression and alternative techniques, model selection and interpretation, practical estimation, and software. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Feature-sampling and random-walk models of individual-stimulus recognition.

Traditional process models of old-new recognition have not addressed differences in accuracy and response time between individual stimuli. Two new process models of recognition are presented and applied to response time and accuracy data from 3 old-new recognition experiments. The 1st model is derived from a feature-sampling account of the time course of categorization, whereas the 2nd model is a generalization of a random-walk model of categorization. In the experiments, a new technique was used, which yielded reliable individual-stimulus data through repeated presentation of structurally equivalent items. The results from the experiments showed reliable differences in accuracy and response times between stimuli. The random-walk model provided the better account of the results from the 3 experiments. The implications of the results for process models of recognition are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Turbulent Flow in Filling Ladles

The fluid flow patterns generated during the course of tapping operations from Basic Oxygen Furnaces have been analyzed using two mathematical models of the filling process. The present work is mainly concerned with the application of the κ — ∈ turbulent flow model to describe recirculatory flow in filling ladles. Predicted velocity fields were quite similar to those based on the laminar flow model and both models provided predictions which checked reasonably closely with experimental values using a one tenth scale model. Based on the full scale predictions, it is suggested that the magnitude of flow velocities are technologically significant in terms of their effect on ferro-alloy immersion times.     

A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena.

Further simulations of human multiple-task performance have been conducted with computational models that are based on the executive-process interactive control (EPIC) architecture introduced by D. E. Meyer and D. E. Kieras (see record 84-14604). These models account well for patterns of reaction times and psychological refractory-period phenomena (delays of overt responses after short stimulus onset asynchronies) observed in a variety of laboratory paradigms and realistic situations. This supports the claim of the present theoretical framework that multiple-task performance relies on adaptive executive control, which enables substantial amounts of temporal overlap among stimulus identification, response selection, and movement-production processes for concurrent tasks. Such overlap is achieved through optimized task scheduling by flexible executive processes that satisfy prevailing instructions about task priorities and allocate limited-capacity perceptual-motor resources efficiently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application Framework for Mapping and Simulation of Waste Handling Processes in Construction

This research is focused on modeling waste-handling processes in construction, with particular emphasis on how to map out and simulate on-site waste sorting processes. The research proposes an application framework for (1) guiding the development of process mapping models and simulation models; and (2) further assessing the cost effectiveness of on-site waste sorting efforts under practical site constraints (such as labor resource availability, time control on refuse chute usage, and limited working area space in a building site). The connection has been established between the mapping and simulation techniques in the context of modeling waste handling processes in construction sites, such that the process flowchart resulting from the mapping technique can serve as convenient model input to facilitate the creation of a “dynamic” operations simulation model. A case study of the on-site waste sorting method with one refuse chute for waste classification is presented to demonstrate the complete application framework spanning (1) process mapping; (2) mapping-to-simulation model conversion; and (3) method optimization based on valid simulations.     

Influence of delayed viral production on viral dynamics in HIV-1 infected patients

We present and analyze a model for the interaction of human immunodeficiency virus type 1 (HIV-1) with target cells that includes a time delay between initial infection and the formation of productively infected cells. Assuming that the variation among cells with respect to this 'intracellular' delay can be approximated by a gamma distribution, a high flexible distribution that can mimic a variety of biologically plausible delays, we provide analytical solutions for the expected decline in plasma virus concentration after the initiation of antiretroviral therapy with one or more protease inhibitors. We then use the model to investigate whether the parameters that characterize viral dynamics can be identified from biological data. Using non-linear least-squares regression to fit the model to simulated data in which the delays conform to a gamma distribution, we show that good estimates for free viral clearance rates, infected cell death rates, and parameters characterizing the gamma distribution can be obtained. For simulated data sets in which the delays were generated using other biologically plausible distributions, reasonably good estimates for viral clearance rates, infected cell death rates, and mean delay times can be obtained using the gamma-delay model. For simulated data sets that include added simulated noise, viral clearance rate estimates are not as reliable. If the mean intracellular delay is known, however, we show that reasonable estimates for the viral clearance rate can be obtained by taking the harmonic mean of viral clearance rate estimates from a group of patients. These results demonstrate that it is possible to incorporate distributed intracellular delays into existing models for HIV dynamics and to use these refined models to estimate the half-life of free virus from data on the decline in HIV-1 RNA following treatment.     

Navigational Models for Computer Supported Project Management Tasks on Construction Sites

Integrated product and process models have started gaining acceptance in the construction industry and it is conceivable that in the near future project data will be contained in these models. A project model can contain large data sets making it harder for users to navigate. This challenge is even more difficult in cases where mobile computing is used on site for accessing and collecting data needed for a construction management task. Navigational models are constructs that provide construction personnel, who are using mobile computing applications on construction sites, with information and data collection support relevant to their tasks and environments. Navigational models provide a flexible and dynamic way of grouping and structuring entities of product and process models such that those entities that need to be related for one task are linked directly to minimize the navigation through a given model. Moreover, navigational models provide a way to structure the data contained in product and process models in hierarchies facilitating interaction with entities at multiple levels of detail. In this paper, we describe construction site-based project management tasks and demonstrate how navigational models can facilitate efficient data access and data collection processes by customizing the presented information for a given task and environment of a user.     

Simple and Patlak models for myocardial blood flow measurements with nitrogen-13-ammonia and PET in humans

The Simple and Patlak models for estimating myocardial blood flow with 13N-ammonia have become attractive for clinical applications with PET because of their simplicity and ease of implementation. However, these models are sensitive to factors such as the data acquisition times and data integration times, which can cause errors in the estimation of myocardial blood flow, as demonstrated in this study. Limiting the application of these models to specific conditions can minimize the errors. METHODS: Dynamic PET images of the uptake of 13N-ammonia in the heart were obtained in seven humans under rest and dipyridamole stress. Myocardial blood flow was estimated using the Simple and Patlak models for different data acquisition times and data integration times. Blood flow values were compared to flow values computed with the two-compartment model as a reference. RESULTS: Blood flow values calculated with the Simple and Patlak models during the first 2 min of data acquisition were closely correlated to the two-compartment model values. Longer acquisition times resulted in significant underestimation of blood flow for the Simple model. Long integration times of greater than 60 sec also resulted in significant underestimation of blood flow for both models. CONCLUSION: The Simple and Patlak models produce estimates of myocardial blood flow that are well correlated with the two-compartment model estimated blood flows for the integration time of 60 sec from 60 to 120 sec postinjection. Because of the errors associated with longer data acquisition times and longer integration times, use of these models should be limited to a well-documented data acquisition paradigm.     

Double Modal Transformation and Wind Engineering Applications

Modal transformation techniques are usually adopted in structural dynamics with the aim of decoupling the equations of motion. They are based on the search for an abstract space in which the solution of the problem results simplified. Analogous transformation techniques have recently been developed with the aim of defining a space where a multivariate stochastic process is expressed by a linear combination of one-variate uncorrelated processes. This paper proposes a method, called double modal transformation, by which the dynamic analysis of a linear structure is carried out through the simultaneous transformation of the equations of motion and the loading process. By adopting this technique, the structural response is obtained through a double series expansion in which structural and loading modal contributions are superimposed. Its effectiveness and application are discussed with reference to two classic wind engineering problems—the alongwind response and the vortex-induced crosswind response of slender structures—which provide a wide panorama of the most relevant properties of this procedure.     

An Exemplar Account of the Bow and Set-Size Effects in Absolute Identification.

The extended generalized context model for response times (K. Lamberts, 2000) was designed to account for choice proportions and response times in perceptual categorization. In this article, the hypothesis that the model also offers an account of accuracy and response times in absolute identification was investigated. The model was applied to the data from 2 absolute identification experiments and provided a good account of the bow and the set-size effects in accuracy and response time data from individual participants, including the response time distributions for individual stimuli. The model applications demonstrated that exemplar-based process models offer a viable account of absolute identification data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Comparison of Sequential Sampling Models for Two-Choice Reaction Time.

The authors evaluated 4 sequential sampling models for 2-choice decisions--the Wiener diffusion, Ornstein-Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models--by fitting them to the response time (RT) distributions and accuracy data from 3 experiments. Each of the models was augmented with assumptions of variability across trials in the rate of accumulation of evidence from stimuli, the values of response criteria, and the value of base RT across trials. Although there was substantial model mimicry, empirical conditions were identified under which the models make discriminably different predictions. The best accounts of the data were provided by the Wiener diffusion model, the OU model with small-to-moderate decay, and the accumulator model with long-tailed (exponential) distributions of criteria, although the last was unable to produce error RTs shorter than correct RTs. The relationship between these models and 3 recent, neurally inspired models was also examined. (PsycINFO Database Record (c) 2011 APA, all rights reserved)