Source discrimination, item detection, and multinomial models of source monitoring.

Source monitoring refers to the discrimination of the origin of information. Multinomial models of source monitoring (W. H. Batchelder & D. M. Riefer, 1990) are theories of the decision processes involved in source monitoring that provide separate parameters for source discrimination, item detection, and response biases. Three multinomial models of source monitoring based on different models of decision in a simple detection paradigm (one-high-threshold, low-threshold, and two-high-threshold models) were subjected to empirical tests. With a 3 (distractor similarity)?×?3 (source similarity) factorial design, the effect of difficulty of item detection and source discrimination on corresponding model parameters was examined. Only the source-monitoring model that is based on a two-high-threshold model of item recognition provides an accurate analysis of the data. Consequences for the use of multinomial models in the study of source monitoring are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A simplified conjoint recognition paradigm for the measurement of gist and verbatim memory.

The distinction between verbatim and gist memory traces has furthered the understanding of numerous phenomena in various fields, such as false memory research, research on reasoning and decision making, and cognitive development. To measure verbatim and gist memory empirically, an experimental paradigm and multinomial measurement model has been proposed but rarely applied. In the present article, a simplified conjoint recognition paradigm and multinomial model is introduced and validated as a measurement tool for the separate assessment of verbatim and gist memory processes. A Bayesian metacognitive framework is applied to validate guessing processes. Extensions of the model toward incorporating the processes of phantom recollection and erroneous recollection rejection are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application of Piecewise Hierarchical Linear Growth Modeling to the Study of Continuity in Behavioral Development of Baboons (Papio hamadryas).

In behavioral science, developmental discontinuities are thought to arise when the association between an outcome measure and the underlying process changes over time. Sudden changes in behavior across time are often taken to indicate that a reorganization in the outcome-process relationship may have occurred. The authors proposed in this article the use of piecewise hierarchical linear growth modeling as a statistical methodology to search for discontinuities in behavioral development and illustrated its possibilities by applying 2-piece hierarchical linear models to the study of developmental trajectories of baboon (Papio hamadryas) mothers' behavior during their infants' 1st year of life. The authors provided empirical evidence that piecewise growth modeling can be used to determine whether abrupt changes in development trajectories are tied to changes in the underlying process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application of stochastic modeling to the assessment of group and individual differences in cognitive functioning.

This article begins with a guiding schema of relations among cognitive science, clinical science, and assessment technology. Emphasis is placed on stochastic modeling of cognitive processes. Basic models are adjusted so as to parsimoniously accommodate performance deviations occurring with psychopathology. Modified portions of models indicate functions affected by disorder, whereas portions remaining intact indicate spared functions. Findings from clinical cognitive science are applied to the individual case using Bayesian procedures. Methods are instantiated with respect to cognitive psychopathology of paranoid schizophrenia. The authors address observations and issues arising from this application, including integration of these methods with current assessment practices. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study

This paper addresses the problem of structural health monitoring (SHM) and damage detection based on a statistical model updating methodology which utilizes the measured vibration responses of the structure without any knowledge of the input excitation. The emphasis in this paper is on the application of the proposed methodology in Phase I of the benchmark study set up by the IASC–ASCE Task Group on structural health monitoring. Details of this SHM benchmark study are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm〉. The benchmark study focuses on important issues, such as: (1) measurement noise; (2) modeling error; (3) lack of input measurements; and (4) limited number of sensors. A statistical methodology for model updating is adopted in this paper to establish stiffness reductions due to damage. This methodology allows for an explicit treatment of the measurement noise, modeling error, and possible nonuniqueness issues characterizing this inverse problem. The paper briefly describes the methodology and reports on the results obtained in detecting damage in all six cases of Phase I of the benchmark study assuming unknown (ambient) data. The performance, limitations, and difficulties encountered by the proposed statistical methodology are discussed.     

Multinomial processing tree models and psychological assessment.

Psychological assessment often focuses on individual participants in testing situations. Psychometric models for assessment include parameters for individual and item differences, but they rarely model the cognitive processes involved in responding to test items. Information-processing models of cognition focus on psychological mechanisms; however, they are rarely used in assessment situations. This article discusses a class of information-processing models for categorical data called multinomial processing tree (MPT) models. While MPT models have been developed mostly for experimental situations, there is a largely untapped potential for using them for assessment. Thus, the goal of this article is to discuss how MPT models can be developed into cognitively based psychometric tools. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

System Identification through Model Composition and Stochastic Search

System identification methodologies are useful for identifying characteristics of structural systems using measurement data. However, incorrect systems might be identified when many combinations of system characteristics result in the same predicted responses at measured locations. The reliability of identification is affected by a number of factors that most previous work has overlooked. This paper presents a system identification methodology that explicitly treats factors that affect the success of identification. Rather than simply determining parametric values, this methodology also involves identification of model characteristics including boundary conditions. Due to inevitable modeling errors, models that provide absolute minimum differences between predictions and measurements are rarely correct models. In such situations, the challenge is to define a population of candidate models that result in such differences being below threshold values that are determined by the magnitude of modeling errors. The methodology is illustrated using a case study in civil engineering. This work contributes to providing engineers with general strategies to meet interpretation challenges associated with sensor data.     

The self system in reciprocal determinism.

Notes that explanations of human behavior have generally favored unidirectional causal models emphasizing either environmental or internal determinants of behavior. In social learning theory, causal processes are conceptualized in terms of reciprocal determinism. Viewed from this perspective, psychological functioning involves a continuous reciprocal interaction between behavioral, cognitive, and environmental influences. The major controversies between unidirectional and reciprocal models of human behavior center on the issue of self influences. A self system within the framework of social learning theory comprises cognitive structures and subfunctions for perceiving, evaluating, and regulating behavior, not a psychic agent that controls action. The influential role of the self system in reciprocal determinism is documented through a reciprocal analysis of self-regulatory processes. Reciprocal determinism is proposed as a basic analytic principle for analyzing psychosocial phenomena at the level of intrapersonal development, interpersonal transactions, and interactive functioning of organizational and social systems. (62 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The future of testing: A research agenda for cognitive psychology and psychometrics.

Discusses social and educational demands of the present and future and examines current research in cognitive and developmental psychology that can influence the development of new psychometric methodology. Research on the diagnosis of performance regularities at different levels of learning and development should contribute to assessment measures that provide information useful for instructional decision and guidance. Investigation of the structure and cognitive processes of highly competent performers in various knowledge domains is beginning to suggest techniques for assessing the attainment of increasingly complex levels of skill and understanding. Research on information processing models of aptitude measures and self-monitoring skills is providing an understanding of the abilities for learning tested by intelligence and aptitude tests. Understanding of how these abilities are acquired is a prerequisite to facilitating their acquisition. It is concluded that these advances in the study of cognition should contribute to redressing social concerns about testing and assessment in education. (59 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A measurement theory of illusory conjunctions.

Illusory conjunctions refer to the incorrect perceptual combination of correctly perceived features, such as color and shape. Research on the phenomenon has been hampered by the lack of a measurement theory that accounts for guessing features, as well as the incorrect combination of correctly perceived features. Recently, several investigators have suggested using multinomial models as a tool for measuring feature integration. The authors examined the adequacy of these models in 2 experiments by testing whether model parameters reflect changes in stimulus factors. In a third experiment, confidence ratings were used as a tool for testing the model. Multinomial models accurately reflected both variations in stimulus factors and observers' trial-by-trial confidence ratings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Educational implications of analogy: A view from case-based reasoning.

Case-based reasoning (CBR) focuses on analogy in the context of solving real-world problems. Its research methodology of computational modeling is aimed at deriving hypotheses about cognition. CBR's computational models show the roles of encoding, retrieval, and adaptation in analogical reasoning processes. In addition, its algorithms provide insight into what it might take to enhance human cognition. CBR as a plausible cognitive model can thus advise on educational philosophy, educational practice, and design of educational software. (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.     

Level A in vivo-in vitro correlation: nonlinear models and statistical methodology

Some new nonlinear models for the relationship between the fraction of drug dose dissolved (absorbed) in vivo and that dissolved in vitro are described. The models are empirical in nature and are generalizations of the linear model that, at present, is the most commonly used model. The modeling approach is based on considering the time at which a drug molecule goes into solution (in vitro or in vivo) to be a random variable and relating the distribution functions using proportional odds, proportional hazards, and proportional reversed hazards models. The models are further extended by allowing the parameter that relates in vivo and in vitro to be a function of time. A statistical model for the data is developed and used as the basis for a statistical methodology for fitting these models. The methods are shown to be generalized linear mixed effects model (GLMM) methods. The models are fitted to some data sets, and the results demonstrate that these models have potential.     

Où en est la cognition sociale? Sociogenèse et sélection ontogénétique des pensées sociales.

Maintains that sociogenetic models of human development invoke processes such as intersubjectivity, experiential canalizaion and ontogenetic selection to explain both interindividual differences and personal changes in children's representations of social reality. Studies stressing information processing focus upon procedural aspects of social thought, whereas constructivists emphasize age graded shifts in conceptual abilities. In contrast, socioconstructivists analyze the bi-directional influence between specific interpersonal relations and the evolution of children's cognitive skills. The authors argue that a major theoretical challenge for this field of study involves linking children's sociocognitive capacities to social adaptation in everyday settings. Theoretical and methodological implications of the sociogenetic model for understanding inter-personal and intra-personal diversity in social thought are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comments on Batchelder and Riefer's multinomial model for source monitoring

Kinchla criticizes Batchelder and Riefer's multinomial model for source monitoring, primarily its high-threshold assumptions, and he advocates an approach based on statistical decision theory (SDT). In this reply, the authors lay out some of the considerations that led to their model and then raise some specific concerns with Kinchla's critique. The authors point out that most of his criticisms are drawn from contrasting the high threshold and the Gaussian, equal-variance SDT models on receiver operating characteristics (ROC) curves for yes-no recognition memory. They indicate how source monitoring is more complicated than yes-no recognition and question the validity of standard ROC analyses in source monitoring. The authors argue that their model is a good approximation for measuring differences between sources on old-new detection and that it has the ability to measure source discrimination as well as detection. The authors also explore a low-threshold multinomial model and discuss the application of SDT models to source monitoring.     

A theory of the measurement of knowledge content, access, and learning.

An approach to the measurement of knowledge content, knowledge access, and knowledge learning is developed. This approach has 2 elements: First, a theoretical view of cognition, called the Newell-Dennett framework, is described that is particularly favorable to the development of a measurement approach. A class of measurement models, based on Rasch modeling, is described that is particularly favorable to the development of cognitive theories. Knowledge content and access are viewed as determining the observable actions selected by an agent to achieve desired goals in observable situations. To the degree that models within the theory fit the data at hand, one considers measures of observed behavior to be manifestations of intelligent agents having specific classes of knowledge content and varying degrees of access to that knowledge. Although agents, environment, and knowledge are constitutively defined (in terms of one another), successful application of the theory affords separation of parameters associated with the person from those associated with the environment. Two examples of measurement models developed within the approach are discussed that address the evolution of cognitive skill, strategy choice and application, and developmental changes in mixtures of strategy use. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structural equation modeling in practice: A review and recommended two-step approach.

In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hierarchical diffusion models for two-choice response times.

Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times—the Wiener diffusion process—with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

On measuring (in)dependence of cognitive processes.

The authors provide a critical evaluation of the use of the stochastic independence in psychological research. Specifically, they consider problems of confirming the null hypothesis, power of the statistical test, Simpson's paradox, and between-Ss and within-S correlations. These problems are discussed in the context of research on theories of memory and cognitive development and illustrated with data on reasoning–remembering relationships. The authors conclude that demonstrations of response independence do not, by themselves, provide sufficient grounds for deciding whether a single process or multiple processes is necessary to account for performance. Instead, they argue that formal models are necessary if findings of (in)dependence are to be interpreted meaningfully in terms of underlying theoretical processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measuring memory factors in source monitoring: Reply to Kinchla.

D. A. Kinchla (see record 1994-16291-001) criticizes W. H. Batchelder and D. M. Reifer's multinomial model for source monitoring, primarily its high-threshold assumptions, and advocates an approach based on statistical decision theory (SDT). The authors lay out some of the considerations that led to their model and then raise some specific concerns with Kinchla's critique. The authors point out that most of his criticisms are drawn from contrasting the high threshold and the Gaussian, equal-variance SDT models on receiver operating characteristics (ROC) curves for yes-no recognition memory. The authors indicate how source monitioring is more complicated than yes-no recognition and question the validity of standard ROC analyses in source monitoring. The authors argue that their model is a good approximation for measuring differences between sources on old-new detection and that it has the ability to measure source discrimination as well as detection. The authors also explore a low-threshold multinomial model and discuss the application of SDT models to source monitoring. (PsycINFO Database Record (c) 2010 APA, all rights reserved)