Colour-cueing in visual search

Several studies have shown that people can selectively attend to stimulus colour, e.g., in visual search, and that preknowledge of a target colour can improve response speed/accuracy. The purpose was to use a form-identification task to determine whether valid colour precues can produce benefits and invalid cues costs. The subject had to identify the orientation of a "T"-shaped element in a ring of randomly-oriented "L"s when either two or four of the elements were differently coloured. Contrary to Moore and Egeth's (1998) recent findings, colour-based attention did affect performance under data-limited conditions: Colour cues produced benefits when processing load was high; when the load was reduced, they incurred only costs. Surprisingly, a valid colour cue succeeded in improving performance in the high-load condition even when its validity was reduced to the chance level. Overall, the results suggest that knowledge of a target colour does not facilitate the processing of the target, but makes it possible to prioritize it.     

Factor copula models for mixed data

We develop factor copula models to analyse the dependence among mixed continuous and discrete responses. Factor copula models are canonical vine copulas that involve both observed and latent variables, hence they allow tail, asymmetric and nonlinear dependence. They can be explained as conditional independence models with latent variables that do not necessarily have an additive latent structure. We focus on important issues of interest to the social data analyst, such as model selection and goodness of fit. Our general methodology is demonstrated with an extensive simulation study and illustrated by reanalysing three mixed response data sets. Our studies suggest that there can be a substantial improvement over the standard factor model for mixed data and make the argument for moving to factor copula models.     

Some Behaviorial Science Measurement Concerns and Proposals

Primarily from a measurement standpoint, we question some basic beliefs and procedures characterizing the scientific study of human behavior. The relations between observed and unobserved variables are key to an empirical approach to building explanatory theories and we are especially concerned about how the former are used as proxies for the latter. We believe that behavioral science can profitably reconsider the prevailing version of this arrangement because of its vulnerability to limiting idiosyncratic aspects of observed/unobserved variable relations. We describe a general measurement approach that takes into account idiosyncrasies that should be irrelevant to the measurement process but can intrude and may invalidate it in ways that distort and weaken relations among theoretically important variables. To clarify further our major concerns, we briefly describe one version of the measurement approach that fundamentally supports the individual as the primary unit of analysis orientation that we believe should be preeminent in the scientific study of human behavior.     

Cooperative responding in rats maintained by fixed‐ and variable‐ratio schedules

The present study investigated the effects of fixed‐ratio (FR) and variable‐ratio (VR) reinforcement schedules on patterns of cooperative responding in pairs of rats. Experiment 1 arranged FR 1, FR 10, and VR 10 schedules to establish cooperative responding (water delivery depended on the joint responding of two rats). Cooperative response rates and proportions were higher under intermittent schedules than under continuous reinforcement. The FR 10 schedule generated a break‐and‐run pattern, whereas the VR 10 schedule generated a relatively high and constant rate pattern. Experiment 2 evaluated the effects of parametric manipulations of FR and VR schedules on cooperative responding. Rates and proportions of cooperative responding generally increased between ratio sizes of 1 and 5 but showed no consistent trend as the ratio increased from 5 to 10. Experiment 3 contrasted cooperative responding between an FR6 schedule and a yoked control schedule. Coordinated behavior occurred at a higher rate under the former schedule. The present study showed that external consequences and the schedules under which the delivery of these consequences are based, select patterns of coordinated behavior.     

A new variable interval schedule with constant hazard rate and finite time range

We propose a new variable interval (VI) schedule that achieves constant probability of reinforcement in time while using a bounded range of intervals. By sampling each trial duration from a uniform distribution ranging from 0 to 2 T seconds, and then applying a reinforcement rule that depends linearly on trial duration, the schedule alternates reinforced and unreinforced trials, each less than 2 T seconds, while preserving a constant hazard function.     

An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models

In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models—the Ising model from physics—and one of the most important latent variable models—the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.     

认知加工速度研究中常用的实验和统计方法

认知加工速度理论是解释年龄与认知老年化现象的理论之一,也是近年来心理学研究中的热点之一.该文从理论要点、实验设计、指标选择和统计分析手段四个方面对认知加工速度的研究进行了回顾.在阐述理论要点的基础上,介绍了常用的实验指标和选择指标的标准,以及一些常用的统计分析手段.作者认为除了深入理解理论之外,谨慎地设计实验方案、选择适当的实验指标,以及采用恰当的统计手段都是研究得以顺利进行的必要保障.     

A real‐world MCDA application: evaluating software

A real‐world multi‐criteria decision aid (MCDA) application on software evaluation is presented in this paper. The decision process concerned a big Italian company faced with the management of a call for tenders for a very important software acquisition. The decision aiding process is extensively presented and discussed, mainly as far as its products are concerned, i.e. –the problem situation; –the problem formulation; –the evaluation model; –the final recommendation. The results of the experience are discussed using the comments of the client of the study. Copyright © 2000 John Wiley & Sons, Ltd.     

Factor analysis with (mixed) observed and latent variables in the exponential family

We develop a general approach to factor analysis that involves observed and latent variables that are assumed to be distributed in the exponential family. This gives rise to a number of factor models not considered previously and enables the study of latent variables in an integrated methodological framework, rather than as a collection of seemingly unrelated special cases. The framework accommodates a great variety of different measurement scales and accommodates cases where different latent variables have different distributions. The models are estimated with the method of simulated likelihood, which allows for higher dimensional factor solutions to be estimated than heretofore. The models are illustrated on synthetic data. We investigate their performance when the distribution of the latent variables is mis-specified and when part of the observations are missing. We study the properties of the simulation estimators relative to maximum likelihood estimation with numerical integration. We provide an empirical application to the analysis of attitudes.     

Item Randomized-Response Models for Measuring Noncompliance: Risk-Return Perceptions,Social Influences,and Self-Protective Responses

Randomized response (RR) is a well-known method for measuring sensitive behavior. Yet this method is not often applied because: (i) of its lower efficiency and the resulting need for larger sample sizes which make applications of RR costly; (ii) despite its privacy-protection mechanism the RR design may not be followed by every respondent; and (iii) the incorrect belief that RR yields estimates only of aggregate-level behavior but that these estimates cannot be linked to individual-level covariates. This paper addresses the efficiency problem by applying item randomized-response (IRR) models for the analysis of multivariate RR data. In these models, a person parameter is estimated based on multiple measures of a sensitive behavior under study which allow for more powerful analyses of individual differences than available from univariate RR data. Response behavior that does not follow the RR design is approached by introducing mixture components in the IRR models with one component consisting of respondents who answer truthfully and another component consisting of respondents who do not provide truthful responses. An analysis of data from two large-scale Dutch surveys conducted among recipients of invalidity insurance benefits shows that the willingness of a respondent to answer truthfully is related to the educational level of the respondents and the perceived clarity of the instructions. A person is more willing to comply when the expected benefits of noncompliance are minor and social control is strong. The authors are grateful to the reviewers whose suggestions helped to improve the clarity of the paper substantially. The authors also wish to thank the Dutch Ministry of Social Affairs and Employment for making the reported data available. This research was supported in parts by grants from the Social Sciences and Humanities Research Council of Canada and the Canadian Foundation of Innovation.