Display size effects in visual search: Analyses of reaction time distributions as mixtures

In a reanalysis of data from Cousineau and Shiffrin (2004) and two new visual search experiments, we used a likelihood ratio test to examine the full distributions of reaction time (RT) for evidence that the display size effect is a mixture-type effect that occurs on only a proportion of trials, leaving RT in the remaining trials unaffected, as is predicted by serial self-terminating search models. Experiment 1 was a reanalysis of Cousineau and Shiffrin's data, for which a mixture effect had previously been established by a bimodal distribution of RTs, and the results confirmed that the likelihood ratio test could also detect this mixture. Experiment 2 applied the likelihood ratio test within a more standard visual search task with a relatively easy target/distractor discrimination, and Experiment 3 applied it within a target identification search task within the same types of stimuli. Neither of these experiments provided any evidence for the mixture-type display size effect predicted by serial self-terminating search models. Overall, these results suggest that serial self-terminating search models may generally be applicable only with relatively difficult target/distractor discriminations, and then only for some participants. In addition, they further illustrate the utility of analysing full RT distributions in addition to mean RT.     

The effect of information distribution on collaborative inhibition

Two experiments examined collaborative memory for information that was studied by all group members (shared items) and information that was studied by only a subset of group members (unshared items). In both experiments significant collaborative inhibition (reduced output of the collaborative groups relative to the pooled output of individuals) was obtained for both shared and unshared information. In Experiment 1 the magnitude of collaborative inhibition was larger for unshared items than for shared items, possibly because unshared items were less likely to be acknowledged and thus incorporated into the groups’ recall. In Experiment 2 the magnitude of collaborative inhibition for shared and unshared information was equivalent once all participants were provided with the category name associated with the shared and unshared items. The results of the experiments are discussed in relation to the retrieval strategy disruption hypothesis of collaborative inhibition and the role of social process variables, such as acknowledgement, in influencing collaborative inhibition across situations involving memory of shared and unshared information.     

A Bayesian psychophysical model for angular variables

Bayesian theories of perception provide a link between observed response distributions and theoretical constructs from Bayesian decision theory. Using Bayesian psychophysics we derive response distributions for two cases, one based on a normal distribution and one on a von Mises distribution for angular variables. Interestingly, where the theoretical response distribution is always unimodal in the case of normal distributions, it can become bimodal in the angular setting in the case when prior and likelihood are about equally strong.     

The boundaries of self face perception: Response time distributions,perceptual categories,and decision weighting

The current study uses an RT distribution analysis approach to examine the self-bias in face categorization. In two experiments we systematically manipulated the decision boundaries between the self, familiar, and unfamiliar others in face categorization tasks. For the first time in studies of self-categorization we estimated parameters from ex-Gaussian fits of reaction time distributions in order to capture the influence of different boundaries on the latency distribution of the categorization responses. The results showed that the distribution of responses to self faces was stable regardless of the face context and the task demands, and changes in context only shifted the response distribution in time. In contrast, varying the decisions with familiar and unfamiliar faces changed the shape of the RT distributions in addition to shifting RTs in time. The results indicate that, in contrast to our perception of familiar and unfamiliar faces of other people, self face perception forms a unique perceptual category unaffected by shifts in context and task demands.     

Circular interpretation of regression coefficients

The interpretation of the effect of predictors in projected normal regression models is not straight-forward. The main aim of this paper is to make this interpretation easier such that these models can be employed more readily by social scientific researchers. We introduce three new measures: the slope at the inflection point (b_c), average slope (AS) and slope at mean (SAM) that help us assess the marginal effect of a predictor in a Bayesian projected normal regression model. The SAM or AS are preferably used in situations where the data for a specific predictor do not lie close to the inflection point of a circular regression curve. In this case b_c is an unstable and extrapolated effect. In addition, we outline how the projected normal regression model allows us to distinguish between an effect on the mean and spread of a circular outcome variable. We call these types of effects location and accuracy effects, respectively. The performance of the three new measures and of the methods to distinguish between location and accuracy effects is investigated in a simulation study. We conclude that the new measures and methods to distinguish between accuracy and location effects work well in situations with a clear location effect. In situations where the location effect is not clearly distinguishable from an accuracy effect not all measures work equally well and we recommend the use of the SAM.     

非正态分布测量数据对克隆巴赫信度α系数的影响

对“增大被试群体异质性,能提高测验信度”观点提出质疑。讨论非正态分布测量数据的偏度对克隆巴赫信度α系数的影响,以模拟方法验证Box—COX正态化变换对信度的高估现象,进而给出对克隆巴赫信度α系数估计的改进方法。     

编制中国人个性测量表(CPAI)的意义与程序

从1990年开始中国科学院心理研究所与香港中文大学心理系合作,编制“中国人个性测量表”(CPAI)。编制方法严格按照个性测验编制程序进行。共分四个步骤,一、建立正常与病态个性量表的框架。二、构成量表的项目。三、测量表的最后确定。四、量表的标准化工作。编制出的测量表共包括22个正常个性量表,12个病态个性量表及2个效度量表,共510个项目。经实践检验与统计分析,证明该测量表具有一定的信度与效度。     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

An approach to combine the logistic threshold model of psychophysics with the Bradley-Terry-Luce models of choice theory

The paper presents a straightforward extension of the Bradley-Terry-Luce model (BTL model) that can be derived from the logistic threshold model of psychophysics which assumes that psychometric functions are logistic probability functions. It is shown that (under weak side conditions) the logistic threshold model is a submodel of the extended BTL model. Moreover, representation and uniqueness theorems are proven that provide some evidence that the extended BTL model is a useful and widely applicable generalization of the ordinary BTL model. Finally, the logistic shape of the psychometric function is derived from axioms about binary choice probabilities. This characterization of the logistic threshold model can replace goodness of fit tests for the logistic probability distribution.