Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

How to, and how not to, bridge computational cognitive neuroscience and Husserlian phenomenology of time consciousness

A number of recent attempts to bridge Husserlian phenomenology of time consciousness and contemporary tools and results from cognitive science or computational neuroscience are described and critiqued. An alternate proposal is outlined that lacks the weaknesses of existing accounts.     

Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning

Learning environmental biases is a rational behavior: by using prior odds, Bayesian networks rapidly became a benchmark in machine learning. Moreover, a growing body of evidence now suggests that humans are using base rate information. Unsupervised connectionist networks are used in computer science for machine learning and in psychology to model human cognition, but it is unclear whether they are sensitive to prior odds. In this paper, we show that hard competitive learners are unable to use environmental biases while recurrent associative memories use frequency of exemplars and categories independently. Hence, it is concluded that recurrent associative memories are more useful than hard competitive networks to model human cognition and have a higher potential in machine learning.     

Empirical underidentification in estimating random utility models: The role of choice sets and standardizations

A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.     

Verbal Sexual Coercion and Perceived Victim Responsibility: Mediating Effects of Perceived Control

Verbal coercion involves unwanted sexual penetration compelled by psychological pressure. It was hypothesized that, to the degree that verbal coercion is seen as controllable, victims may be held more responsible. Two samples of US undergraduates rated perceptions of hypothetical sexual coercion scenarios. In Study 1, participants (N = 120) read scenarios of either verbal coercion or rape by a male dating partner. Participants perceived verbal coercion as more controllable than rape, which accounted for the greater responsibility attributed to verbal coercion victims. In Study 2, participants (N = 275) read scenarios involving either male-to-female or female-to-male verbal coercion. Participants perceived female-to-male coercion as more controllable, which accounted for the greater responsibility attributed to a verbally coerced man.     

The effects of verbal information on children's fear beliefs about social situations

Two experiments explored the role of verbal information in changing children's fear-related beliefs about social situations. In Experiment 1, 118 6- to 8- and 12- to 13-year-olds heard positive, negative, or no information about individuals' experiences of three social situations. Fear beliefs regarding each situation were assessed before and after this manipulation. Verbal information had no significant influence on children's fear beliefs. In Experiment 2, the same paradigm was used with 80 12- to 13-year-olds, but the information took the form of multiple attitude statements about the situations expressed by groups of peers, older children, or adults. An affective priming task of implicit attitudes was used to complement the explicit questions about fear beliefs. Negative information influenced both explicit and implicit fear beliefs. The source of information and the child's own social anxiety did not moderate these effects. Implications for our understanding of the socialisation of childhood fears are discussed.     

Dysfunctional implications of narrow window theory: variability in the intuitive assessment of correlation

Narrow Window theory, suggested by Y. Kareev ten years ago, has so far focused on one central implication of the limited capacity of working memory on intuitive correlation estimation, namely, overestimation of the distal population correlation. This paper points to additional and perhaps more dramatic implications due to the large dispersion of intuitive estimates: (a) large estimation errors, possibly causing overestimation of negligible rhos, misses of strong rhos, and distorted hierarchies of the rhos between different pairs of variables; and (b) large interpersonal differences in the estimation of any given rho and highly incongruent hierarchies of estimated correlations between different pairs of variables. These implications impede both individuals' adaptation to the empirical world and communication among themselves.     

儿童数字估计的表征模式与发展

探讨中国儿童数字估计的表征模式与发展趋势。包括两个实验,均采用数字线估计任务,实验一以92名幼儿园、一年级及二年级儿童为被试,考察其在0~100范围的数字估计,结果显示,幼儿园儿童在数字估计更多地采用对数表征,而一二年级的儿童在数字估计中更多地采用线性表征;实验二以86名一、三、五年级儿童为被试,考察其在0~1000范围的数字估计,结果显示,一年级儿童有一半采用对数表征,另一半采用线性表征,而三五年级儿童大多采用线性表征。中国儿童的数字估计表现出与美国儿童相同的发展模式,都是由不精确的对数表征逐步向精确的线性表征发展;人的数表征有多种形式,即使在同一年龄阶段,也会因任务难度的不同而选择不同的表征模式。中国儿童精确数字估计能力的出现要早于美国儿童。     

时距估计任务中的间断期望效应

使用单任务研究程序,采用引入提示线索的方法,以产生时距作为反应指标对存在间断的时距估计任务中的间断期望效应和提示线索效应（注意效应）进行系统考察,并对间断时距的效应、产生时距与等待时距的关系问题作出进一步探讨。结果表明,间断位置（等待时距）因素是被试时间判断的主要线索,被试的产生时距随着等待时距的增加而延长。间断实验中表现出极其显著的提示线索效应,此效应既增加了时距估计的变异,又延长了被试的时距估计。无间断实验条件下,被试表现出显著的间断期望效应,被试对间断的期望有损于时间估计。