On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

Some observations on the application of frame theory to the psychoanalytic situation and process

Frame theory can contribute to our understanding of psychoanalytic work in ways compatible with established psychoanalytic theory. Its concepts and metaphor convey the relativism of the psychic realities both parties bring to the analytic situation, as well as the multiple levels of transference that become framed upon the relationship. Its linkages to game theory stress the power of play and illusion in transference actualization and working through. The importance of rules and conventions for all three theories, and for the ability to do analytic work, suggests their essential grounding in the child's capacities for play, particularly those capabilities elaborated in latency following oedipal conflict resolution.     

Integrating analogical mapping and general problem solving: the path-mapping theory

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects' incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.     

A general first-order solution to the ramification problem with cycles

We provide a solution to the ramification problem that integrates findings of different axiomatic approaches to ramification from the last ten to fifteen years. For the first time, we present a solution that: (1) is independent of a particular time structure, (2) is formulated in classical first-order logic, (3) treats cycles – a notoriously difficult aspect – properly, and (4) is assessed against a state-transition semantics via a formal correctness proof.This is achieved as follows: We introduce indirect effect laws that enable us to specify ramifications that are triggered by activation of a formula rather than just an atomic effect. We characterise the intended models of these indirect effect laws by a state-transition semantics. Afterwards, we show how to compile a class of indirect effect laws into first-order effect axioms that then solve the ramification and frame problems. We finally prove the resulting effect axioms sound and complete with respect to the semantics defined earlier.     

An introduction to Bayesian hierarchical models with an application in the theory of signal detection

Although many nonlinear models of cognition have been proposed in the past 50 years, there has been little consideration of corresponding statistical techniques for their analysis. In analyses with nonlinear models, unmodeled variability from the selection of items or participants may lead to asymptotically biased estimation. This asymptotic bias, in turn, renders inference problematic. We show, for example, that a signal detection analysis of recognition memory data leads to asymptotic underestimation of sensitivity. To eliminate asymptotic bias, we advocate hierarchical models in which participant variability, item variability, and measurement error are modeled simultaneously. By accounting for multiple sources of variability, hierarchical models yield consistent and accurate estimates of participant and item effects in recognition memory. This article is written in tutorial format; we provide an introduction to Bayesian statistics, hierarchical modeling, and Markov chain Monte Carlo computational techniques.     

Applying global workspace theory to the frame problem

The subject of this article is the frame problem, as conceived by certain cognitive scientists and philosophers of mind, notably Fodor for whom it stands as a fundamental obstacle to progress in cognitive science. The challenge is to explain the capacity of so-called informationally unencapsulated cognitive processes to deal effectively with information from potentially any cognitive domain without the burden of having to explicitly sift the relevant from the irrelevant. The paper advocates a global workspace architecture, with its ability to manage massively parallel resources in the context of a serial thread of computation, as an answer to this challenge. Analogical reasoning is given particular attention, since it exemplifies informational unencapsulation in its most extreme form. Because global workspace theory also purports to account for the distinction between conscious and unconscious information processing, the paper advances the tentative conclusion that consciousness may go hand-in-hand with a solution to the frame problem in the biological brain.     

A characterization of stochastic independence by association,with an application to random utility theory

A characterization of stochastic independence in terms of association of random variables is given. The result is applied to yield a simple proof of the Sattath-Tversky inequality without continuity assumptions.     

A new rank correlation coefficient with application to the consensus ranking problem

The consensus ranking problem has received much attention in the statistical literature. Given m rankings of n objects the objective is to determine a consensus ranking. The input rankings may contain ties, be incomplete, and may be weighted. Two solution concepts are discussed, the first maximizing the average weighted rank correlation of the solution ranking with the input rankings and the second minimizing the average weighted Kemeny–Snell distance. A new rank correlation coefficient called τ_x is presented which is shown to be the unique rank correlation coefficient which is equivalent to the Kemeny‐Snell distance metric. The new rank correlation coefficient is closely related to Kendall's tau but differs from it in the way ties are handled. It will be demonstrated that Kendall's τ_b is flawed as a measure of agreement between weak orderings and should no longer be used as a rank correlation coefficient. The use of τ_x in the consensus ranking problem provides a more mathematically tractable solution than the Kemeny–Snell distance metric because all the ranking information can be summarized in a single matrix. The methods described in this paper allow analysts to accommodate the fully general consensus ranking problem with weights, ties, and partial inputs. Copyright © 2002 John Wiley & Sons, Ltd.     

Drivers' compliance with speed limits: an application of the theory of planned behavior

The theory of planned behavior (TPB; I. Ajzen, 1985) was applied to drivers' compliance with speed limits. Questionnaire data were collected for 598 drivers at 2 time points separated by 3 months. TPB variables, demographic information, and self-reported prior behavior were measured at Time 1, and self-reported subsequent behavior was measured at Time 2. In line with the TPB, attitude, subjective norm, and perceived control were positively associated with behavioral intention, and intention and perceived control were positively associated with subsequent behavior. TPB variables mediated the effects of age and gender on behavior. Prior behavior was found to moderate the perceived control-intention and perceived control-subsequent behavior relationships. Practical implications of the findings for road safety and possible avenues for further research are discussed.     

Promoting drivers' compliance with speed limits: Testing an intervention based on the theory of planned behaviour

The efficacy of a theory of planned behaviour (TPB)‐based intervention to promote drivers' compliance with speed limits was tested. Participants (N=300) were randomly assigned to an experimental condition, and received persuasive messages designed to change beliefs as specified in the TPB, or a control condition. Baseline and follow‐up (1 month post‐baseline) measures of TPB variables and behaviour were collected using postal questionnaires. Results showed that the intervention had a significant effect on one control belief, and significantly increased perceived behavioural control and reported behaviour. Mediation analyses confirmed that the control belief change generated the perceived behavioural control change and that the perceived behavioural control change generated the behaviour change. Implications for promoting road safety are discussed.     

Dogs learn to solve the support problem based on perceptual cues

Numerous recent studies have investigated how animals solve means-end tasks and unraveled considerable variation in strategies used by different species. Domestic dogs (Canis familiaris) have typically performed comparably poorly in physical cognition tasks, but a recent study showed that they can solve the on–off condition of the support problem, where they are confronted with two boards, one with a reward placed on it and the other with a reward placed next to it. To explore which strategies dogs use to solve this task, we first tested 37 dogs with the on–off condition tested previously and then tested subjects that passed this condition with three transfer tasks. For the contact condition, the inaccessible reward was touching the second board. For the perceptual containment condition, the inaccessible reward was surrounded on three sides by the second board, but not supported by it, whereas for the gap condition, discontinuous boards were used. Unlike in the previous study, our subjects did not perform above chance level in the initial trials of the on–off condition, but 13 subjects learned to solve it. Their performance in the transfer tasks suggests that dogs can learn to solve the support problem based on perceptual cues, that they can quickly adopt new cues when old ones become unreliable, but also that some apparently inherent preferences are hard to overcome. Our study contributes to accumulating evidence demonstrating that animals typically rely on a variety of perceptual cues to solve physical cognition tasks, without developing an understanding of the underlying causal structure.     

PEAK关系训练系统：孤独症语言障碍康复的新方法

PEAK (Promoting the Emergence of Advanced Knowledge, 促进高阶知识涌现)关系训练系统是全球首个同时整合了斯金纳的《言语行为》和后斯金纳主义的“关系框架理论”, 促进孤独症谱系障碍(Autism spectrum disorders, ASD)患者的语言、学习、社交等核心技能发展的语言行为评估训练系统。截至2018年底, PEAK关系训练系统由以下4个模块组成：直接训练模块、泛化模块、等价关系模块、功能转化模块。每一模块依据难易程度分别设置了184个目标能力的评估方法及训练课程。已发表的多项实证研究表明, PEAK关系训练系统打破了《语言行为里程碑评估及安置程序》(Verbal Behavior Milestones and Placement Program, VB-MAPP) 对ASD患者进行里程碑评估时出现的“天花板效应”, 有望比VB-MAPP提供更全面、高阶的语言行为评估体系。此外, PEAK关系训练系统表现出的评估工具的信效度良好、多项效果研究显著、实践中易操作等特性, 使其不仅适用于专业人员教学, 未来在ASD患者家庭干预模式中也有潜在的应用价值。