Developing new treatments: on the interplay between theories, experimental science and clinical innovation

It is often argued that behaviour therapy and cognitive-behaviour therapy have a sound theoretical and experimental basis. In the early days of behaviour therapy, the learning theory accounts that were the basis of treatment made clear suggestions about the procedures that were likely to be effective in treatment. In contrast, more recent cognitive-behavioural models tend to specify targets for therapy, but not the procedures that might be optimal for changing the targets. As a consequence, a considerable amount of work has to be done in order to create an effective cognitive-behavioural treatment from a promising cognitive-behavioural model. The process by which cognitive-behavioural treatments are developed is rarely discussed in the literature. For this reason, the way in which one group has used a mixture of phenomenological, experimental and treatment development studies to create effective cognitive therapy programmes for anxiety disorders is described.     

Evolutionary preference/utility functions: A dynamic perspective

The collection of repeated measures in psychological research is one of the most common data collection formats employed in survey and experimental research. The behavioral decision theory literature documents the existence of the dynamic evolution of preferences that occur over time and experience due to learning, exposure to additional information, fatigue, cognitive storage limitations, etc. We introduce a Bayesian dynamic linear methodology employing an empirical Bayes estimation framework that permits the detection and modeling of such potential changes to the underlying preference utility structure of the respondent. An illustration of revealed stated preference analysis (i.e., conjoint analysis) is given involving students’ preferences for apartments and their underlying attributes and features. We also present the results of several simulations demonstrating the ability of the proposed procedure to recover a variety of different sources of dynamics that may surface with preference elicitation over repeated sequential measurement. Finally, directions for future research are discussed.The authors wish to acknowledge and thank the Editor, the Associate Editor, and two anonymous reviewers for their constructive and insightful comments. Duncan K.H. Fong’s work was sponsored in part by a research grant from the Smeal College.This revised article was published online in August 2005 with the PDF paginated correctly.     

分类中相似性的理论与模型

相似性在分类的原型理论、样例理论、定义理论和理论解释观中都扮演着重要的角色。人们对相似性的研究由来已久,但是它在分类的领域中至今仍是一个相对模糊的概念,这部分地由于揭示相似性的真正机制将涉及到复杂的信息加工过程。本文以分类中的相似性为出发点介绍了近期相似性研究的一些理论与模型并在此基础上对概念和分类领域中的相似性研究进行了分析和总结。     

Probabilistic Subset Conjunction

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only main effects or only interaction effects. This results in a confounding of the main and interaction effects when there is little or no response error. With greater response error, a logistic regression, even if it gives a good fit to data, can produce parameter estimates that do not reflect the underlying decision process. The authors propose a model in which the binary classification of alternatives into acceptable/unacceptable categories is based on a probabilistic implementation of a subset-conjunctive process. The satisfaction of decision criteria biases the odds toward one outcome or the other. The authors then describe a two-stage choice model in which a (possibly large) set of alternatives is first reduced using a subset-conjunctive rule, after which an alternative is selected from this reduced set of items. They describe methods for estimating the unobserved consideration probabilities from classification and choice data, and illustrate the use of the models for cancer diagnosis and consumer choice. They report the results of simulations investigating estimation accuracy, incidence of local optima, and model fit. The authors thank the Editor, the Associate Editor, and three anonymous reviewers for their constructive suggestions, and also thank Asim Ansari and Raghuram Iyengar for their helpful comments. They also thank Sawtooth Software, McKinsey and Company, and Intelliquest for providing the PC choice data, and the University of Wisconsin for making the breast-cancer data available at the machine learning archives.     

Distinguishing Between Causes and Enabling Conditions—Through Mental Models or Linguistic Cues?

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson-Laird, 2001, Cognitive Science, 25, 565–610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that frame which event to assume as given (the enabling condition) and which to consider as responsible for the effect under this assumption (the cause). By disentangling these two factors, we were able to show that when identifying causes and enabling conditions in these tasks, people rely strongly on the linguistic cues but not on the proposed set of models and that this set of models does not even reflect people's typical interpretation of the tasks. We propose an alternative explanation that integrates syntactic and causal considerations.     

Acquiring new spatial intuitions: learning to reason about rotations

There are certain simple rotations of objects that most people cannot reason about accurately. Reliable gaps in the understanding of a fundamental physical domain raise the question of how learning to reason in that domain might proceed. Using virtual reality techniques, this project investigated the nature of learning to reason across the domain of simple rotations. Learning consisted of the acquisition of spatial intuitions: there was encoding of useful spatiotemporal information in specific problem types and a gradual accumulation of this understanding across the domain. This pattern of learning through the accumulation of intuitions is especially interesting for rotational motion, in which an elegant domain-wide kinematics is available to support insightful learning. Individual ability to reason about rotations correlated highly with mastery motivation, skill in fluid reasoning, and skill in reasoning about spatial transformations. Thus, general cognitive advantages aided the understanding of individual rotations without guaranteeing immediate generalization across the domain.     

Meaning and Method in the Social Sciences<Superscript>1</Superscript>

Academia’s mathematical metaphysics are briefly explored en route to an elaboration of the qualitatively rigorous requirements underpinning the calibration and unambiguous interpretation of quantitative instrumentation in any science. Of particular interest are Gadamer’s emphases on number as the paradigm of the noetic, on the role of play in interpretation, and on Hegel’s sense of method as the activity of the thing itself that thought experiences. These point toward and overlap with (1) Latour’s study of the metrological social networks through which technological phenomena are brought into language as modes of being that can be understood, and (2) the way that Rasch’s models for measurement comprise a potential beginning for metaphysically astute, qualitatively and quantitatively integrated, mathematical methods in the social sciences. The paper closes with observations on the general problem that is philosophy, the need to remain open to multiplicities of meaning even as clear understandings are sought and obtained.