垂直量尺化中的参数标定方法及其性能比较

在使用项目反应理论进行量尺化的语境下, 参数标定方法是垂直量尺化结果的一个至关重要的影响因素。目前的参数标定研究就部分标定方法的相对性能获得了较一致的结果, 就如何实现更佳量尺化也提出了很多新的标定方法。除了在既有框架内继续探索, 以形成完整的研究体系, 未来的研究应结合相关学科, 在深入了解学业增长的性质的基础上探究标定方法的性能, 并应考察特定的标定方法和特定的研究条件、量尺化语境之间的最佳匹配关系。     

熟悉性与回想分离的新证据

人们常常发现自己对某一情景有些熟悉, 但又想不起以前有关的经历。对这种熟悉感的理论解释, 一种观点认为熟悉感来自一种较弱的记忆形式, 与有回忆情况下的再认属于同一加工过程(single-process models, SPM)。另一种观点则认为, 熟悉感来自一种基于熟悉性的再认, 与基于回想的再认相互独立(dual-process theories, DPT)。在实验室, 评估熟悉性与回想是否相互独立的基本方法是寻找两者分离的证据。本文尝试梳理无线索回忆再认(recognition without cued recall, RWCR)范式的研究, 结果显示：熟悉性对某种特殊的概念加工和整体特征的知觉加工要比回想更加敏感, 并没有支持SPM关于回想和熟悉性各自对意义加工和局部特征的知觉加工更加敏感的推断。而且RWCR研究还观测到熟悉性与回想在行为和神经成像方面的实验性分离, 支持了DPT关于熟悉性与回想相互独立的推断。最后, 本文从熟悉性的深层机制和中英文材料对RWCR的不同影响出发, 对未来研究提出展望。     

Trajectories of Parenting Processes and Adolescent Substance Use: Reciprocal Effects

Drawing on transactional theories of child development, we assessed bidirectional links between trajectories of adolescent substance use and parenting processes from early through mid adolescence. Hierarchical generalized models estimated trajectories for 3,317 adolescents from the National Longitudinal Survey of Youth 1997, exploring both between- and within-individual effects. Between individuals, adolescents reporting more regular family activities and greater father and mother knowledge of friends and teachers experienced lower levels of substance use through mid adolescence. Similarly, adolescents with more frequent substance use reported lower family activities, father knowledge, and mother knowledge, though these differences dissipated over time. More conservative within-individual differences indicated a prospective protective effect of family activities, with increases in adolescent participation in family activities predicting later declines in substance use. Results support the central importance of engagement in regular family activities, and suggest the need for further exploration of transactional processes between parents and children in the development of risk behaviors.     

文本加工中情境模型建构的五个维度

情境模型理论是当代西方文本阅读加工的一个重要理论。该文通过详细介绍情境模型建构的空间、时间、因果、主人公、意图五大维度,以助于对情境模型理论整体理解。通过总结可知,目前国内外关于情境模型建构中的空间、因果和主人公三个维度的研究成果较多,研究范式也较为成熟,时间和意图维度则研究成果较少     

Stevens' power law and the problem of meaningfulness

Frequently, it is postulated that the results of a ratio production (resp., ratio estimation) experiment can be summarized by Stevens' power law psi=alphaphi(beta). In the present article, it is argued that the power law parameters depend, among other things, on the standard stimulus presented as a reference point, and the physical stimulus scale by which the physical intensities are measured. To formalize this idea, a new formulation of Stevens' power law is presented. We show that the exponent in Stevens' power law can only be interpreted in a meaningful way if the stimulus scale is a ratio scale. Furthermore, we present empirically testable axioms (termed invertibility and weak multiplicativity) which are both necessary and sufficient for the power law exponent to be invariant under changes of the standard stimulus. Finally, invertibility and weak multiplicativity are evaluated in a ratio production experiment. Ten participants were required to adjust the area of variable circles to prescribed ratio production factors. Both axioms are violated for all participants. The results cast doubts on the well-established practice of comparing power law exponents across different modalities.     

Inferring latent class lexicographic rules from choice data

A lexicographic rule orders multi-attribute alternatives in the same way as a dictionary orders words. Although no utility function can represent lexicographic preference over continuous, real-valued attributes, a constrained linear model suffices for representing such preferences over discrete attributes. We present an algorithm for inferring lexicographic structures from choice data. The primary difficulty in using such data is that it is seldom possible to obtain sufficient information to estimate individual-level preference functions. Instead, one needs to pool the data across latent clusters of individuals. We propose a method that identifies latent clusters of subjects, and estimates a lexicographic rule for each cluster. We describe an application of the method using data collected by a manufacturer of television sets. We compare the predictions of the model with those obtained from a finite-mixture, multinomial-logit model.     

Similarity, kernels, and the triangle inequality

Similarity is used as an explanatory construct throughout psychology and multidimensional scaling (MDS) is the most popular way to assess similarity. In MDS, similarity is intimately connected to the idea of a geometric representation of stimuli in a perceptual space. Whilst connecting similarity and closeness of stimuli in a geometric representation may be intuitively plausible, Tversky and Gati [Tversky, A., & Gati, I. (1982). Similarity, separability, and the triangle inequality. Psychological Review, 89(2), 123-154] have reported data which are inconsistent with the usual geometric representations that are based on segmental additivity. We show that similarity measures based on Shepard’s universal law of generalization [Shepard, R. N. (1987). Toward a universal law of generalization for psychologica science. Science, 237(4820), 1317-1323] lead to an inner product representation in a reproducing kernel Hilbert space. In such a space stimuli are represented by their similarity to all other stimuli. This representation, based on Shepard’s law, has a natural metric that does not have additive segments whilst still retaining the intuitive notion of connecting similarity and distance between stimuli. Furthermore, this representation has the psychologically appealing property that the distance between stimuli is bounded.     

Categories and Dimensions

ABSTRACT— The distinction between categories and dimensions has important consequences for basic and applied science in many areas of psychological research. Decisions as to whether individuals should be assigned to groups or located along one or more continua often are based on personal preferences or discipline-specific measurement traditions, which can lead to the creation, use, or reification of spurious categories or dimensions. Methods for evaluating the latent structure of psychological constructs, using powerful and informative tests between competing models, are available. Rather than choosing on a priori grounds, investigators can perform structural research to evaluate the strength and consistency with which results tease apart categorical and dimensional models. Here, we review why researchers should make this distinction empirically, briefly discuss methods available for doing so, and describe the breadth of areas ripe for exploiting the largely untapped potential of structural research.     

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect averages over many individuals. On the conjecture that the accuracy of the group response is chiefly a consequence of aggregating across individuals, we constructed simple, heuristic approximations to the Bayesian model premised on the hypothesis that individuals have access merely to a sample of k instances drawn from the relevant distribution. The accuracy of the group response reported by Griffiths and Tenenbaum could be accounted for by supposing that individuals each utilize only two instances. Moreover, the variability of the group data is more consistent with this small-sample hypothesis than with the hypothesis that people utilize veridical or nearly veridical representations of the underlying prior distributions. Our analyses lead to a qualitatively different view of how individuals reason from past experience than the view espoused by Griffiths and Tenenbaum.     

Measuring model flexibility with parameter space partitioning: an introduction and application example

A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general-purpose method for analyzing and comparing all classes of cognitive models. PSP is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility.