Review of Evnine,Simon J., Epistemic Dimensions of Personhood,New York: Oxford University Press, 2008, pp. viii + 176, £32.50 (cloth)

The different occurrences of a word in a sentence cannot be identified with the one word type, nor with its many tokens. What then are occurrences of a word? How can one type occur more than once in another type? Is the conception of ‘structural universals’ that leads to these questions incoherent, as Lewis maintained? I argue against the answer Wetzel suggested, which identifies sentences with functions from numbers to expressions, and propose instead that occurrences of one type in another are subtypes with different relative positions in the whole. Types have subtypes as well as tokens. Occurrences in non-symmetrical types like sequences are individuated by their relative positions in the type. Occurrences in a symmetrical whole have different positions but stand in the same relations to the other components. They are numerically different subtypes that do not differ from each other qualitatively.     

Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies

In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.     

A Jungian contribution to a dynamic systems understanding of disorganized attachment

This panel emerged from shared clinical concerns when working with adult patients whose presentation style was reminiscent of a disorganized (Type D) infant attachment pattern. Psychotherapeutic work with such patients poses complicated transference and countertransference dilemmas which are addressed by all four panellists via theory and clinical vignettes. In common is an interest in contemporary attachment, neuroscience and trauma theories and their relationship to analytical psychology. Intergenerational trauma seems to be a salient factor in the evolution of fragmented and fragmenting interactions that lead to failures in self-coherence and healthy interpersonal relationships. Such early relational trauma is compounded by further episodes of abuse and neglect leading to failure in a core sense of self. These clinicians share how they have integrated theory and practice in order to help dissociated and disorganized patients to transform their dark and extraordinary suffering through implicit and explicit experiences with the analyst into new, life giving patterns of relationship with self and others. The alchemy of transformation, both positive and negative, is evident in the case material presented.     

A phase transition model for the speed-accuracy trade-off in response time experiments

Most models of response time (RT) in elementary cognitive tasks implicitly assume that the speed-accuracy trade-off is continuous: When payoffs or instructions gradually increase the level of speed stress, people are assumed to gradually sacrifice response accuracy in exchange for gradual increases in response speed. This trade-off presumably operates over the entire range from accurate but slow responding to fast but chance-level responding (i.e., guessing). In this article, we challenge the assumption of continuity and propose a phase transition model for RTs and accuracy. Analogous to the fast guess model (Ollman, 1966), our model postulates two modes of processing: a guess mode and a stimulus-controlled mode. From catastrophe theory, we derive two important predictions that allow us to test our model against the fast guess model and against the popular class of sequential sampling models. The first prediction--hysteresis in the transitions between guessing and stimulus-controlled behavior--was confirmed in an experiment that gradually changed the reward for speed versus accuracy. The second prediction--bimodal RT distributions--was confirmed in an experiment that required participants to respond in a way that is intermediate between guessing and accurate responding.     

On the sampling theory roundations of item response theory models

Item response theory (IT) models are now in common use for the analysis of dichotomous item responses. This paper examines the sampling theory foundations for statistical inference in these models. The discussion includes: some history on the stochastic subject versus the random sampling interpretations of the probability in IRT models; the relationship between three versions of maximum likelihood estimation for IRT models; estimating versus estimating -predictors; IRT models and loglinear models; the identifiability of IRT models; and the role of robustness and Bayesian statistics from the sampling theory perspective.A presidential address can serve many different functions. This one is a report of investigations I started at least ten years ago to understand what IRT was all about. It is a decidedly one-sided view, but I hope it stimulates controversy and further research. I have profited from discussions of this material with many people including: Brian Junker, Charles Lewis, Nicholas Longford, Robert Mislevy, Ivo Molenaar, Donald Rock, Donald Rubin, Lynne Steinberg, Martha Stocking, William Stout, Dorothy Thayer, David Thissen, Wim van der Linden, Howard Wainer, and Marilyn Wingersky. Of course, none of them is responsible for any errors or misstatements in this paper. The research was supported in part by the Cognitive Science Program, Office of Naval Research under Contract No. Nooo14-87-K-0730 and by the Program Statistics Research Project of Educational Testing Service.     

What Are We Doing When We Translate from Quantitative Models?

Although quantitative analysis (in which behavior principles are defined in terms of equations) has become common in basic behavior analysis, translational efforts often examine everyday events through the lens of narrative versions of laboratory-derived principles. This approach to translation, although useful, is incomplete because equations may convey concepts that are difficult to capture in words. To support this point, we provide a nontechnical introduction to selected aspects of quantitative analysis; consider some issues that translational investigators (and, potentially, practitioners) confront when attempting to translate from quantitative models; and discuss examples of relevant translational studies. We conclude that, where behavior-science translation is concerned, the quantitative features of quantitative models cannot be ignored without sacrificing conceptual precision, scientific and practical insights, and the capacity of the basic and applied wings of behavior analysis to communicate effectively.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

公平感对社会认知的影响:经济因素的调节作用

目的及方法:基于中国综合社会调查2010年数据,采用多层结构方程模型,探究中国民众公平感对社会认知的作用,及经济因素在其中的调节效应。研究结果:第一,公平感越高,则社会冲突感越低,外在政治效能感越高,越倾向于对社会不平等进行内部归因,即对于社会的整体认知更加积极; 第二,经济因素能调节公平感与社会不平等内归因的关系,具体而言,经济水平越低,则公平感对于社会不平等的内部归因正向预测作用越大。     

Decomposition,Interdependence and Precision in Multiattribute Utility Measurements

Traditionally, parameters of multiattribute utility models, representing a decision maker's preference judgements, are treated deterministically. This may be unrealistic, because assessment of such parameters is potentially fraught with imprecisions and errors. We thus treat such parameters as stochastic and investigate how their associated imprecision/errors are propagated in an additive multiattribute utility function in terms of the aggregate variance. Both a no information and a rank order case regarding the attribute weights are considered, assuming a uniform distribution over the feasible region of attribute weights constrained by the respective information assumption. In general, as the number of attributes increases, the variance of the aggregate utility in both cases decreases and approaches the same limit, which depends only on the variances as well as the correlations among the single-attribute utilities. However, the marginal change in aggregate utility variance decreases rather rapidly and hence decomposition as a variance reduction mechanism is generally useful but becomes relatively ineffective if the number of attributes exceed about 10. Moreover, it was found that utilities which are positively correlated increase the aggregate utility variance, hence every effort should be made to avoid positive correlations between the single-attribute utilities. We also provide guidelines for determining under what condition and to what extent a decision maker should decompose to obtain an aggregate utility variance that is smaller than that of holistic assessments. Extensions of the current model and empirical research to support some of our behavioural assumptions are discussed. © 1997 John Wiley & Sons, Ltd.