大枫树下有鬼之谜

刘家村村后有棵大枫树,相传这棵树以前吊死过人。现在随着树龄增长,根繁加果实芽发,大枫树周围有了众多小树,成为阴森森的树丛堆。也不知是何年何人先说起,说这阴森森树丛正好给吊死的人安家。从此刘家村人很少单身夜走大枫树下,要么     

Family tree matters

The present paper investigates the form and structure of the psychoanalytic 'family tree', concentrating on the analysands of Sándor Ferenczi and Otto Rank, and their impact on contemporary psychoanalysis.     

The decision tree polytope and its application to sequential decision problems

This paper describes a new mathematical programming approach to sequential decision problems that have an underlying decision tree structure. The approach, based upon a characterization of strategies as extreme points of a 0–1 polytope called the ‘decision tree polytope’, is particularly suited to the direct examination of risk-return and other tradeoffs amongst strategies. However, it can also be used for conventional utility maximization if a utility function is available. Further, the approach requires no algorithmic development—it can be implemented using commercially available algebraic modeling software and can solve large problems. A related, and already known, approach can be used for some more general Markov decision problems. © 1998 John Wiley & Sons, Ltd.     

The statistical analysis of general processing tree models with the EM algorithm

Multinomial processing tree models assume that an observed behavior category can arise from one or more processing sequences represented as branches in a tree. These models form a subclass of parametric, multinomial models, and they provide a substantively motivated alternative to loglinear models. We consider the usual case where branch probabilities are products of nonnegative integer powers in the parameters, 0_s1, and their complements, 1 - _s. A version of the EM algorithm is constructed that has very strong properties. First, the E-step and the M-step are both analytic and computationally easy; therefore, a fast PC program can be constructed for obtaining MLEs for large numbers of parameters. Second, a closed form expression for the observed Fisher information matrix is obtained for the entire class. Third, it is proved that the algorithm necessarily converges to a local maximum, and this is a stronger result than for the exponential family as a whole. Fourth, we show how the algorithm can handle quite general hypothesis tests concerning restrictions on the model parameters. Fifth, we extend the algorithm to handle the Read and Cressie power divergence family of goodness-of-fit statistics. The paper includes an example to illustrate some of these results.This research was supported by National Science Foundation Grant BNS-8910552 to William H. Batchelder and David M. Riefer. We are grateful to David Riefer for his useful comments, and to the Institute for Mathematical Behavior Sciences for its support.     

Out of the laboratory and into the community. 26 years of applied behavior analysis at the Juniper Gardens Children's Project.

Application of Skinner's principles to socially significant human behavior had been well articulated by 1968 (Baer, Wolf, & Risley, 1968). Applications of these principles by Baer, Wolf, Risley, Hall, Hart, Christophersen, and their colleagues were in evidence as early as 1964 in the homes, schools, and clinics of inner-city Kansas City, Kansas, at the Juniper Gardens Housing Project. The work continues relatively uninterrupted, having contributed extensively to the literature of applied behavior analysis and the lives of community residents. This article describes the project and illustrates how applied behavioral research was initiated and extended, how the work addressed general concerns in psychology, and how it continues to address contemporary concerns within the community.     

Descartes and the tree of knowledge

Descartes' image of the tree of knowledge from the preface to the French edition of the Principles of Philosophy is usually taken to represent Descartes' break with the past and with the fragmentation of knowledge of the schools. But if Descartes' tree of knowledge is analyzed in its proper context, another interpretation emerges. A series of contrasts with other classifications of knowledge from the seventeenth and eighteenth centuries raises some puzzles: claims of originality and radical break from the past do not seem warranted. Further contrasts with Descartes' unpublished writings and with school doctrines lead to the ironic conclusion that, in the famous passage, Descartes is attempting to appeal to conventional wisdom and trying to avoid sounding novel.     

Parameter identification in multinomial processing tree models

Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis of the likelihood function, and for the interpretation of the results. The required calculations to establish global identification can become intractable in complex models. We show how to establish local identification in multinomial processing tree models, based on formal methods independently proposed by Catchpole and Morgan (1997) and by Bekker, Merckens, and Wansbeek (1994). This approach is illustrated with multinomial processing tree models for the source-monitoring paradigm in memory research.     

Spatial versus tree representations of proximity data

In this paper we investigated two of the most common representations of proximities, two-dimensional euclidean planes and additive trees. Our purpose was to develop guidelines for comparing these representations, and to discover properties that could help diagnose which representation is more appropriate for a given set of data. In a simulation study, artificial data generated either by a plane or by a tree were scaled using procedures for fitting either a plane (KYST) or a tree (ADDTREE). As expected, the appropriate model fit the data better than the inappropriate model for all noise levels. Furthermore, the two models were roughly comparable: for all noise levels, KYST accounted for plane data about as well as ADDTREE accounted for tree data. Two properties of the data proved useful in distinguishing between the models: the skewness of the distribution of distances, and the proportion of elongated triangles, which measures departures from the ultrametric inequality, Applications of KYST and ADDTREE to some twenty sets of real data, collected by other investigators, showed that most of these data could be classified clearly as favoring either a tree or a two-dimensional representation.A portable PASCAL program implementing the Sattath and Tversky [1977] ADDTREE algorithm is available from J. Corter, Department of Psychology, Stanford University, Stanford, California 94305.     

Multinomial processing tree models: An implementation

Multinomial processing tree (MPT) models have been widely used by researchers in cognitive psychology. This paper introduces MBT.EXE, a computer program that makes MPT easy to use for researchers. MBT.EXE implements the statistical theory developed by Hu and Batchelder (1994). This user-friendly software can be used to construct MPT models and conduct statistical inferences, including point and interval estimation, hypothesis testing, and goodness of fit. Furthermore, this program can be used to examine the robustness of MPT models. Algorithms for parameter estimation, hypothesis testing, and Monte Carlo simulation are presented.     

The impact of subjective recognition experiences on recognition heuristic use: A multinomial processing tree approach

The recognition heuristic (RH) theory states that, in comparative judgments (e.g., Which of two cities has more inhabitants?), individuals infer that recognized objects score higher on the criterion (e.g., population) than unrecognized objects. Indeed, it has often been shown that recognized options are judged to outscore unrecognized ones (e.g., recognized cities are judged as larger than unrecognized ones), although different accounts of this general finding have been proposed. According to the RH theory, this pattern occurs because the binary recognition judgment determines the inference and no other information will reverse this. An alternative account posits that recognized objects are chosen because knowledge beyond mere recognition typically points to the recognized object. A third account can be derived from the memory-state heuristic framework. According to this framework, underlying memory states of objects (rather than recognition judgments) determine the extent of RH use: When two objects are compared, the one associated with a “higher” memory state is preferred, and reliance on recognition increases with the “distance” between their memory states. The three accounts make different predictions about the impact of subjective recognition experiences—whether an object is merely recognized or recognized with further knowledge—on RH use. We estimated RH use for different recognition experiences across 16 published data sets, using a multinomial processing tree model. Results supported the memory-state heuristic in showing that RH use increases when recognition is accompanied by further knowledge.     

Assessing item-feature effects with item response tree models

Recent applications of item response tree models demonstrate that this model class is well suited to detect midpoint and extremity response style effects in both attitudinal and personality measurements. This paper proposes an extension of this approach that goes beyond measuring response styles and allows us to examine item-feature effects. In a reanalysis of three published data sets, it is shown that the proposed extension captures item-feature effects across affirmative and reverse-worded items in a psychological test. These effects are found to affect directional responses but not midpoint and extremity preferences. Moreover, accounting for item-feature effects substantially improves model fit and interpretation of the construct measurement. The proposed extension can be implemented readily with current software programs that facilitate maximum likelihood estimation of item response models with missing data.     

A context-free language for binary multinomial processing tree models

This paper provides a new formalization for the class of binary multinomial processing tree (BMPT) models, and theorems for the class are developed using the formalism. MPT models are a popular class of information processing models for categorical data in specific cognitive paradigms. They have a recursive structure that is productively described with the tools of formal language and computation theory. We provide an axiomatization that characterizes BMPT models as strings in a context-free language, and then we add model-theoretic axioms and definitions to interpret the strings as parameterized probabilistic models for categorical data. The language for BMPT models is related to the Full Binary Tree language, a well-studied context-free language. Once BMPT models are viewed from the perspective of the Full Binary Tree language, a number of theoretical and computational results can be developed. In particular, we have a number of results concerning the enumerations of BMPT models as well as the identifiability of subclasses of these models.     

Theoretical and empirical review of multinomial process tree modeling

We review a current and popular class of cognitive models calledmultinomial processing tree (MPT) models. MPT models are simple, substantively motivated statistical models that can be applied to categorical data. They are useful as data-analysis tools for measuring underlying or latent cognitive capacities and as simple models for representing and testing competing psychological theories. We formally describe the cognitive structure and parametric properties of the class of MPT models and provide an inferential statistical analysis for the entire class. Following this, we provide a comprehensive review of over 80 applications of MPT models to a variety of substantive areas in cognitive psychology, including various types of human memory, visual and auditory perception, and logical reasoning. We then address a number of theoretical issues relevant to the creation and evaluation of MPT models, including model development, model validity, discrete-state assumptions, statistical issues, and the relation between MPT models and other mathematical models. In the conclusion, we consider the current role of MPT models in psychological research and possible future directions.     

Question production in agrammatism: the tree pruning hypothesis

This study investigated question production in agrammatic aphasia, focusing on the comparison between Wh questions and yes/no questions and on the interaction between the question production deficit and language-specific properties. A total of 16 agrammatic aphasics (13 Hebrew speakers, 2 Palestinian Arabic speakers, and 1 English speaker) participated in the study, which included sentence elicitation and repetition tasks. In addition, the patients' spontaneous speech, containing 2272 utterances, was analyzed. The main findings were that Hebrew- and Arabic-speaking agrammatics encounter severe difficulties in Wh question production but retain the ability to produce yes/no questions. English-speaking agrammatics do not show this dissociation and can form neither Wh nor yes/no questions. These dissociations as well as the error pattern, are explained by reference to the Tree Pruning Hypothesis, according to which the highest nodes of the syntactic tree, which are required for Wh questions in Hebrew, Arabic, and English and for yes/no questions in English, are impaired or inaccessible in agrammatism.     

Probability and category redefinition in the fault tree paradigm

Fault trees have been advocated as aids for problem solvers. However, research has suggested limitations in their usefulness. Fischhoff, Slovic, and Lichtenstein (1978) found that subjects given incomplete (pruned) trees were insensitive to omissions; these authors hypothesized that Tversky and Kahneman's (1974) availability heuristic was the mediating factor. Using a within-subjects design, subjects in Experiment 1 received both full and pruned trees and estimated probabilities for various reasons why a car would fail to start. To increase the availability of omissions, some Experiment 1 subjects first generated possible causes of starting failure. The basic Fischhoff et al. findings were replicated, but several aspects of the results argued against the availability hypothesis as the mechanism for judgment. Instead, subjects appeared to idiosyncratically redefine category membership when making judgments based upon pruned trees. By employing a sorting task in Experiment 2 we corroborated the results of Experiment 1: Subjects do redefine the actual contents of the categories when faced with an omission from the fault tree. The implications of these results for the use of fault trees as a problem solving aid are discussed.