The Mental Representation of Human Action

Various theories of moral cognition posit that moral intuitions can be understood as the output of a computational process performed over structured mental representations of human action. We propose that action plan diagrams—“act trees”—can be a useful tool for theorists to succinctly and clearly present their hypotheses about the information contained in these representations. We then develop a methodology for using a series of linguistic probes to test the theories embodied in the act trees. In Study 1, we validate the method by testing a specific hypothesis (diagrammed by act trees) about how subjects are representing two classic moral dilemmas and finding that the data support the hypothesis. In Studies 2–4, we explore possible explanations for discrete and surprising findings that our hypothesis did not predict. In Study 5, we apply the method to a less well‐studied case and show how new experiments generated by our method can be used to settle debates about how actions are mentally represented. In Study 6, we argue that our method captures the mental representation of human action better than an alternative approach. A brief conclusion suggests that act trees can be profitably used in various fields interested in complex representations of human action, including law, philosophy, psychology, linguistics, neuroscience, computer science, robotics, and artificial intelligence.     

Individual differences in willingness to become an organ donor: A decision tree approach to reasoned action

The promotion of organ donation requires a better understanding of the attributes associated with willingness to donate. This entails revealing complex interactions among personality attributes related to intentions and behaviors. In the present study, survey data from young adults (N = 367) were analyzed using decision tree algorithms to identify these interactions within the reasoned action framework. Within this structure, donation attitudes, intentions, and behaviors were examined in relation to individual differences such as conscientiousness, empathy, time orientation, religiosity, and interpersonal trust.     

Notes on the weighting biases in value trees

The variation in the structure of value trees can have undesirable effects on the attribute weights. Earlier experiments suggest that an attribute receives a higher weight if it is presented at an upper level in a value tree or if it is split into subattributes. Here we show that it is flawed to make conclusions about the biases at the individual level based on the averages of weights across large groups of subjects. Averages do not describe individual behavior. Furthermore, the averages of weights tend to approach even weights. By using the data from earlier experiments we illustrate how the averaging can produce different phenomena. We also show that the use of weights based on the rank order of attributes only can easily lead to biases when the structure of a value tree is changed. © 1998 John Wiley & Sons, Ltd.     

Extended similarity trees

Proximity data can be represented by an extended tree, which generalizes traditional trees by including marked segments that correspond to overlapping clusters. An extended tree is a graphical representation of the distinctive features model. A computer program (EXTREE) that constructs extended trees is described and applied to several sets of conceptual and perceptual proximity data.This research was supported in part by a National Science Foundation Pre-doctoral Fellowship to the first author.A magnetic tape containing both the EXTREE program described in the article and ADDTREE/P program for fitting additive trees can also be obtained from the above address. Requests for the program should be accompanied by a check for $25 made out to Teachers College, to cover the costs of the tape and postage.     

“Minimax length links” of a dissimilarity matrix and minimum spanning trees

A theorem is proved stating that the set of all minimax links, defined as links minimizing, over paths, the maximum length of links in any path connecting a pair of objects comprising nodes in an undirected weighted graph, comprise the union of all minimum spanning trees of that graph. This theorem is related to methods of fitting network models to dissimilarity data, particularly a method called Pathfinder due to Schvaneveldt and his colleagues, as well as to single linkage clustering, and results concerning the relationship between minimum spanning trees and single linkage hierarchical trees.Acknowledgments: The author thanks Phipps Arabie, Lawrence J. Hubert, and K. Christoph Klauer for a number of helpful suggestions and comments on various aspects of this paper.     

A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis

This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objectsi andj is a function of both the interpoint distance betweeni andj and the density of other stimulus points in the regions surroundingi andj. We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.     

Simple rules for detecting depression

Depressive disorders are major public health issues worldwide. We tested the capacity of a simple lexicographic and noncompensatory fast and frugal tree (FFT) and a simple compensatory unit-weight model to detect depressed mood relative to a complex compensatory logistic regression and a naïve maximization model. The FFT and the two compensatory models were fitted to the Beck Depression Inventory (BDI) score of a representative sample of 1382 young women and cross validated on the women's BDI score approximately 18 months later. Although the FFT on average inspected only approximately one cue, it outperformed the naïve maximization model and performed comparably to the compensatory models. The heavier false alarms were weighted relative to misses, the better the FFT and the unit-weight model performed. We conclude that simple decision tools—which have received relatively little attention in mental health settings so far—might offer a competitive alternative to complex weighted assessment models in this domain.     

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.