A parametric procedure for ultrametric tree estimation from conditional rank order proximity data

The psychometric and classification literatures have illustrated the fact that a wide class of discrete or network models (e.g., hierarchical or ultrametric trees) for the analysis of ordinal proximity data are plagued by potential degenerate solutions if estimated using traditional nonmetric procedures (i.e., procedures which optimize a STRESS-based criteria of fit and whose solutions are invariant under a monotone transformation of the input data). This paper proposes a new parametric, maximum likelihood based procedure for estimating ultrametric trees for the analysis of conditional rank order proximity data. We present the technical aspects of the model and the estimation algorithm. Some preliminary Monte Carlo results are discussed. A consumer psychology application is provided examining the similarity of fifteen types of snack/breakfast items. Finally, some directions for future research are provided.     

Netscal: A network scaling algorithm for nonsymmetric proximity data

A simple property of networks is used as the basis for a scaling algorithm that represents nonsymmetric proximities as network distances. The algorithm determines which vertices are directly connected by an arc and estimates the length of each arc. Network distance, defined as the minimum pathlength between vertices, is assumed to be a generalized power function of the data. The derived network structure, however, is invariant across monotonic transformations of the data. A Monte Carlo simulation and applications to eight sets of proximity data support the practical utility of the algorithm.I am grateful to Roger Shepard and Amos Tversky for their helpful comments and guidance throughout this project. The work was supported by National Science Foundation Grant BNS-75-02806 to Roger Shepard and a National Science Foundation Graduate Fellowship to the author. Parts of this paper were drawn from a doctoral dissertation submitted to Stanford University (Hutchinson, 1981).     

Ordinal network representation: Representing proximities by graphs

Ordinal network representations are graph-theoretic representations of proximity data. They seek to provide parsimonious representations of the ordinal (nonmetric) information in observed proximity data by means of the minimum-path-length distance of a connected and weighted graph. In contrast to traditional tree-based graph-theoretic approaches, ordinal network representation is not limited to but includes the representation by trees. Asymmetry in the proximity data and violations of zero-minimality are allowed for. The paper explores fundamental representation and uniqueness results and discusses a method of constructing ordinal network representations. A simple strategy for handling the problem of errors in the data is described and illustrated.This work was supported by grant Fe 75/20-2 of the Deutsche Forschungsgemeinschaft. The author is indebted to Hubert Feger for many inspiring discussions.     

Domain dimensionality and temporality of outcome expectancy for physical activity among middle-aged and older Chinese adults: A latent profile analysis

ObjectivesUsing a person-centered approach, this study aimed to identify the profiles of outcome expectancy for physical activity among middle-aged and older Chinese adults. Profiles were defined by two aspects of outcome expectancy including domain dimensionality and temporality, given the emphasis on social connectedness and the shortened future-time perception in this population.DesignCross-sectional survey design.MethodsParticipants included 300 Chinese adults aged 50 years or above, recruited in Hong Kong. Participants were asked to indicate their outcome expectancy for physical activity in physical, social and self-evaluative domains, rate the immediacy of each outcome expectancy item, and then report their physical activity for the past week. A latent class analysis was used to identify the number of profiles and profile characteristics, and to examine the predictors of the profile memberships and effects of the memberships on physical activity.ResultsResults suggested four profiles, including low, moderate, delayed-high and immediate-high expectancy classes. Temporality, rather than domain dimensionality, was essential in defining the profiles. Interdependent self-construal and future-time perception were predictive of the profile memberships. People with immediate-high expectancy were more physically active than those in other expectancy classes.ConclusionsThis study provided supporting evidence for the temporal self-regulation theory, and demonstrated the importance of immediate outcomes. More research attention in physical activity promotion should be given to the temporal dimension, especially for older adults who perceive future time as more limited. New directions to enhance outcome expectancies, especially for this population, also need further investigation.     

Composition in distributional models of semantics

Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.     

Do you see what I see? Co-actor posture modulates visual processing in joint tasks

ABSTRACT

Interacting with other people is a ubiquitous part of daily life. A complex set of processes enable our successful interactions with others. The present research was conducted to investigate how the processing of visual stimuli may be affected by the presence and the hand posture of a co-actor. Experiments conducted with participants acting alone have revealed that the distance from the stimulus to the hand of a participant can alter visual processing. In the main experiment of the present paper, we asked whether this posture-related source of visual bias persists when participants share the task with another person. The effect of personal and co-actor hand-proximity on visual processing was assessed through object-specific benefits to visual recognition in a task performed by two co-actors. Pairs of participants completed a joint visual recognition task and, across different blocks of trials, the position of their own hands and of their partner's hands varied relative to the stimuli. In contrast to control studies conducted with participants acting alone, an object-specific recognition benefit was found across all hand location conditions. These data suggest that visual processing is, in some cases, sensitive to the posture of a co-actor.     

Preaching,Sexuality, and Women Religious

Abstract

This paper addresses how the role of the prophetic is contested by examining ecclesial conflict in the Catholic Church as exemplified in the Vatican critique of the Leadership Conference of Women Religious and the social ministries and lobbying efforts of NETWORK’s “Nuns on the Bus.” Drawing on the fields of homiletics, biblical studies, public theology, and feminist theology, we examine patterns of prophetic discourse and defend the work of the nuns as prophetic in that their work bears witness to the reign of God in everyday contexts. Binary patterns such as church/world, ordained/lay, sacred/profane, male/female, privileged/marginalized are ultimately inaccurate and unhelpful. We argue that the prophetic must be shaped and discerned by the lived experiences of the whole people of God but privileging the experiences of those on the margins. We invite readers to think of the role of the prophetic beyond the pulpit and into the public sphere.     

Moving Without Being Where You’re Not; A Non-Bivalent Way

The classical response to Zeno’s paradoxes goes like this: ‘Motion cannot properly be defined within an instant. Only over a period’ (Vlastos.) I show that this ob-jection is exactly what it takes for Zeno to be right. If motion cannot be defined at an instant, even though the object is always moving at that instant, motion cannot be defined at all, for any longer period of time identical in content to that instant. The nonclassical response introduces discontinuity, to evade the paradox of infinite proximity of any point of a distance with any ‘next’. But it introduces the wrong sort of discontinuity because, rather than assuming the discontinuity of motion, as Quantum Theory does, it assumes the discontinuity of space. Due then to the resulting spacetime disorder, though all else is certainly lost, the Tortoise now turns up at least as fast as Achilles and hence not even this much is rescued. Zeno rejects motion because he shows that a moving object must be where it is not. Hence motion, if to occur, must violate the Law of Contradiction (LNC). Applying the concept of quantum discontinuity, I produce an alternative. If an object is to move discontinuously between two boundary points, A and B, what actually obtains is, rather, that it is nowhere at all in-between A and B. And cannot therefore be at two places in-between A and B. And cannot therefore be where it is not. Thus, LNC is conserved. However, in these conditions, the Law of the Excluded Middle (LEM) fails. To mitigate the undesirability of this effect, I show that LEM fails because LNC holds. Thus, the resulting nonbivalent logic, which is also appropriate for quantized transitions of all kinds, will always turn up nonbivalent, because consistent. And this is not too bad, considering.     

A Modal Logic for Discretely Descending Chains of Sets

We present a modal logic for the class of subset spaces based on discretely descending chains of sets. Apart from the usual modalities for knowledge and effort the standard temporal connectives are included in the underlying language. Our main objective is to prove completeness of a corresponding axiomatization. Furthermore, we show that the system satisfies a certain finite model property and is decidable thus.