管理评价判断模型与策略获取

管理评价是在不确定情景中的一种多特征决策,非补偿策略是决策者所采用的主导判断模型。作为一种结构建模程序,策略捕捉可以建构包含决策标准及其权重的统计模型,获取决策内在过程和预测判断。多层线性模型能够方便地考察人内和人间变异,表征判断非线性结构,是分析策略捕捉数据的恰当方法。管理评价设计可以借鉴行为经济学和实验经济学的思路,实现对现实管理决策过程的抽象模拟。     

Affect, Curiosity, and Socialization-Related Learning: A Path Analysis of Antecedents to Job Performance

This study examined the influence of affect, curiosity, and socialization-related learning on job performance, with 233 service industry employees from a diverse variety of occupations completing surveys at their places of work. Both state and trait curiosity and socialization-related learning (learning associated with employee socialization) were hypothesized to mediate the relationship between affect (operationalized as state and trait anxiety and anger) and job performance. Structural equation analyses indicate that the data are consistent with the theoretical models proposed. As expected, anxiety negatively influences curiosity, socialization-related learning, and job performance; conversely, anger positively influences curiosity, socialization-related learning, and job performance. Overall, the findings suggest that affects indeed predict perception of job performance, but through the mediation of curiosity and the learning associated with the socialization process. These results highlight the importance of the complex interplay between affect, curiosity, and learning when thinking about successful employee socialization and best possible job performance.     

Capturing the interplay among within- and between-person processes using multilevel modeling techniques

Multilevel modeling is an excellent way to analyze nested or clustered data of the type commonly collected through investigations into the linkages between psychological functioning and relationship processes. This article describes two especially relevant applications of multilevel modeling. The first application, growth curve analysis, is already familiar to many researchers and involves modeling individuals’ change trajectories over time and relating the derived change parameters to person-level characteristics or phenomena. The purpose of this paper is to emphasize a second application, multilevel process analysis, which involves modeling within-subject characteristics other than change over a representation of time. Multilevel analysis of within-subject processes is particularly well-suited for hypotheses common to clinical psychology investigations, yet has received substantially less attention in the literature than its growth curve counterpart. Types of research questions and methodologies that can be addressed within the multilevel process analysis framework are described. Finally, aspects of multilevel process analysis are demonstrated with daily diary data collected from wives who reported on their marital happiness and depressed mood for 3 weeks.     

Analysis of the factor structure of the Sociocultural Attitudes Towards Appearance Questionnaire (SATAQ-3) in Spanish secondary-school students through exploratory structural equation modeling

The aims of the present study were: (1) to assess the factor structure of the SATAQ-3 in Spanish secondary-school students by means of exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) models; and (2) to study its invariance by sex and school grade. ESEM is a technique that has been proposed for the analysis of internal structure that overcomes some of the limitations of EFA and CFA. Participants were 1559 boys and girls in grades seventh to tenth. The results support the four-factor solution of the original version, and reveal that the best fit was obtained with ESEM, excluding Item 20 and with correlated uniqueness between reverse-keyed items. Our version shows invariance by sex and grade. The differences between scores of different groups are in the expected direction, and support the validity of the questionnaire. We recommend a version excluding Item 20 and without reverse-keyed items.     

Computational exploration of metaphor comprehension processes using a semantic space model

Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view ( Bowdle & Gentner, 2005 ), aptness view ( Glucksberg & Haught, 2006b ), and interpretive diversity view ( Utsumi, 2007 ); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization and comparison processes. This article attempts to answer the question regarding which views are plausible by using cognitive modeling and computer simulation based on a semantic space model. In the simulation experiment, categorization and comparison processes are modeled in a semantic space constructed by latent semantic analysis. These two models receive word vectors for the constituent words of a metaphor and compute a vector for the metaphorical meaning. The resulting vectors can be evaluated according to the degree to which they mimic the human interpretation of the same metaphor; the maximum likelihood estimation determines which of the two models better explains the human interpretation. The result of the model selection is then predicted by three metaphor properties (i.e., vehicle conventionality, aptness, and interpretive diversity) to test the three views. The simulation experiment for Japanese metaphors demonstrates that both interpretive diversity and vehicle conventionality affect the choice between the two processes. On the other hand, it is found that metaphor aptness does not affect this choice. This result can be treated as computational evidence supporting the interpretive diversity and conventionality views.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

Number versus continuous quantity in numerosity judgments by fish

A fundamental question in human cognition is how people reason about space. We use a computational model to explore cross-cultural commonalities and differences in spatial cognition. Our model is based upon two hypotheses: (1) the structure-mapping model of analogy can explain the visual comparisons used in spatial reasoning; and (2) qualitative, structural representations are computed by people’s visual systems and used in these comparisons. We apply our model to a visual oddity task, in which individuals are shown an array of two-dimensional images and asked to the pick the one that does not belong. This task was previously used to evaluate understanding of geometric concepts in two disparate populations: North Americans, and the Mundurukú, a South American indigenous group. Our model automatically generates representations of each hand-segmented image and compares them to solve the task. The model achieves human-level performance on this task, and problems that are hard for the model are also difficult for people in both cultures. Furthermore, ablation studies on the model suggest explanations for cross-cultural differences in terms of differences in spatial representations.     

Restructuring partitioned knowledge: the role of recoordination in category learning

Knowledge restructuring refers to changes in the strategy with which people solve a given problem. Two types of knowledge restructuring are supported by existing category learning models. The first is a relearning process, which involves incremental updating of knowledge as learning progresses. The second is a recoordination process, which involves novel changes in the way existing knowledge is applied to the task. Whereas relearning is supported by both single- and multiple-module models of category learning, only multiple-module models support recoordination. To date, only relearning has been directly supported empirically. We report two category learning experiments that provide direct evidence of recoordination. People can fluidly alternate between different categorization strategies, and moreover, can reinstate an old strategy even after prolonged use of an alternative. The knowledge restructuring data are not well fit by a single-module model (ALCOVE). By contrast, a multiple-module model (ATRIUM) quantitatively accounts for recoordination. Low-level changes in the distribution of dimensional attention are shown to subsequently affect how ATRIUM coordinates its modular knowledge. We argue that learning about complex tasks occurs at the level of the partial knowledge elements used to generate a response strategy.