Evolutionary preference/utility functions: A dynamic perspective

The collection of repeated measures in psychological research is one of the most common data collection formats employed in survey and experimental research. The behavioral decision theory literature documents the existence of the dynamic evolution of preferences that occur over time and experience due to learning, exposure to additional information, fatigue, cognitive storage limitations, etc. We introduce a Bayesian dynamic linear methodology employing an empirical Bayes estimation framework that permits the detection and modeling of such potential changes to the underlying preference utility structure of the respondent. An illustration of revealed stated preference analysis (i.e., conjoint analysis) is given involving students’ preferences for apartments and their underlying attributes and features. We also present the results of several simulations demonstrating the ability of the proposed procedure to recover a variety of different sources of dynamics that may surface with preference elicitation over repeated sequential measurement. Finally, directions for future research are discussed.The authors wish to acknowledge and thank the Editor, the Associate Editor, and two anonymous reviewers for their constructive and insightful comments. Duncan K.H. Fong’s work was sponsored in part by a research grant from the Smeal College.This revised article was published online in August 2005 with the PDF paginated correctly.     

An Introduction to the Special Issue on Advances in Process and Dynamic System Analysis of Social Interaction and the Development of Antisocial Behavior

A thorough understanding of how social relationships contribute to child and adolescent trajectories for antisocial behavior may be facilitated by: (a) ascertaining multiple relationship processes (e.g., warmth and reciprocity, coercion and deviancy training); (b) focusing on multiple relationships (e.g., with parents, peers, siblings, and teachers): and (c) assessing relationship processes using increasingly sophisticated measurement and theoretical models (e.g., global ratings and sequential and dynamical systems analyses) of observed microsocial interaction. The reports comprising this special issue, and how they build on and advance previous research efforts, are described from this frame of reference.     

几种时间表征的研究概况

概述时间表征外显与内隐的分类,对动力心理表征及表征动量的研究进行了讨论     

驾驶员的动态反应时研究

本研究应用DFY-1型动态反应时测试仪,以解放CA10B 型卡车为实验用车,结合日常运输任务,对三种年龄组(20—29岁、30—39岁、40—49岁)的30名驾驶员,在三种车速(静态,30公里/小时及50公里/小时)条件下的反应时、动作时及制动反应时进行了测试。结果表明:车速对驾驶员的反应时及制动反应时有显著影响,而对动作时则无显著影响;驾驶员的年龄对反应时、动作时及制动反应时均无显著影响.本研究结果为加强驾驶员的安全教育与管理,控制车速提供了佐证。更高车速条件下驾驶员的反应时特点尚待进一步研究。     

On Agents That Have the Ability to Choose

We demonstrate ways to incorporate nondeterminism in a system designed to formalize the reasoning of agents concerning their abilities and the results of the actions that they may perform. We distinguish between two kinds of nondeterministic choice operators: one that expresses an internal choice, in which the agent decides what action to take, and one that expresses an external choice, which cannot be influenced by the agent. The presence of abilities in our system is the reason why the usual approaches towards nondeterminism cannot be used here. The semantics that we define for nondeterministic actions is based on the idea that composite actions are unravelled in the strings of atomic actions and tests that constitute them. The main notions used in defining this semantics are finite computation sequences and finite computation runs of actions. The results that we obtain meet our intuitions regarding events and abilities in the presence of nondeterminism.     

基于用户动态信息加工的信息流广告回避机制与重定向策略

信息流广告飞速发展的同时用户广告回避愈发普遍,然而传统广告回避结论无法平移到该情境。本研究基于用户动态的信息加工视角,试图探讨(1)动态信息加工状态下(收敛式vs发散式)产生广告屏蔽和广告跳过行为的内在机制;(2)采用归因引导重定向策略,挖掘用户屏蔽广告的“残留效应”所带来的信号价值;(3)采用广告凸显重定向策略,突破用户跳过广告的“学习效应”造成的负面影响。丰富现有理论的同时,为信息流广告的响应式优化提供理论和决策支持。     

Intensive Transactional Analysis Psychotherapy (ITAP): A case series study

Intensive Transactional Analysis Psychotherapy (ITAP) is a new therapeutic approach based on the integration of Transactional Analysis and brief psychodynamic approaches. ITAP is based on two key therapeutic tools—the intrapsychic triangle and the interpersonal triangle—which the therapist uses to focus patients’ attention on moment‐by‐moment therapy processes. The aim of the present study was to provide early empirical documentation of ITAP effectiveness. To this aim, six patients (four females, two males) were longitudinally evaluated using an A‐B‐A mixed quantitative and qualitative design, with evaluations at Baseline, Treatment and Follow‐up phases. Each patient received 16 50‐min weekly sessions of ITAP therapy over 4 months. For quantitative evaluations, patients filled out the Clinical Outcome in Routine Evaluation‐Outcome Measure (CORE‐OM) at each evaluation. Effects size values were estimated to evaluate quantitative changes comparing mean scores of Baseline, Treatment and Follow‐up phases. At the first follow‐up, the Change Interview was used to qualitatively evaluate patients’ perceived changes. In the quantitative data, four of the six patients reported a very good outcome, with large to very large effect sizes in Baseline versus Treatment and/or Baseline versus Follow‐up scores comparisons, whereas two patients reported variable fluctuations within the non‐clinical range of outcome scores. In the qualitative evaluations, all patients reported several moderately to extremely important perceived changes attributed to psychotherapy.     

潜在剖面分析在组织行为领域中的应用

以个体为中心的研究路径将各个变量看作是相互依赖的一个系统, 基于多项特征(变量)将被试分为多个子群体, 分析子群体的前因与影响。以个体为中心的研究路径理解更加直观、更贴近实践, 受到越来越多的关注。潜在剖面分析(latent profile analysis, LPA)是以个体为中心研究路径的典型分析技术。在总结归纳以个体与以变量为中心两种研究路径异同、LPA与传统以个体为中心的分析技术差异后, 系统梳理了LPA在组织行为学领域的应用主题, 并从研究主题选取、样本要求、理论使用、剖面数量确定等方面归纳了LPA应用的步骤与注意事项。最后, 提出了未来研究的方向。     

Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data

Ambulatory assessment methods provide a rich approach for studying daily behaviour. Too often, however, these data are analysed in terms of averages, neglecting patterning of this behaviour over time. This paper describes recurrence quantification analysis (RQA), a non-linear time series technique for analysing dynamic systems, as a method for analysing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively assessed social behaviour (e.g. talking to another person) coded from the Electronically Activated Recorder. Conceptual interpretations of RQA parameters, and an analysis of Electronically Activated Recorder data in adults going through a marital separation, are provided. Using machine learning techniques to avoid model overfitting, we find that adding RQA parameters to models that include just average amount of time spent talking (a static measure) improves prediction of four Big Five personality traits: extraversion, neuroticism, conscientiousness, and openness. Our strongest results suggest that a combination of average amount of time spent talking and four RQA parameters yield an R² = .09 for neuroticism. Neuroticism is shown to be associated with shorter periods of extended conversation (periods of at least 12 minutes), demonstrating the utility of RQA to identify new relationships between personality and patterns of daily behaviour. Materials: https://osf.io/5nkr9/ . © 2020 European Association of Personality Psychology