心理学对经济学的涉入——从理性自利到社会偏好的决策框架

理性认知能力与社会偏好存在紧密的关联。文章简要回顾了经济决策理论从理性模型到有限理性模型和社会偏好模型的发展进程,论述了人们理性的局限性及其根源,并进一步探讨理性认知能力与社会偏好的关系。对人类以及灵长目动物的研究显示,有限理性可能是由根源于演化的适应性机制所导致。人类不公平厌恶的起源、个体公平能力的发展规律和表征公平的大脑结构上的证据表明,理性认知能力能让人更好地抑制自私性,实现更高层次的公平。     

城镇化进程中离乡农民心理健康特点调查及心理素质、相对剥夺感的影响机制

为探讨城镇化进程中离乡农民的心理健康特点以及心理素质、相对剥夺感对其的影响,通过量表对1339名城乡居民进行调查。结果发现:离乡与留乡农民在幸福感、拥有生命意义感、感知控制感、心理素质方面显著低于城镇居民,在状态焦虑方面显著高于城镇居民; 离乡农民在抑郁上显著高于城镇居民,在感知控制感上显著高于留乡农民,在心理素质上显著低于留乡农民; 留乡农民在相对剥夺感上显著高于城镇居民; 离乡农民的幸福感、拥有生命意义感、感知控制感态与相对剥夺感呈显著负相关,与心理素质呈显著正相关; 离乡农民的抑郁、状态焦虑与相对剥夺感呈显著正相关,与心理素质呈显著负相关; 离乡农民的相对剥夺感与心理素质之间呈显著负相关。心理素质除了可以直接预测心理健康各项指标,还可以通过相对剥夺感的中介作用,间接预测幸福感、抑郁及状态焦虑。提升心理素质和降低相对剥夺感是离乡农民心理健康服务的重要切入点。     

认知诊断缺失数据处理方法的比较：零替换、多重插补与极大似然估计法

数据缺失在测验中经常发生, 认知诊断评估也不例外, 数据缺失会导致诊断结果的偏差。首先, 通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明：(1)缺失数据导致估计精确性下降, 随着人数与题目数量减少、缺失率增大、题目质量降低, 所有方法的PCCR均下降, Bias绝对值和RMSE均上升。(2)估计题目参数时, EM法表现最好, 其次是MI, FIML和ZR法表现不稳定。(3)估计被试知识状态时, EM和FIML表现最好, MI和ZR表现不稳定。其次, 在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果, 推荐选用EM或FIML法进行缺失数据处理。     

Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment

The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.     

Towards autonomous artificial agents with an active self: Modeling sense of control in situated action

In this paper we present a computational modeling account of an active self in artificial agents. In particular we focus on how an agent can be equipped with a sense of control and how it arises in autonomous situated action and, in turn, influences action control. We argue that this requires laying out an embodied cognitive model that combines bottom-up processes (sensorimotor learning and fine-grained adaptation of control) with top-down processes (cognitive processes for strategy selection and decision-making). We present such a conceptual computational architecture based on principles of predictive processing and free energy minimization. Using this general model, we describe how a sense of control can form across the levels of a control hierarchy and how this can support action control in an unpredictable environment. We present an implementation of this model as well as first evaluations in a simulated task scenario, in which an autonomous agent has to cope with un-/predictable situations and experiences corresponding sense of control. We explore different model parameter settings that lead to different ways of combining low-level and high-level action control. The results show the importance of appropriately weighting information in situations where the need for low/high-level action control varies and they demonstrate how the sense of control can facilitate this.     

时间感知在跨期决策中的作用——时间决策模型的新探索

“时间”是跨期决策的“必需品”, 人们感知到的延迟时间决定跨期决策的结果。近年来, 研究者发现“时间长度感知”、“时间资源感知”和“时间框架感知”是时间感知作用于跨期决策的主要方式。时间感知的神经作用机制包含微观层面和宏观层面两种。“对数/指数时间折扣模型”、“感知时间基础模型”及“多模态漂移扩散模型”解释了时间感知的作用方式。然而, 现有理论模型还存在诸多局限, 主要包括“长短时距预测偏差”和“实际决策与预期决策偏差”两个方面。因此, 深入探讨时间感知影响跨期决策的基本方式, 分析现有理论模型的局限性并提出整合的机制框架具有十分重要的意义。未来研究亟需进一步整合时间感知的理论模型, 开展脑机制与应用方面的研究, 从深层揭露时间感知的作用本质, 帮助个人与社会更理性地决策。     

旅游商品多语言包装策略的作用机制和影响效果——基于选择通达模型的视角

多语言包装策略被广泛应用于促进旅游商品的销售。然而, 现有研究仅从消费者感知视角出发, 难以揭示多语言线索的复杂效应。本研究基于选择通达模型, 主要探讨(1)多语言包装策略导致消费者推断旅游商品是针对某类群体(即对比效应)还是广泛模糊的“国际市场” (即同化效应)的作用机制; (2)多语言包装策略对旅游商品购买决策的影响机制。本研究有助于扩展旅游商品和包装语言的相关研究, 为旅游商品营销提供重要参考。     

The relationship between sensory responsiveness profiles,attachment orientations,and anxiety symptoms

Individual variations in the way people respond to sensory stimuli can sometimes lead to maladaptive representations of the world. Indeed, sensory responsiveness profiles were found to be associated with mood symptoms such as depression and anxiety. The goal of the current study was to investigate whether attachment orientations can account for the relationship between sensory responsiveness profiles and anxiety symptoms. Participants (N = 194) completed a battery of questionnaires assessing sensory responsiveness profiles, attachment orientations, and anxiety symptoms. As expected, various associations between sensory responsiveness profiles and anxiety symptoms were accounted for by attachment anxiety and avoidance. We suggest a possible causal path, in which early‐developing sensory responsiveness profiles lead to attachment insecurities, which in turn may lead to mood symptoms such as anxiety.     

A sixth personality domain that is independent of the Big Five domains: The psychometric properties of the HEXACO Personality Inventory in a Japanese sample

We investigated the reliability of the HEXACO‐Personality Inventory (HEXACO‐PI) using the NEO Five‐Factor Inventory (NEO‐FFI) and the International Personality Item Pool (IPIP) Big Five scales in a Japanese sample of 492 respondents (mean age = 20.4 years). The psychometric properties of the HEXACO‐PI were examined and found to be satisfactory. The results of the factor analysis showed that the six hypothesized personality domains were found independently. A total of 307 participants responded to the NEO‐FFI and the IPIP Big Five scales. The results showed that the five domains of the HEXACO model corresponded to the Five‐Factor domains and were relative to the Big Five personality domains as a whole, but the sixth domain, Honesty‐Humility, showed weak to moderate correlations with some factors of the NEO‐FFI and the IPIP Big Five scales. These results suggest that the Honesty‐Humility domain is independent of the five fundamental personality factors and that the HEXACO‐PI is a useful and reliable questionnaire for investigating personality structure in cross‐cultural and other context.     

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.