The kinematics of belief and desire

Richard Jeffrey regarded the version of Bayesian decision theory he floated in ‘The Logic of Decision’ and the idea of a probability kinematics—a generalisation of Bayesian conditioning to contexts in which the evidence is ‘uncertain’—as his two most important contributions to philosophy. This paper aims to connect them by developing kinematical models for the study of preference change and practical deliberation. Preference change is treated in a manner analogous to Jeffrey’s handling of belief change: not as mechanical outputs of combinations of intrinsic desires plus information, but as a matter of judgement and of making up one’s mind. In the first section Jeffrey’s probability kinematics is motivated and extended to the treatment of changes in conditional belief. In the second, analogous kinematical models are developed for preference change and in particular belief-induced change that depends on an invariance condition for conditional preference. The two are the brought together in the last section in a tentative model of pratical deliberation. This paper is one of a pair dedicated to Richard Jeffrey and prepared for a workshop held in his memory at the 26th International Wittgenstein Symposium. My thanks to the organisers of, and the participants in, this workshop and to two anonymous referees for their comments.     

风险决策和跨期决策的过程比较：以确定效应和即刻效应为例

风险决策和跨期决策与人类生存发展密切相关, 且两类决策在理论发展、行为效应及神经基础等方面具有相似性。为检验二者是否具有共同过程机制, 本研究以风险决策中的确定效应和跨期决策中的即刻效应为例, 采用眼动追踪技术比较了它们的局部、整体过程及模型拟合。辅以贝叶斯因子分析实验数据表明：二者的主要过程特征均相似, 且更符合非折扣模型假设; 二者在加工复杂程度等少数特征上有所不同; 确定和即刻信息在加工方向等特征上存在特异性。这表明二者可能具有共同的核心决策规则：两类决策更可能遵循非折扣模型预期的简捷、启发式规则, 而不是折扣模型所假设的补偿性、基于选项规则。本研究为建立两类决策的共同解释框架做出了有益尝试, 并为决策比较研究方法提供新的方向。     

Dimensions of fatigue in different working populations

Perceived fatigue related to work has often been measured in one dimension. The main purpose of the present study was to validate a proposed five-factor model of perceived fatigue in a new sample. 597 persons, employed in five occupations with different types of work loads, rated their fatigue after work. The ratings were subjected to analyses of linear structural equation models. The results suggest a slightly revised model for perceived fatigue, still with the five dimensions: Lack of energy, Physical exertion, Physical discomfort, Lack of motivation and Sleepiness. As expected, the rating profiles describing fatigue states differed between the five occupations. On the basis of these results, a revised version of the Swedish Occupational Fatigue Inventory (SOFI) is presented.     

Information seeking on Bayesian conditional probability problems: A fuzzy-trace theory account

Recently, the ‘heuristics and biases’ approach to the study of decision making has been criticized, with a call for better integrated theory. Three experiments stemming from fuzzy-trace theory addressed information seeking on probability problems, and the cognitive representation of hit-rates, base-rates, and the contrapositive. As predicted by the fuzzy-trace principle of ‘denominator neglect’, many subjects exhibited ‘conversion errors’, confusing the hit-rate, P(A|B), with the answer, P(B|A). These subjects sought base-rates less often than other subjects. On causal problems, more subjects correctly represented base-rates, sought base-rates more often, and produced more accurate estimates than on non-causal problems. Subjects tutored on the meaning of the hit-rate sought the base-rate more often, and were more accurate than control subjects. Results are explained by fuzzy-trace theory principles of gist extraction, fuzzy processing preference, denominator neglect, and output interference.     

贝叶斯统计方法的应用与现状

贝叶斯统计方法是心理学数据分析的热门方法。研究全面论述贝叶斯方法在心理学领域的应用与方向。现阶段贝叶斯方法以模拟研究为主,应用方向为心理学研究常用的项目反应理论、认知诊断、计算机自适应、结构方程模型。同时,评述发现贝叶斯方法正逐步被国内心理学研究者所接受。最后,文章讨论了当下贝叶斯统计在心理学研究中应用的局限性及可能的原因,建议统计学者开发界面友好的贝叶斯软件,并在心理学课程中加入贝叶斯知识。     

Integrating Covariates into Social Relations Models: A Plausible Values Approach for Handling Measurement Error in Perceiver and Target Effects

The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. However, the estimated individual SRM effects might not provide a very reliable measure of the true, unobserved SRM effects, resulting in distorted estimates of associations with other variables. This article introduces a plausible values approach that allows users to correct for measurement error when assessing the association of individual SRM effects with other individual difference variables. In the plausible values approach, the latent, true individual SRM effects are treated as missing values and are imputed from an imputation model by applying Bayesian estimation techniques. In a simulation study, the statistical properties of the plausible values approach are compared with two approaches that have been used in previous research. A data example from educational psychology is presented to illustrate how the plausible values approach can be implemented with the software WinBUGS.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

潜变量建模的贝叶斯方法

贝叶斯统计是统计学的两大流派之一,近年来贝叶斯统计在社会及行为科学领域日益流行。鉴于国内心理学界对贝叶斯统计应用仍不广泛,本文尝试从非技术性的角度对贝叶斯统计用于潜变量建模的过程进行简要介绍。主要涉及贝叶斯与频率论在统计学基本概念上的对比;贝叶斯统计的基本原理和分析过程。最后以一个验证性因子分析为例,简要介绍贝叶斯统计用于潜变量建模的分析过程。希望本文能为国内心理学者进行潜变量建模提供新的视角。     

Assessment of Differential Rater Functioning in Latent Classes with New Mixture Facets Models

Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity. However, DRF may occur when the group membership is unknown (latent) and thus has to be estimated from data. To solve this problem, in this study, we developed a new mixture facets model to assess DRF when the group membership is latent and we provided two empirical examples to demonstrate its applications. A series of simulations were also conducted to evaluate the performance of the new model in the DRF assessment in the Bayesian framework. Results supported the use of the mixture facets model because all parameters were recovered fairly well, and the more data there were, the better the parameter recovery.     

Enhancing treatment effectiveness through social modelling: A pilot study

Objective: Medical treatments take place in social contexts; however, little research has investigated how social modelling might influence treatment outcomes. This experimental pilot study investigated social modelling of treatment effectiveness and placebo treatment outcomes.

Design: Fifty-nine participants took part in the study, ostensibly examining the use of beta-blockers (actually placebos) for examination anxiety. Participants were randomly assigned to observe a female confederate report positive treatment effects (reduced heart rate, relaxed, calm) or feeling no different.

Main outcome measures: Heart rate, anxiety and blood pressure were assessed, as were symptoms and attributed side effects.

Results: Heart rate decreased significantly more in the social modelling compared to control condition, p = .027 (d = .63), and there were trends towards effects in the same direction for both anxiety, p = .097 (d = .46), and systolic blood pressure, p = .077 (d = .51). Significant pre-post placebo differences in heart rate, anxiety and diastolic blood pressure were found in the social modelling group, ps < .007 (ds = .77–1.37), but not the control condition, ps > .28 (ds = .09–.59).

Conclusions: Social observation of medication effectiveness enhanced placebo effectiveness in heart rate, and showed a trend towards enhancing treatment effectiveness in both anxiety and systolic blood pressure. Social modelling may have utility in enhancing the effectiveness of many active medical treatments.