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。     

Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

Transportability of equivalence-based programmed instruction: efficacy and efficiency in a college classroom

College students in a psychology research-methods course learned concepts related to inferential statistics and hypothesis decision making. One group received equivalence-based instruction on conditional discriminations that were expected to promote the emergence of many untaught, academically useful abilities (i.e., stimulus equivalence group). A negative control group received no instruction, and a positive (complete instruction) control group received instruction on all possible relations (those taught to, and emerging untaught in, the stimulus equivalence group). On posttests, the stimulus equivalence group performed as well as the positive control group (and both outperformed the negative control group), but those in the equivalence-based instruction condition achieved this outcome with significantly less training, thereby demonstrating the efficiency of equivalence-based instruction. Social validity measures indicated that participants found the instruction to be beneficial and as enjoyable as traditional teaching methods.     

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.     

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。     

The Generalized Quantum Episodic Memory Model

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over‐distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over‐distribution effect and subadditivity have been widely observed in item and source‐memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal‐detection theory. A dual‐trace model called the over‐distribution (OD) model (Brainerd & Reyna, 2008) can explain the episodic over‐distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory (GQEM) model, which extends the Quantum Episodic Memory (QEM) model developed by Brainerd, Wang, and Reyna (2013). We test GQEM by comparing it to the OD model using data from a novel item‐memory experiment and a previously published source‐memory experiment (Kellen, Singmann, & Klauer, 2014) examining the over‐distribution effect. Using the best‐fit parameters from the over‐distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory.     

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.