Identification and Testing of Modes in Beliefs

Characterizing diversity in beliefs is of paramount importance in empirical research on human behavior. In game theory, the focus is on the hypothesis formation. We present a data set with ideal properties for characterizing such diversity. The data consist of hypotheses on the empirical distribution of opponent choices. Using nonparametric techniques, we identify modes in the data corresponding to subpopulations of behavioral types and find the statistical significance of these modes in the underlying population distribution.     

The Function, Mechanism, And Evolution Of Learning: A Review Of Sara J. Shettleworth’s Cognition, Evolution, And Behavior.

Behavior is a property of living animals and is therefore a biological phenomenon. This book shows us what it looks like to have a truly biological science of behavior. Such a science needs to discover the laws that control behavior as it is occurring, and it is this that behavior analysts and other psychologists interested in animal behavior and learning have done so well. The science also needs to explain, however, the role that behavior plays in the life of the individual and in the existence of the species, and this has not been part of the agenda for most psychologists. Shettleworth addresses all of these questions about behavior. She views learning in terms of what it accomplishes for the individual and then provides insight into its causal laws and its evolution. All of this is accomplished with a critical eye and unremitting rigor. These accomplishments occur in the context of a theory based on a unique combination of domain‐general and domain‐specific processes that takes a major step in the direction of showing what students of animal behavior and animal learning have to offer each other.     

错误管理理论:一种新的认知偏差理论

基于统计学上的两类基本错误而提出的错误管理理论,为一种新的认知偏差--适应性偏差提供了理论上的解释.作者在阐述这一理论的同时,指出了错误管理理论在社会心理学领域,尤其在侵犯等一些应用性的社会心理领域,具有不可估量的理论意义和实践价值.     

大学生实时距、空时距估计的比较研究

本研究采用2(空时距、实时距)×8(1秒～8秒)的实验设计,以224名大学生为被试,考察了不同类型、不同长度的时距估计特点,结果发现;(1)时距类型在时距估计中存在主效应,在本实验中,1秒～8秒条件下实时距估计比空时距准确;(2)时距长度在时距估计中存在主效应,时距估计所产生的误差随时距长度的增加而增大;(3)时距类型与时距长度之间存在交互作用,对实时距、空时距估计进行曲线估计及预测,发现两类时距估计呈现动态特征,1845052秒之前实时距估计比空时距准确,之后空时距估计比实时距准确,两类时距估计曲线的变化速度也呈交替上升趋势。     

个人目标相关性对非临床抑郁者未来想象的影响

通过两个实验考查了非临床抑郁者未来想象的异常是否受到个人目标相关性的调节。实验1采用未来想象任务, 实验2采用可能性评估范式, 两个实验一致发现, 抑郁倾向者想象未来积极事件的异常, 受到了与个人目标相关性的调节：相对于非抑郁倾向者, 抑郁倾向者对未来与个人目标相关的积极事件的预期减弱, 而对未来与个人目标无关的积极事件的预期则没有表现出异常; 同时还发现, 抑郁倾向者表现出了对未来消极预期的普遍增强, 不受与个人目标相关性的影响。     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.This research was mostly supported by the Office of Naval Research (N00014-77-C-0360, N00014-81-C-0569, N00014-87-K-0320, N00014-90-J-1456).     

Do people reason rationally about causally related events? Markov violations,weak inferences,and failures of explaining away

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X₁ → Y → X₂, common cause structures X₁ ← Y → X₂, and common effect structures X₁ → Y ← X₂, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative “explaining away” pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations.     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.