预期推理在长时记忆中的表征形式

该研究采用移动窗口技术,通过两个实验研究探讨在高低不同竞争条件下预期推理结果在长时记忆中的表征形式。实验结果表明,预期推理在长时记忆中的表征形式依赖于文本中预期推理的引发语境,在不同竞争强度的预期语境中,预期推理的结果在长时记忆会以不同的形式进行表征。在低竞争条件下,预期推理的结果以确定的形式表征;只有在高竞争条件下,预期推理的结果才会以假定的形式进行表征。     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.     

共同方法变异的影响及其统计控制途径的模型分析

共同方法变异(common method variance, CMV)指两个变量之间变异的重叠是因为使用同类测量工具而导致, 而不是代表潜在构念之间的真实关系。虽然以往研究显示CMV不一定导致研究结果的偏差, 在实际研究中应当加以考量。特别是在使用测量方法的研究中, 如果数据来源越单一, 测量方法越类似, CMV效应使研究结果产生偏差的可能性越大。CMV效应的控制方法包括过程控制法和统计控制法。在统计控制法的选择和使用上, 需要重点考虑该方法是否分离了三大变异(特质变异、方法变异和误差变异), CMV效应是在测量构念层面还是题目层面, CMV效应是加法效应还是乘法效应。控制潜在方法因子途径是统计控制方法中最重要的一类方法, 理解其模型是正确使用这类方法的前提。未来研究应当关注多个研究的CMV效应和侧重评估某个理论研究中CMV所引起的潜在的效度威胁。     

条件推理中的二元效应初探

通过预备实验选取前件(或后件)是二元(或多元)的5种条件命题的4种推理形式作为实验材料,以大学本科生为被试,考察了五种不同条件命题类型对4种推理结果的影响.结果表明命题类型、推理规则以及两者的交互作用都非常显著.这个结果进一步验证了条件推理的集合映射模型.     

A multilevel framework for understanding relationships among traits,states, situations and behaviours

A conceptual and analytic framework for understanding relationships among traits, states, situations, and behaviours is presented. The framework assumes that such relationships can be understood in terms of four questions. (1) What are the relationships between trait and state level constructs, which include psychological states, the situations people experience and behaviour? (2) What are the relationships between psychological states, between states and situations and between states and behaviours? (3) How do such state level relationships vary as a function of trait level individual differences? (4) How do the relationships that are the focus of questions 1, 2, and 3 change across time? This article describes how to use multilevel random coefficient modelling (MRCM) to examine such relationships. The framework can accommodate different definitions of traits and dispositions (Allportian, processing styles, profiles, etc.) and different ways of conceptualising relationships between states and traits (aggregationist, interactionist, etc.). Copyright © 2007 John Wiley & Sons, Ltd.     

A Psychometric Analysis of the Child Behavior Checklist DSM-Oriented Scales

The Child Behavior Checklist for Ages 6–18 (CBCL/6-18) possesses newly developed DSM-Oriented Scales, constructed through expert clinical judgment to match selected categories for behavioral/emotional problems as described in the DSM-IV. The present investigation examined the basic psychometric properties for all six DSM-Oriented Scales (i.e., Affective, Anxiety, Somatic, Attention-Deficit/Hyperactivity, Oppositional, and Conduct Scales) in a large clinical sample of children and adolescents (N = 673). Findings from the present study provide strong evidence for the reliability, as well as convergent and discriminative validity, of these scales. It appears that the DSM-Oriented Scales may provide accurate supplementary information that may be considered when formulating clinical diagnoses.

The development of category learning strategies: What makes the difference?

Category learning can be achieved by identifying common features among category members, distinctive features among non-members, or both. These processes are psychologically and computationally distinct, and may have implications for the acquisition of categories at different hierarchical levels. The present study examines an account of children’s difficulty in acquiring categories at the subordinate level grounded on these distinct comparison processes. Adults and children performed category learning tasks in which they were exposed either to pairs of objects from the same novel category or pairs of objects from different categories. The objects were designed so that for each category learning task, two features determined category membership whereas two other features were task irrelevant. In the learning stage participants compared pairs of objects noted to be either from the same category or from different categories. Object pairs were chosen so that the objective amount of information provided to the participants was identical in the two learning conditions. We found that when presented only with object pairs noted to be from the same category, young children (6 ? YO ? 9.5) learned the novel categories just as well as older children (10 ? YO ? 14) and adults. However, when presented only with object pairs known to be from different categories, unlike older children and adults, young children failed to learn the novel categories. We discuss cognitive and computational factors that may give rise to this comparison bias, as well as its expected outcomes.     

When some is actually all: Scalar inferences in face-threatening contexts

Accounts of the scalar inference from ‘some X-ed’ to ‘not all X-ed’ are central to the debate between contemporary theories of conversational pragmatics. An important contribution to this debate is to identify contexts that decrease the endorsement rate of the inference. We suggest that the inference is endorsed less often in face-threatening contexts, i.e., when X implies a loss of face for the listener. This claim is successfully tested in Experiment 1. Experiment 2 rules out a possible confound between face-threatening contexts and lower-bound contexts. Experiment 3 shows that whilst saying ‘some X-ed’ when one knew for a fact that all X-ed is always perceived as an underinformative utterance, it is also seen as a nice and polite thing to do when X threatens the face of the listener. These findings are considered from the perspective of Relevance Theory as well as that of the Generalized Conversational Inference approach.     

A Deep Inference System for the Modal Logic S5

We present a cut-admissible system for the modal logic S5 in a formalism that makes explicit and intensive use of deep inference. Deep inference is induced by the methods applied so far in conceptually pure systems for this logic. The system enjoys systematicity and modularity, two important properties that should be satisfied by modal systems. Furthermore, it enjoys a simple and direct design: the rules are few and the modal rules are in exact correspondence to the modal axioms. Presented by Heinrich Wansing