知觉加工中存在颜色类别知觉效应的证据

对于颜色的辨别具有类别知觉效应：类间两种颜色的辨别能力比同等颜色空间距离的类内两种颜色的辨别能力更高。对于类别知觉效应的产生机制存在两种观点：知觉特性假设、语言标签假设。以往的研究范式由于实验任务涉及到工作记忆成分,被试在完成任务时会自动地对颜色命名以利于记忆,因此所得证据大多支持语言标签假设,而对知觉特性假设的支持证据则很少。本文利用目标觉察范式最大限度去掉了工作记忆成分,通过测量被试辨别两种颜色的反应时,得到了类别知觉效应。并通过语言干扰任务进一步证实在该实验范式下类别知觉效应与语言的无关性。从而,为知觉特性假设提供了证据     

不确定性决策违背“确定事件原则”的心理机制再探

不确定性决策违背＂确定事件原则＂的心理机制再探从决策理由切入,在现实情境中考察了违背确定事件原则（sure-thing principle）的心理机制,检验了基于理由（reason-based）的假设。研究结果表明,违背确定事件原则的被试在不确定条件下知觉到的理由冲突程度显著高于遵循确定事件原则的被试在不确定条件下知觉到的理由冲突程度;违背确定事件原则的被试在不确定条件下知觉到的理由清晰程度显著低于其在两种确定条件下知觉到的理由清晰程度,而遵循确定事件原则的被试在不确定条件下知觉到的理由清晰程度则介于其在两种确定条件下知觉到的理由清晰程度之间。研究结果支持了基于理由的假设。     

假设检验思维过程中的启发式策略研究

本研究运用自编的一个关于规则发现的计算机程序研究了大学生问题解决过程中形成假设、设计实验、实施检验三个阶段上的“假设———检验”的思维过程。结果发现,规则差别对规则发现有显著影响,反馈方式在本实验条件下没有主效应,启发式是假设检验过程中的重要思维策略     

反馈延迟与掩蔽类型对知觉类别学习的影响

通过两个实验探讨反馈延迟与掩蔽类型对知觉类别学习的影响。实验1光栅掩蔽条件下,采用2（类别结构：基于规则vs.信息整合）×2（延迟反馈：500vs.3000ms）被试间实验设计,实验2采用同样的实验设计,保持延迟反馈时间不变,但改掩蔽类型为黑屏掩蔽。结果发现：（1）光栅掩蔽条件下,反馈延迟削弱信息整合类别结构的成绩,不影响基于规则类别学习,内隐外显类别结构之间存在分离效应。（2）黑屏掩蔽条件下,信息整合与基于规则类别结构的成绩均不受到延迟反馈的影响,分离效应消失。实验说明“无关因素”掩蔽影响知觉类别学习,知觉噪音与标准噪音在反馈延迟影响知觉类别学习中起到重要作用,COVIS模型理论关于延迟反馈时间影响知觉类别学习的解释具有局限性。     

类别数量对基于规则和信息整合类别学习的影响

研究以大学生为被试,运用即时反馈训练范式,探讨类别数量对基于规则和信息整合两种结构的类别学习的影响,并探讨被试在类别学习过程中的反应策略。实验结果显示:(1)类别数量不仅影响基于规则结构的类别学习,也影响信息整合结构的类别学习,类别学习成绩随类别数量的增多而下降;(2)在基于规则类别学习中,大多数被试使用理想的分类规则进行类别判断;在信息整合类别学习中,大多数被试使用极端值策略。研究结果支持难度观,不支持多重系统理论。     

语言表征在颜色类别知觉中的作用

采用同时和继时实验任务,探讨语言表征在颜色类别知觉中的作用.实验一刺激同时呈现,被试表现出类别知觉效应,但效应不受语言干扰影响.实验二刺激间隔呈现,被试表现出类别知觉效应,效应在语言干扰时消失.实验三采用继时任务,没有语言干扰,在刺激呈现间隔(SOA)小于300ms时效应不显著,大于等于300ms时效应显著.研究提示类别知觉效应的机制取决于任务要求和被试无意识的认知策略.在同时任务中效应来自知觉过程本身,在继时任务中效应来自语言编码.继时任务中效应量随SOA变化,说明语言表征由上至下地影响知觉加工,类别知觉是一个动态过程.     

迫选规则体验法: 检验规范性和描述性风险决策理论的新尝试

为寻求检验规范性和描述性风险决策理论的通用标准, 本研究以期望价值理论和齐当别抉择模型为例, 探讨了“迫选规则体验法”的适用性。被试为120名大学生, 实验任务为要求被试分别完成自主决策(采用未知规则: 真规则)和规则迫选决策(遵循给定规则: 假规则)任务, 并对决策后的情感和认可程度进行评定。研究发现: (1) 被试在自主决策条件下比在规则迫选条件下体验到的正性情感程度更强, 负性情感的程度更弱; (2) 被试在自主决策与规则迫选决策两种条件下做出的相同决策越多, 该被试对迫选规则更加认可并体验到的正性情感程度越强, 负性情感的程度越弱; (3) 与期望价值理论相比, 齐当别抉择模型可能符合更多决策者的实际决策规则。这些结果表明, 作为检验规范性和描述性风险决策理论的新尝试, 迫选规则体验法可能更有助于回答“决策者实际采用的决策规则是什么”的问题。     

基于潜在类别分析的6~15岁儿童平衡秤任务认知规则探索

本研究使用自编平衡秤测验测试468名6~15岁儿童,通过潜在类别分析对认知规则进行分类。结果发现,被试使用了规则Ⅰ、规则Ⅰ'、规则Ⅱ、补偿规则、规则Ⅳ、距离优势规则等六种规则;6~9岁儿童主要使用规则Ⅰ;10~13岁儿童主要使用补偿规则;14岁以上儿童主要使用规则Ⅳ;13岁到14岁之间,使用规则Ⅳ的儿童数量呈跳跃式增加。与规则评估技术相比,潜在类别分析用于认知规则研究具有明显优势,最后对运用此方法的前提假设与局限进行了讨论。     

知觉者与行为者贫富对自发特质推理的影响

本研究采用探测词再认范式，分3个研究考察了知觉者和行为者的贫富对个体自发特质推理的影响。研究1考察了知觉者贫富对自发特质推理的影响，结果表明富裕启动条件下的被试更可能进行自发特质推理。研究2考察了行为者贫富对自发特质推理的影响，结果表明被试更有可能对富人刻板一致的行为句子进行自发特质推理。研究3考察了知觉者和行为者贫富对自发特质推理的交互影响，结果发现仅富裕启动条件下的被试更可能对富人刻板一致句子进行自发特质推理。本研究为自发特质推理发生的灵活性提供了新证据。     

网络词语与非网络词语的知觉差异:时间知觉、空间距离知觉与知觉范围

根据具身认知理论,人们的认知会受到身体活动的影响。网络环境下人的身体活动受到限制,因而其认知活动与非网络环境存在很大的差异。本研究目的在于考察人们对网络词语与非网络的日常词语的时间知觉,空间距离知觉与知觉范围是否有差异。结果发现被试对网络词语的时间知觉更长;网络词语的空间距离感与日常词语没有显著差异;在启动网络词语之后,被试的知觉范围缩小,对中心刺激的反应显著快于边缘刺激。本研究发现了网络词语加工在时间知觉和知觉范围上的变化。     

Estimation of the hypothesis hierarchy in probabilistic inference tasks*

Is there a hierarchical order among the hypotheses about functional rules in probabilistic inference tasks, i.e. what is the construction and the procedure of the “hypothesis sampling mechanism” employed by the subjects in this kind of task? According to the hypothesis sampling model initially proposed by Brehmer (1974) there should be a hierarchical order among the hypotheses in the subject's hypothesis pools. The procedures of hypothesis sampling and testing ought to follow this strict data independent order (see e.g. Sniezek, 1986; Brehmer, 1987). Knez (1991a, b) showed, however, that this assumption may be incorrect. As a follow up to these results the question regarding the construction of the subject's hypothesis pools was reapproached in the present study. The results indicated a consistency with the hierarchical assumption (Brehmer, 1974) only regarding the relation between the linear and nonlinear rules but not within these types of rules.     

Subjects' inferential performance and the interaction of data and hypotheses in probabilistic inference tasks

In a recent paper, Knez (1991) showed an interaction of data and hypotheses in probabilistic inference tasks. The results illustrated two, earlier not obtained, significant main effects on subjects' hypothesis sampling , viz. the effect of different forms of data presentation and subjects' execution of cognitive control over their hypothesis pool throughout the series of trials. The present paper followed up these results in that the subjects' hypothesis testing , in Knez (1991) was subjected to an analysis. Hence, to see if the effects mentioned above significantly influenced the subjects' hypothesis testing, as they did for subjects' hypothesis sampling. The results showed a consistency with Knez (1991), i.e. the results emphasize the interaction of data and hypothesis in probabilistic inference tasks, as well as the subjects' execution of cognitive control over their hypothesis pool concerning both the subjects' hypothesis sampling and testing.     

Exploring the determinants of dual goal facilitation in a rule discovery task

Wason's standard 2-4-6 task requires discovery of a single rule and leads to around 20% solutions, whereas the dual goal (DG) version requires discovery of two rules and elevates solutions to over 60%. We report an experiment that aimed to discriminate between competing accounts of DG facilitation by manipulating the degree of complementarity between the to-be-discovered rules. Results indicated that perfect rule complementarity is not essential for task success, thereby undermining a key tenet of the goal complementarity account of DG facilitation. The triple heterogeneity account received a good degree of support since more varied triple exploration was associated with facilitatory DG conditions, in line with this account's prediction that task success is associated with the creative search of the problem space. The contrast class account (an extension of Oaksford & Chater's, 1994, iterative counterfactual model) was also corroborated in that the generation of descending triples was demonstrated to be the dominant predictor of DG success. We focus our discussion on conceptual ideas relating to the way in which iterative counterfactual testing and contrast class identification may work together to provide a powerful basis for effective hypothesis testing.     

Relevance-driven information search in “pseudodiagnostic” reasoning

When faced with two competing hypotheses, people sometimes prefer to look at multiple sources of information in support of one hypothesis rather than to establish the diagnostic value of a single piece of information for the two hypotheses. This is termed pseudodiagnostic reasoning and has often been understood to reflect, among other things, poor information search strategies. Past research suggests that diagnostic reasoning may be more easily fostered when participants seek data to help in the selection of one of two competing courses of action as opposed to situations where they seek data to help infer which of two competing hypotheses is true. In the experiment reported here, we provide the first empirical evidence demonstrating that manipulating the relevance of the feature for which participants initially receive information determines whether they will make a nominally diagnostic or pseudodiagnostic selection. The discussion of these findings focuses on implications for the ability to engage in diagnostic hypothesis testing.     

Seeking Confirmation Is Rational for Deterministic Hypotheses

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best‐known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4).     

Learning Problem‐Solving Rules as Search Through a Hypothesis Space

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design.     

Evaluation of hypotheses in concept identification

The aim of this paper is to investigate the nature of hypothesis evaluation in conceptual tasks, especially in the identification of bidimensional concepts. In such tasks, hypothesis testing is seen as being composed of sampling and evaluation stages. With complex problems only one hypothesis seems likely to be sampled on each trial, and it is suggested that this hypothesis is evaluated according to a statistical decision-making process. In Experiment I, Ss were given an initial hypothesis involving one of eight rules and required to test it in an attempt to find a concept. When this given hypothesis was true (i.e., the concept), no difference was found between rules for the number of instances selected to the criterion of solution. Moreover, there was a tendency for Ss to choose instances which were predicted to be positive according to the hypothesis under test. Experiment II examined the role of memory in hypothesis testing. Immediate recall of instances selected revealed no difference between true and false hypotheses. Both primacy and recency effects were evident in recall. The number of instances correctly recalled was more than expected by models of hypothesis sampling and evaluation, and this was attributed to Ss having a low criterion for recall without intrusions. Examination of intrusions suggested that Ss may have retained some, but not all, of the features of the stimuli selected. Some differences were found between rules and between positive and netative instances on recall. These effects were suggested to be due to different amounts of information processing when classifying each type of instance for each rule; the results of Experiment III supported this suggestion.     

Interaction of data and hypotheses in probabilistic inference tasks: Rejection of the hypothesis sampling model?

The problem of whether or not data influence subjects' sampling of hypotheses about functional relations in the probabilistic inference tasks was examined. The results of the experiment show first, a considerable impact of data on subjects' hypothesis selection and second, that the subjects significantly modify their hypothesis selection from one part of the experiment to the other. Thus, there is an interaction of data and hypotheses in cue probability learning (CPL) tasks regarding the hypothesis sampling process and an execution of cognitive control over the subjects' hypothesis pool.     

A peculiar prevalence of p values just below .05

In null hypothesis significance testing (NHST), p values are judged relative to an arbitrary threshold for significance (.05). The present work examined whether that standard influences the distribution of p values reported in the psychology literature. We examined a large subset of papers from three highly regarded journals. Distributions of p were found to be similar across the different journals. Moreover, p values were much more common immediately below .05 than would be expected based on the number of p values occurring in other ranges. This prevalence of p values just below the arbitrary criterion for significance was observed in all three journals. We discuss potential sources of this pattern, including publication bias and researcher degrees of freedom.     

The Savanna-IQ interaction hypothesis: A critical examination of the comprehensive case presented in Kanazawa's The Intelligence Paradox

Kanazawa (2012b) has recently presented the most comprehensive case yet for his Savanna-IQ Interaction Hypothesis. According to the hypothesis, intelligence is a domain-specific adaptation which has been selected for as humans have moved away from the (evolutionarily familiar) Savanna. As such, ability in ‘evolutionarily novel’ tasks and ‘evolutionarily novel’ preferences are positively correlated with high IQ. This article will present a critical examination of the hypothesis, arguing that there is a strong case against anchoring human nature on the Savanna, the hypothesis predicts contradictory findings, there is empirical evidence against it, it is not falsifiable, and it is not necessary to explain that data which Kanazawa presents.