进化频率表征对统计推理（非）灵活性的影响：认知风格和简要提示不影响贝叶斯推理

当形式操纵有助于贝叶斯推理时会有怎样的加工发生呢？一种观点认为自然取样的频率可以激发在其操作中具有相对特异性的特权表征系统。而与之相对的一种观点则认为,自然取样频率只是引发具有嵌套关系的更为普遍的加工的一种方式。比较两种观点,后者预示着只需要使用相当简要和直接的干预（如简单的指示）就能够促进推理的改善,而前者则意味着更为广泛的干预和/或更有洞见的理解才能改善推理。本研究表明,无论是短暂立即的干预,还是预存的表征偏向,抑或是表征的灵活性都不能促进被试的表现。另一方面,也有证据显示,频率论者的问题解释可以改善统计推理表现,而且有时还会增加其反应的信心。这些结果支持了特权表征系统观     

A Bayesian view of covariation assessment

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants' prior beliefs about the variables' relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.     

推理与工作记忆

系统地总结了推理与工作记忆的研究进展,具体包括：（1）工作记忆的含义及其在推理中作用的相关研究;（2）推理与工作记忆关系的理论模型;（3）不同类型推理与工作记忆的实验研究;（4）推理与工作记忆的认知神经研究;（5）推理与工作记忆的未来研究展望     

类似性比较对3～5岁儿童内隐推理的影响

本研究以三个实验探讨了3～5岁儿童词汇学习的内隐推理过程。实验1首先让被试者看一个熟悉物体的标准图片,分别以熟悉的或者不熟悉的名词命名,然后要求从两个选项中选择一个图片与标准图片匹配。结果表明3～4岁儿童可以不熟悉名词完成词汇扩展作业,但是只有5岁儿童可以熟悉名词完成词汇扩展作业,这表明5岁儿童可能有一个内隐推理加工过程。实验2以不熟悉物体作为标准图片,以熟悉名词与指代词分别作为指导语进一步测试5岁儿童的内隐推理过程,结果表明这个年龄阶段儿童的类概念形成有一种自上而下加工的内隐推理过程。实验3呈现基本概念的图片检验什么因素影响3～5岁儿童词汇学习的内隐推理过程,结果表明在类似性比较条件下与3～4岁儿童相比,5岁儿童更加明显地表现了内隐推理的加工过程。最后以类似性理论讨论了本研究的结果。     

两种学习模式下类别学习的结果：原型和样例

利用“学习－迁移”的任务范式和单一特征类别判断技术,探讨了分类和推理两种类别学习模式的结果,比较了两种学习模式的效果和策略。研究表明：两种学习模式产生了不同的结果,分类学习的结果是样例,推理学习的结果是原型;在学习效果方面,分类学习比推理学习在达标比例上更高,但在进度上差异不显著;在策略运用方面,分类学习比推理学习更快地使用单维度策略,而在高水平策略的运用上,两者差异不显著     

Observing evidence accumulation during multi-alternative decisions

Most decision-making research has focused on choices between two alternatives. For choices between many alternatives, the primary result is Hick’s Law—that mean response time increases logarithmically with the number of alternatives. Various models for this result exist within specific paradigms, and there are some more general theoretical results, but none of those have been tested stringently against data. We present an experimental paradigm that supports detailed examination of multi-choice data, and analyze predictions from a Bayesian ideal observer model for this paradigm. Data from the experiment deviate from the predictions of the Bayesian model in interesting ways. A simple heuristic model based on evidence accumulation provides a good account for the data, and has attractive properties as a limit case of the Bayesian model.     

EQUIVALENCE CLASS FORMATION IN A TRACE STIMULUS PAIRING TWO‐RESPONSE FORMAT: EFFECTS OF RESPONSE LABELS AND PRIOR PROGRAMMED TRANSITIVITY INDUCTION

Three experiments identified factors that did and did not enhance the formation of two‐node four‐member equivalence classes when training and testing were conducted with trials presented in a trace stimulus pairing two‐response (SP2R) format. All trials contained two separately presented stimuli. Half of the trials, called within‐class trials, contained stimuli from the same class while the other half, called cross class trials, contained stimuli from different classes. On within class trials, making a YES response was correct and making a NO response was wrong. On cross class trials, making a NO response was correct and making a YES response was wrong. In Experiment 1, similar intermediate percentages of participants (about 50%) formed classes, regardless of whether the responses were labeled YES and NO or SAME and DIFF. Response labeling thus did not influence class formation. Regardless of response labels, failures of class formation were primarily due to failure of class‐indicative responding produced by within‐class transitivity probes. In Experiment 2, only 50% of participants formed classes without prior training, as in Experiment 1, but 100% of participants formed equivalence classes after the establishment of a generalized transitivity repertoire by use of a programmed transitivity induction protocol. Experiment 3 examined two components of the programmed transitivity induction protocol and found that the exclusion of AC trials had no effect on the percentage of participants who formed equivalence classes, while presenting the stimulus sets in randomized order interfered with equivalence class formation. A further analysis found that a number of stimulus control topographies differentiated between individuals who did and did not form equivalence classes. In general, then, these experiments demonstrate that equivalence classes can be formed reliably when training and testing are conducted in an SP2R format, supporting the view that equivalence class formation can account for the development of conceptual categories in natural settings.     

Analyzing Machine-Learned Representations: A Natural Language Case Study

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero-shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human-like language understanding.     

Familiarity-Matching: An Ecologically Rational Heuristic for the Relationships-Comparison Task

Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., “Which city has the larger population, city A or city B?”). However, objects can be presented not only as alternatives but also in a question (e.g., “Which country is city X in, country A or country B?”). In such a situation, people can make inferences based on the relationship between the object in the question and each object given as an alternative. In the present study, we call this type of task a “relationships-comparison task.” We modeled the three inference strategies that people could apply to solve it (familiarity-matching [FM; a new heuristic we propose in this study], familiarity heuristic [FH], and knowledge-based inference [KI]) to examine people's inference processes. Through Studies 1, 2, and 3, we found that (a) people tended to rely on heuristics, and that FM (inferences based on similarity in familiarity between objects) well explained participants' inference patterns; (b) FM could work as an ecologically rational strategy for the relationships–comparison task since it could effectively reflect environmental structures, and that the use of FM could be highly replicable and robust; and (c) people could sometimes use a decision strategy like FM, even in their daily lives (consumer behaviors). The nature of the relationships–comparison task and human cognitive processes is discussed.     

Temporal Binding,Causation, and Agency: Developing a New Theoretical Framework

In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency.” However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause–effect sequence. In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, has been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people's causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding.