Analogical problem solving: a hierarchical analysis of procedural similarity

Similarity between source analogues and target problems is a central theme in the research on analogical transfer. Much of the theorizing and research has focused on the effects of superficial and structural similarity on transfer. The present research is an attempt to analyze systematically another critical type of similarity, namely, procedural similarity, and to examine its effects on the executing process. Participants viewed a schematic picture as a source model, interpreted its conceptual meaning, and then attempted to solve a problem to which the conceptual information from the source model could be applied. The results indicate that the ease with which a source solution was implemented was largely determined by the abstraction level at which a solution was shared by a source analogue and the target problem. The degree of procedural similarity was also found to influence the executing process in analogical transfer. A conceptual model concerning the function of procedural similarity as a utilizational constraint in analogical problem solving is proposed.     

Bayesian generic priors for causal learning

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.     

Exemplars in the mist: the cognitive substrate of the representativeness heuristic

The idea that people often make probability judgments by a heuristic short-cut, the representativeness heuristic, has been widely influential, but also criticized for being vague. The empirical trademark of the heuristic is characteristic deviations between normative probabilities and judgments (e.g., the conjunction fallacy, base-rate neglect). In this article the authors contrast two hypotheses concerning the cognitive substrate of the representativeness heuristic, the prototype hypothesis (Kahneman & Frederick, 2002) and the exemplar hypothesis (Juslin & Persson, 2002), in a task especially designed to elicit representativeness effects. Computational modelling and an experiment reveal that representativeness effects are evident early in training and persist longer in a more complex task environment and that the data are best accounted for by a model implementing the exemplar hypothesis.     

The CLASSI-N Method for the Study of Sequential Processes

In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the relationship between these mediating variables and the response. Recently, Ceulemans and Van Mechelen (Psychometrika 73(1):107–124, 2008) addressed these challenges by developing the CLASSI model. A major drawback of CLASSI is that it requires information about the same set of stimuli for all participants (i.e., crossed data), whereas recently a number of data gathering techniques have been proposed in which the set of stimuli differs across participants, yielding nested data. Therefore we present the CLASSI-N model, which extends the CLASSI model to nested data. A simulated annealing algorithm is proposed. The results of a simulation study are discussed as well as an application to data concerning depression.     

汉字形声字声旁的语义加工(英文)

作为汉字书写系统和意义表达基本单位的汉字可分为独体字(占5％)和合体字(占95％)两大类。绝大多数合体字由两部分组成：义旁(通常在左边)和声旁(通常在右边)。声旁能够为这些合体字提供语音信息。根据声旁的读音与整字的读音是否一致,可把合体字分为规则字和不规则字。本研究的目的在于考察合体字声旁的亚词汇加工是否仅仅是一个纯粹的语音事件,只涉及声旁的语音加工,还是同时也是一个语义事件,涉及到声旁语义信息的激活。要求被试对屏幕上先后呈现的合体字(如“冯”)和与其声旁语义相关的字(“牛”)作语义相关判断。整字之间并无语义关系,被试正确的反应应是“否”。实验结果表明,相对于完全无关的控制组(如“冯——后”)来说,被试对声旁相关组的反应明显减慢,出现了抑制效应。这种效应基本不受整字的读音规则性和呈现顺序的影响。这些实验结果表达了合体字加工中的分解和平行激活过程。在整字加工的同时,声旁在心理词典中的语音和语义表征得到了激活。汉字声旁的亚词汇加工既是个语音事件,也是个语义事件,与词汇水平的加工没有本质的区别。