学习因素对语义信息加工性质的影响

本研究通过两个行为实验,采用句子核证作业,将SAD技术和范畴学习范式有机地结合起来,对语义信息加工进程以及学习因素在其中的作用进行了考察。实验一研究了熟悉性对语义信息加工的影响。结果表明,高熟悉句的部分信息积累具有离散的趋势;低熟悉句的部分信息积累是连续性的。实验二采用范畴学习。并结合SAD技术的句子核证作业．考察了在学习过程中语义加工性质的变化。结果发现：过度学习比初始学习有更多的部分信息积累,并随信号间隔的延长而逐渐增加;过度学习表现出部分信息积累的离散性变化;初始学习则表现出连续累积的趋势。说明学习确实造成了连续性加工向离散性质的过渡。     

在多媒体语境条件下词类对儿童英语词汇学习效果的影响

该研究考察了多媒体动画片语境条件下,词类对儿童英语词汇学习效果的影响,结果发现:(1)儿童对名词和形容词的再认成绩显著优于代词;(2)就独立回忆测验而言,名词的成绩显著高于代词,形容词的成绩处于代词和名词之间,但与二者的差异均不显著;(3)就情境回忆测验而言,名词与形容词的成绩差异不显著,二者均显著高于代词;(4)名词与形容词的情境回忆测验成绩均显著高于独立回忆测验成绩,但代词的独立回忆与情境回忆测验成绩差异不显著。总体而言,儿童对名词和形容词的学习效果好于代词,表明词类会对儿童英语词汇学习效果产生影响。     

Learning: Association or Computation? Introduction to a Special Section

The fundamental nature of learning is a central problem in psychology. Traditionally, psychologists have assumed that learning must involve the formation of associations. Early last century, the pioneering work of Pavlov on conditioned learning in animals seemed to put this assumption beyond doubt. More recently, many psychologists came to believe that a different kind of process must underlie complex learning, such as language learning in humans, and that this process must be described as computational rather than associative. Whether complex human learning is associative or computational continues to be a subject of intense research. The articles in this Special Section turn this debate on its head by asking whether simple animal learning is associative or computational. Surprisingly, the question is still very much open, and excitingly, it appears quite tractable.     

Causal Learning: Association Versus Computation

Causal learning enables humans and other animals not only to predict important events or outcomes, but also to control their occurrence in the service of needs and desires. Computational theories assume that causal judgments are based on an estimate of the contingency between a causal cue and an outcome. However, human causal learning exhibits many of the characteristics of the associative learning processes thought to underlie animal conditioning. One problem for associative theory arises from the finding that judgments of the causal power of a cue can be revalued retrospectively after learning episodes when that cue is not present. However, if retrieved representations of cues can support learning, retrospective revaluation is anticipated by modified versions of standard associative theories.     

抽象动词的具身表征：运动特征的影响

本研究使用具身行为范式—动作-句子相符效应（ACE）探讨了动词运动特征在抽象动词具身表征中的作用。实验1验证ACE在考察感觉运动系统参与双音节动词表征问题上的可行性,结果在具体动词中得到了ACE。实验2以不同运动特征值的抽象动词为材料,在高运动抽象动词中得到了ACE,但在低运动抽象动词中未发现ACE,且ACE大小与运动特征评分显著相关。结果证明了感觉运动系统在抽象动词表征中的参与,支持抽象概念的具身表征观点。     

Spatial navigation: Cognitive variables involved in route retracing among an elderly population

The cognitive processes involved in route retracing are not well known. This study aims to highlight them in an elderly population in which contradictory results have been obtained, certain studies showing specific difficulties for route retracing, others not. Thirty-nine elderly subjects performed a route-learning task (forward-backward) in a garden, then completed spatial knowledge tasks and standardised cognitive tests. Results show four factors that were predictive of route retracing performance: route repetition, the pointing task, and two standardised cognitive tests, one assessing spatial working memory, and another global cognitive efficiency. According to these results, route retracing involves route and survey knowledge (i.e., egocentric and allocentric strategy), and the integration of forward-backward perspectives is underpinned by the spatial working memory. Moreover, the subjects did not make more errors in route retracing than in the route repetition task, suggesting that a real environment could compensate for a failing allocentric strategy.     

Reproducibility and a unifying explanation: Lessons from the shape bias

The goal of science is to advance our understanding of particular phenomena. However, in the field of development, the phenomena of interest are complex, multifaceted, and change over time. Here, we use three decades of research on the shape bias to argue that while replication is clearly an important part of the scientific process, integration across the findings of many studies that include variations in procedure is also critical to create a coherent understanding of the thoughts and behaviors of young children. The “shape bias,” or the tendency to generalize a novel label to novel objects of the same shape, is a reliable and robust behavioral finding and has been shown to predict future vocabulary growth and possible language disorders. Despite the robustness of the phenomenon, the way in which the shape bias is defined and tested has varied across studies and laboratories. The current review argues that differences in performance that come from even seemingly minor changes to the participants or task can offer critical insight to underlying mechanisms, and that working to incorporate data from multiple labs is an important way to reveal how task variation and a child’s individual pathway creates behavior—a key issue for understanding developmental phenomena.     

What Determines Visual Statistical Learning Performance? Insights From Information Theory

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.