Category transfer in sequential causal learning: the unbroken mechanism hypothesis

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis.     

Learning categories by making predictions: An investigation of indirect category learning

Categories are learned in many ways, but studies of category learning have generally focused on classification learning. This focus may limit the understanding of categorization processes. Two experiments were conducted in which participants learned categories of animals by predicting how much food each animal would eat. We refer to this as indirect category learning, because the task andthe feedback were not directly related to category membership, yet category learning was necessary for good performance in the task. In the first experiment, we compared the performance of participants who learned the categories indirectly with the performance of participants who first learned to classify the objects. In the second experiment, we replicated the basic findings and examined attention to different features during the learning task. In both experiments, participants who learned in the prediction-only condition displayed a broader distribution of attention than participants who learned in the classification-and-prediction condition did. Some participants in the prediction-only group learned the family resemblance structure of the categories, even when a perfect criterial attribute was present. In contrast, participants who first learned to classify the objects tended to learn the criterial attribute.     

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     

不同学习方式下归类不确定时的特征推理

采用学习-迁移模式, 探讨了同时学习和继时学习两种方式下归类不确定时的特征推理。共包括2个实验, 其中实验1探讨了固定学习轮次的情况, 实验2探讨了固定学习正确率的情况。实验结果表明：同时呈现类别要素的同时学习方式下, 被试习得序列式的单类别表征(原型表征), 在归类不确定时的特征推理中按照“单类的Bayesian规则”进行特征推理, 即P(j\F) =P(k\F)·P(j\k); 相继呈现类别要素的继时学习方式下, 被试习得并列式的多类别表征, 在归类不确定时的特征推理中按照“理性模型”进行推理, 即 P(j\F) =Σk P(k\F)·P(j\k)。     

Categories and causality: the neglected direction

The standard approach guiding research on the relationship between categories and causality views categories as reflecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may influence subsequent causal learning. In three experiments we show that identical causal learning input yields different attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new effect, and when they were motivated by goals that differed from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal effect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal effects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal effect learners abandoned the categories, and used different categories to predict the causal effect.     

类别学习中两种学习模式的比较研究：分类学习与推理学习

采用学习迁移任务范式,使用基于单一特征的类别判断技术,比较了非线性分离结构下,分类学习和推理学习的学习效率、学习过程与策略和学习结果。结果表明：在学习效率上,分类学习比推理学习更好地习得了含有较多样例的类别知识,分类学习的速度上显著快于推理学习。在学习的过程与策略上,推理学习比分类学习更为关注类别内不同特征的相关,但在分类策略的运用上不如分类学习灵活。在学习的结果上,推理学习倾向于原型记忆,分类学习倾向于进行样例记忆,分类学习比推理学习更好地掌握了类别原型     

Observed attention allocation processes in category learning

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that “optimal” patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fail to do so is that under certain conditions the cost of information retrieval or use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an “information-board” display that collects detailed information on participants' patterns of attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost–benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocate attention by weighting stimulus dimensions.     

Observed attention allocation processes in category learning

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that "optimal" patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fail to do so is that under certain conditions the cost of information retrieval or use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an "information-board" display that collects detailed information on participants' patterns of attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost-benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocate attention by weighting stimulus dimensions.     

Unconscious symmetrical inferences: A role of consciousness in event integration

Explicit and implicit learning have been attributed to different learning processes that create different types of knowledge structures. Consistent with that claim, our study provides evidence that people integrate stimulus events differently when consciously aware versus unaware of the relationship between the events. In a first, acquisition phase participants sorted words into two categories (A and B), which were fully predicted by task-irrelevant primes-the labels of two other, semantically unrelated categories (C and D). In a second, test phase participants performed a lexical decision task, in which all word stimuli stemmed from the previous prime categories (C and D) and the (now nonpredictive) primes were the labels of the previous target categories (A and B). Reliable priming effects in the second phase demonstrated that bidirectional associations between the respective categories had been formed in the acquisition phase (A<-->C and B<-->D), but these effects were found only in participants that were unaware of the relationship between the categories! We suggest that unconscious, implicit learning of event relationships results in the rather unsophisticated integration (i.e., bidirectional association) of the underlying event representations, whereas explicit learning takes the meaning of the order of the events into account, and thus creates unidirectional associations.     

关系复杂性对关系类别间接性学习中选择性注意的影响

以大学生为被试,使用关系复杂性逐渐变化的实验材料——4特征复杂关系的虚拟外星生物、6特征复杂关系加二阶同功能简单关系的虚拟外星生物和6特征复杂关系加二阶异功能简单关系的虚拟外星生物,采用类别的间接性学习范式——个人功能预测的关系类别的间接性学习条件和参照性交流的关系类别的间接性学习条件,通过三个实验任务（功能预测、自由分类和维度选择）,探讨材料关系复杂性对关系类别间接性学习中选择性注意的影响。结果发现：随着关系复杂性的逐渐增高,被试的选择性注意水平不存在显著差异,但选择性注意的指向性存在极其显著差异,选择性注意的集中性（对无关维度的抑制）不存在显著差异;参照条件下被试选择性注意水平极其显著地高于个人条件,这种差异主要表现在选择性注意的指向性方面,而不表现在选择性注意的集中性（对无关维度的抑制）方面。     

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.     

A further investigation of category learning by inference

Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.     

Accessing causal relations in semantic memory

Most studies investigating semantic memory have focused on taxonomic or associative relations. Little is known about how other relations, such as causal relations, are represented and accessed. In three experiments, we presented participants with pairs of words one after another, describing events that referred to either a cause (e.g., spark) or an effect (e.g., fire). We manipulated the temporal order of word presentation and the question participants had to respond to. The results revealed that questions referring to the existence of a causal relation are answered faster when the first word refers to a cause and the second word refers to its effect than vice versa. However, no such asymmetry was observed with questions referring to the associative relation. People appear to distinguish the roles of cause and effect when queried specifically about a causal relation, but not when the same information is evaluated for the presence of an associative relation.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.     

Classification versus inference learning contrasted with real-world categories

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories—what each category is like—while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.     

注意和工作记忆对交错呈现优势的影响

以家族相似性图案为材料,让被试在单任务或双任务条件下以集中呈现或交错呈现的方式进行观察（实验1）或反馈（实验2）学习,记录眼动,探究注意对交错呈现优势的影响,以及工作记忆在其中的作用。发现当进行观察学习时,注意影响交错呈现优势,结果支持区别对比理论和注意衰减理论;当进行反馈学习时,注意的影响还有待进一步探究。同时,工作记忆影响交错呈现优势,但工作记忆并非完全通过影响注意从而影响交错呈现优势。     

Sorting out categories: Incremental learning of category structure

Two experiments examine how inferences might promote unsupervised and incremental category learning. Many categories have members related through overall similarity (e.g., a family resemblance structure) rather than by a defining feature. However, when people are asked to sort category members in a category construction task, they often do so by partitioning on a single feature. Starting from an earlier result showing that pairwise inferences increase family resemblance sorting (Lassaline & Murphy, 1996), we examine how these inferences lead to learning the family resemblance structure. Results show that the category structure is learned incrementally. The pairwise inferences influence participants’ weightings of feature pairs that were specifically asked about, which in turn affects their sorting. The sorting then allows further learning of the categorical structure. Thus, the inferences do not directly lead learners to the family resemblance structure, but they do provide a foundation to build on as the participants make additional judgments.     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

Category induction from distributional cues in an artificial language

The ability to identify the grammatical category of a word (e.g., noun, verb, adjective) is a fundamental aspect of competence in a natural language. Children show evidence of categorization by as early as 18 months, and in some cases younger. However, the mechanisms that underlie this ability are not well understood. The lexical co-occurrence patterns of words in sentences could provide information about word categories--for example, words that follow the in English often belong to the same category. As a step in understanding the role distributional mechanisms might play in language learning, the present study investigated the ability of adults to categorize words on the basis of distributional information. Forty participants listened for approximately 6 min to sentences in an artificial language and were told that they would later be tested on their memory for what they had heard. Participants were next tested on an additional set of sentences and asked to report which sentences they recognized from the first 6 min. The results suggested that learners performed a distributional analysis on the initial set of sentences and recognized sentences on the basis of their memory of sequences of categories of words. Thus, mechanisms that would be useful in natural language learning were shown to be active in adults in an artificial language learning task.     

Paralinguistic correlates of conceptual structure

How is conceptual knowledge transmitted during conversation? When a speaker refers to an object, the name that the speaker chooses conveys information about categoryidentity. In addition, I propose that a speaker’s confidence in a classification can convey information about categorystructure. Because atypical instances of a category are more difficult to classify than typical instances, when speakers refer to these instances their lack of confidence will manifest itself “paralinguistically”—that is, in the form of hesitations, filled pauses, or rising prosody. These features can help listeners learn by enabling them to differentiate good from bad examples of a category. So that this hypothesis could be evaluated, in a category learning experiment participants learned a set of novel colors from a speaker. When the speaker’s paralinguistically expressed confidence was consistent with the underlying category structure, learners acquired the categories more rapidly and showed better category differentiation from the earliest moments of learning. These findings have important implications for theories of conversational coordination and language learning.