Effects of Variation and Prior Knowledge on Abstract Concept Learning

Learning abstract concepts through concrete examples may promote learning at the cost of inhibiting transfer. The present study investigated one approach to solving this problem: systematically varying superficial features of the examples. Participants learned to solve problems involving a mathematical concept by studying either superficially similar or varied examples. In Experiment 1, less knowledgeable participants learned better from similar examples, while more knowledgeable participants learned better from varied examples. In Experiment 2, prior to learning how to solve the problems, some participants received a pretraining aimed at increasing attention to the structural relations underlying the target concept. These participants, like the more knowledgeable participants in Experiment 1, learned better from varied examples. Thus, the utility of varied examples depends on prior knowledge and, in particular, ability to attend to relevant structure. Increasing this ability can prepare learners to learn more effectively from varied examples.     

Response times seen as decompression times in Boolean concept use

This paper reports a study of a multi-agent model of working memory (WM) in the context of Boolean concept learning. The model aims to assess the compressibility of information processed in WM. Concept complexity is described as a function of communication resources (i.e., the number of agents and the structure of communication between agents) required in WM to learn a target concept. This model has been successfully applied in measuring learning times for three-dimensional (3D) concepts (Mathy and Bradmetz in Curr Psychol Cognit 22(1):41-82, 2004). In this previous study, learning time was found to be a function of compression time. To assess the effect of decompression time, this paper presents an extended intra-conceptual study of response times for two- and 3D concepts. Response times are measured in recognition phases. The model explains why the time required to compress a sample of examples into a rule is directly linked to the time to decompress this rule when categorizing examples. Three experiments were conducted with 65, 49, and 84 undergraduate students who were given Boolean concept learning tasks in two and three dimensions (also called rule-based classification tasks). The results corroborate the metric of decompression given by the multi-agent model, especially when the model is parameterized following static serial processing of information. Also, this static serial model better fits the patterns of response times than an exemplar-based model.     

A theory-based approach to teaching young children about health: A recipe for understanding

The theory-theory account of conceptual development posits that children's concepts are integrated into theories. Concept learning studies have documented the central role that theories play in children's learning of experimenter-defined categories, but have yet to extensively examine complex, real-world concepts such as health. The present study examined whether providing young children with coherent and causally-related information in a theory-based lesson would facilitate their learning about the concept of health. This study used a pre-test/lesson/post-test design, plus a five month follow-up. Children were randomly assigned to one of three conditions: theory (i.e., 20 children received a theory-based lesson); nontheory (i.e., 20 children received a nontheory-based lesson); and control (i.e., 20 children received no lesson). Overall, the results showed that children in the theory condition had a more accurate conception of health than children in the nontheory and control conditions, suggesting the importance of theories in children's learning of complex, real-world concepts.     

The Development of Infants' Spatial Categories

ABSTRACT— Early theories of how infants develop spatial concepts focused on the perceptual and cognitive abilities that contribute to this ability. More recent research, however, has centered on whether experience with spatial language might also play a role. The present article reviews how infants learn to form spatial categories, outlining the perceptual and cognitive abilities that drive this learning, and examines the role played by spatial language. I argue that infants' spatial concepts initially are the result of nonlinguistic perceptual and cognitive abilities, but that, as infants build a spatial lexicon, spatial language becomes an important tool in the spatial categories infants learn to form.     

Interpretation of Scientific or Mathematical Concepts: Cognitive Issues and Instructional Implications

Scientific and mathematical concepts are significantly different from everyday concepts and are notoriously difficult to learn. It is shown that particular instances of such concepts can be identified or generated by different possible modes of concept interpretation. Some of these modes use formally explicit knowledge and thought processes; others rely on less formal case-based knowledge and more automatic recognition processes. The various modes differ in attainable precision, likely errors, and ease of use. A combination of such modes can be used to formulate an “ideal” model for interpreting scientific concepts both reliably and efficiently. Comparisons are made with the actual concept interpretations of expert scientists and novice students. The discussion elucidates some cognitive and metacognitive reasons why the learning of scientific or mathematical concepts is difficult. It also suggests instructional guidelines for teaching such concepts more effectively.     

Learning time-varying categories

Many kinds of objects and events in our world have a strongly time-dependent quality. However, most theories about concepts and categories either are insensitive to variation over time or treat it as a nuisance factor that produces irrational order effects during learning. In this article, we present two category learning experiments in which we explored peoples’ ability to learn categories whose structure is strongly time-dependent. We suggest that order effects in categorization may in part reflect a sensitivity to changing environments, and that understanding dynamically changing concepts is an important part of developing a full account of human categorization.     

The most efficient sequence of study depends on the type of test

Across three experiments featuring naturalistic concepts (psychology concepts) and naïve learners, we extend previous research showing an effect of the sequence of study on learning outcomes, by demonstrating that the sequence of examples during study changes the representation the learner creates of the study materials. We compared participants' performance in test tasks requiring different representations and evaluated which sequence yields better learning in which type of tests. We found that interleaved study, in which examples from different concepts are mixed, leads to the creation of relatively interrelated concepts that are represented by contrast to each other and based on discriminating properties. Conversely, blocked study, in which several examples of the same concept are presented together, leads to the creation of relatively isolated concepts that are represented in terms of their central and characteristic properties. These results argue for the integrated investigation of the benefits of different sequences of study as depending on the characteristics of the study and testing situation.     

LOGIQUE ET PSYCHOLOGIE DE L'APPRENTISSAGE DES CONCEPTS

In the field of concept learning, there are very few studies in which logícal conditions are used as independent variables. A logical analysis of concept learning phenomena is presented: it is an application of some elementary theoremas of logics and set theory. To learn a concept means to identify relevant attributes of the phenomena observed; the theoretical analysis of the situation shows that concept learning can be seen as a problem, the solution of which depends on a precise algorithm. Numerous experiments have been carried out on the basis of such an analysis, some of which are described here. Results provide evidence that time and quality of performance depend on the length and logical structure of the set of successive stimuli presented to subjccts. So logical features of the situation influence concept learning; but, there are various other factors which control subject's performance : for instance, number of redundant examples, and subject's memory and perceptual-motor activity. Current interpretations of concept learning, i.e., stochastic models of learning and logical and mathematical interpretation are discussed and the need of an approach which would combine both to fit the complexity of concept learning phenomena is emphasized.     

概念组合的理论模型

概念组合是将两个或多个概念组合成一个新概念的过程,生成的新概念被称为组合概念。组合概念的解释策略及影响因素已成为概念研究中的热点问题。研究者相继提出了关系竞争理论、双重加工理论和约束理论等模型。但是,这些理论模型只能解释特定的实验现象,对某些稳定的实验现象（如涌现特征、范畴效应等）却缺乏解释力度。文章提出,未来的研究应该整合已有的实验现象和理论模型,关注概念组合的时间进程,深入考察语境的作用,并系统地研究组合概念的涌现特征和范畴效应。     

A probabilistic model of theory formation

Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a domain, and the laws or principles that relate these concepts. We apply our model to several real-world problems, including learning the structure of kinship systems and learning ontologies. We also compare its predictions to data collected in two behavioral experiments. Experiment 1 shows that our model helps to explain how simple theories are acquired and used for inductive inference. Experiment 2 suggests that our model provides a better account of theory discovery than a more traditional alternative that focuses on features rather than relations.     

The long-term effect of relational information in classification learning

This study examines the long-term effect of mutual information in the learning of Shepardian classifications. Mutual information is a measure of the complexity of the relationship between features because it quantifies how the features relate to each other. For instance, in various categorisation models, Type VI concepts—originally studied by Shepard, Hovland, and Jenkins (1961)—are unanimously judged to be the most complex kind of 3-D Boolean concepts. This has been largely confirmed by empirical data. Yet, it is apparently inconsistent with the fact that this concept entails the greatest amount of mutual information of all the 3-D Boolean concepts. The present study was aimed at verifying whether individuals can use relational information, in the long run, to devise easier strategies for category learning. Subject performance was measured repeatedly for 1 hour on either successive Type VI concepts (using different features between problems) or successive Type IV concepts. The results showed that shortly after the second problem, Type VI concepts became easier to learn than Type IV ones. The gap between the mean per-problem error rates of the two concepts continued to increase as the number of problems increased. Two other experiments tended to confirm this trend. The discussion brings up the idea of combining different metrics in categorisation models in order to include every possible way for subjects to simplify the categorisation process.     

From Perceptual Categories to Concepts: What Develops?

People are remarkably smart: they use language, possess complex motor skills, make non-trivial inferences, develop and use scientific theories, make laws, and adapt to complex dynamic environments. Much of this knowledge requires concepts and this paper focuses on how people acquire concepts. It is argued that conceptual development progresses from simple perceptual grouping to highly abstract scientific concepts. This proposal of conceptual development has four parts. First, it is argued that categories in the world have different structure. Second, there might be different learning systems (sub-served by different brain mechanisms) that evolved to learn categories of differing structures. Third, these systems exhibit differential maturational course, which affects how categories of different structures are learned in the course of development. And finally, an interaction of these components may result in the developmental transition from perceptual groupings to more abstract concepts. This paper reviews a large body of empirical evidence supporting this proposal.     

Making Probabilistic Relational Categories Learnable

Theories of relational concept acquisition (e.g., schema induction) based on structured intersection discovery predict that relational concepts with a probabilistic (i.e., family resemblance) structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category‐learning task to choosing the “winning” object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this “who's winning” effect. Experiment 4 replicated and generalized the “who's winning” effect with more natural stimuli. Together, our findings suggest that people learn relational concepts by a process of intersection discovery akin to schema induction, and that any task that encourages people to discover a higher order relation that remains invariant over members of a category will facilitate the learning of putatively probabilistic relational concepts.     

The effects of relational structure on analogical learning

Relational structure is important for various cognitive tasks, such as analogical transfer, but its role in learning of new relational concepts is poorly understood. This article reports two experiments testing people’s ability to learn new relational categories as a function of their relational structure. In Experiment 1, each stimulus consisted of 4 objects varying on 2 dimensions. Each category was defined by two binary relations between pairs of objects. The manner in which the relations were linked (i.e., by operating on shared objects) varied between subjects, producing 3 logically different conditions. In Experiment 2, each stimulus consisted of 4 objects varying on 3 dimensions. Categories were defined by three binary relations, leading to six logically different conditions. Various learning models were compared to the behavioral data, based on the theory of schema refinement. The results highlight several shortcomings of schema refinement as a model of relational learning: (1) it can make unreasonable demands on working memory, (2) it does not allow schemas to grow in complexity, and (3) it incorrectly predicts learning is insensitive to relational structure. We propose schema elaboration as an additional mechanism that provides a more complete account, and we relate this mechanism to previous proposals regarding interactions between analogy and representation construction. The current findings may advance understanding of the cognitive mechanisms involved in learning and representing relational concepts.     

Analytic and holistic modes of learning family-resemblance concepts

Three studies examined how individuals learn concepts structured according to family-resemblance principles. The materials were cartoon faces varying in the attributes of hair, mustache, ears, and nose. In contrast to previous studies purporting to show holistic modes of learning family-resemblance concepts, the present studies indicate that many individuals learn such concepts by an analytic, attribute-plus-exception rule. The attribute-plus-exception rule characterized the learning shown by adults under both intentional (Studies 1 and 2) and incidental (Study 3) learning conditions and by children under intentional learning conditions (Study 2). There was no evidence to indicate that a holistic mode is a more primitive one, since it did not occur more frequently among children or adults under incidental learning conditions. It is suggested that the extent to which holistic or analytic modes of learning are observed will be found to depend on an interaction among stimulus, task, and observer factors.     

知识点认知规律的实验研究

知识点是人的认知单位,人学习知识,必须以知识点为单位、逐个知识点地学习。本实验以概念学习为例,研究了人一次学习一个知识点与同时学习两个知识点时的成绩差异。研究结果表明,1. 在概念中的信息组块数不超过短时记忆容量的前提下,一次学习一个概念,构成概念的信息组块数对概念掌握影响不显著。2.在同样条件下,一次学习一个知识点的成绩好于同时学习两个知识点的成绩,从而验证了知识点认知规律。     

A rational account of pedagogical reasoning: Teaching by,and learning from,examples

Much of learning and reasoning occurs in pedagogical situations—situations in which a person who knows a concept chooses examples for the purpose of helping a learner acquire the concept. We introduce a model of teaching and learning in pedagogical settings that predicts which examples teachers should choose and what learners should infer given a teacher’s examples. We present three experiments testing the model predictions for rule-based, prototype, and causally structured concepts. The model shows good quantitative and qualitative fits to the data across all three experiments, predicting novel qualitative phenomena in each case. We conclude by discussing implications for understanding concept learning and implications for theoretical claims about the role of pedagogy in human learning.     

Neural plasticity and human development: the role of early experience in sculpting memory systems

The concept of sensitive or critical periods in the context of memory development is examined in this paper. I begin by providing examples of the role of experience in influencing sensory, linguistic and emotional functioning. This is followed by a discussion of the role of experience in influencing cognitive functioning, particularly memory. Based on this discussion, speculation is offered that the infant’s proclivity for novelty, which makes its appearance shortly after birth, provides critical input into a nervous system that will eventually be set up to learn and remember for the entire lifespan. Because learning and memory are fundamental to the survival of our species, those aspects of the nervous system that permit the encoding and retention of new information are remarkably malleable from the outset, even in the face of some types of neural trauma. This flexibility is retained for many years so long as the learning and memory ‘system’ is challenged. The implications of this model are discussed in the context of those life events that might undermine the longevity of memory systems.