A Probabilistic Constraints Approach to Language Acquisition and Processing

This article provides an overview of a probabilistic constraints framework for thinking about language acquisition and processing. The generative approach attempts to characterize knowledge of language (i.e., competence grammar) and then asks how this knowledge is acquired and used. Our approach is performance oriented: the goal is to explain how people comprehend and produce utterances and how children acquire this skill. Use of language involves exploiting multiple probabilistic constraints over various types of linguistic and nonlinguistic information. Acquisition is the process of accumulating this information, which begins in infancy. The constraint satisfaction processes that are central to language use are the same as the bootstrapping processes that provide entry to language for the child. Framing questions about acquisition in terms of models of adult performance unifies the two topics under a set of common principles and has important consequences for arguments concerning language learnability.     

The use of computer-controlled video disks in the study of spatial learning

The empirical study of spatial learning has been constrained by a range of methodological and technical limitations. Part of these are due to constraints in the stimulus delivery systems for pictorial displays that are available to the researcher. Surrogate travel systems—based on model photography, off-line computer graphics, and on-line computer graphics—provide alternative research settings. Yet none of the systems provides for both detailed displays and ease of subject interaction, both of which prove important in the investigation of spatial learning. Computer-controlled video-disk systems do allow both detail and interaction, therefore providing a research tool in which spatial learning phenomena can be observed carefully. A pilot study of spatial learning using a video-disk system is briefly described to demonstrate the sorts of phenomena observable with such a system.     

Constraints on children's color term acquisition

Kowalski and Zimiles (2006) and O'Hanlon and Roberson (2006) address an age-old question: Why do children find it difficult to learn color terms? Here these articles are reflected on, providing a focused examination of the issues central to this question. First, the criteria by which children are said to find color naming difficult are considered. Although the age of color term acquisition is decreasing, and color naming might not be more difficult than other abstract attributes, several stages of difficulty are identified. Second, it is argued that there are potentially multiple constraints (e.g., conceptual, attentional, and linguistic) for these multiple stages of difficulty with color term acquisition. Third, it is argued that the validity and reliability of techniques for identifying constraints need to be considered and that converging evidence for the constraints should be provided. Finally, a series of new questions that need to be asked to provide a well-rounded explanation of the difficulties children face when learning color terms is outlined.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Reflections on how color term acquisition is constrained

Compared with object word learning, young children typically find learning color terms to be a difficult linguistic task. In this reflections article, I consider two questions that are fundamental to investigations into the developmental acquisition of color terms. First, I consider what constrains color term acquisition and how stable these constraints are over culture and age. I review recent studies that have identified conceptual, attentional, and linguistic constraints that seemingly operate on the acquisition of color terms compared with object words and the differential acquisition of color terms within a given language. Second, I consider whether these constraints are specific to the acquisition of color terms or whether they reflect more general constraints that operate on other classes of lexical terms such as different abstract property terms.     

Maturational Constraints on Language Learning

This paper suggests that there are constraints on learning required to explain the acquisition of language, in particular, maturational constraints. First, empirical evidence for this claim is reviewed. The evidence from several studies of both first and second language acquisition suggests that normal language learning occurs only when exposure to the language begins early in life. With exposure beginning later in life, asymptotic performance in the language declines: the effects over age of first exposure are approximately linear through childhood, with a flattening of the function in adulthood. These outcomes argue that some type of constraints ensuring successful language learning exist early in life, and weaken with increasing maturation. Second, two hypotheses are considered as to the nature of these maturational changes. One hypothesis is that constraints on learning particular to language acquisition undergo maturational decay. A second hypothesis, which is considered in more detail, suggests that language learning abilities decline because of the expansion of nonlinguistic cognitive abilities.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Where do "constraints" come from? A comparison between inferences in biology and in word learning

In this paper, as a commentary on the papers in this special issue, the nature of constraints is discussed in terms of the comparison between inferences in biology and in word learning. Young children's inferences in biology could be constrained by three sources of information: factual knowledge, expectations based on a theory, and ontological knowledge. For example, young children's inference about the length of noses could be constrained by the facts the children know about the animals, the similarity between the animals and humans, and the ontological distinction between living things and nonliving things. In the same way, young children might figure out word meanings by linguistic and pragmatic knowledge, expectations of word meanings (e.g., the whole-object assumption), and ontological knowledge. Interactions among these sources of knowledge are documented by the papers in this special issue and related studies. It is argued that learning biases such as the whole-object assumption could not be induced only by linguistic and pragmatic cues in a social context, but should be regarded as a product of the interaction between a social context and children's theories.     

Probabilistic models of language processing and acquisition

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.     

IMPACT OF THE IMPLEMENTATION OF THE TEACHING/LEARNING CYCLE ON TEACHER DECISION-MAKING AND EMERGENT READERS

No topic is on the minds of the general public, federal and state legislatures, parents, and educators more than the issue of establishing and maintaining school programs that are consistent in their ability to produce competent readers. Teachers, administrators, and teacher preparation programs are admonished to change programs, resources, monitoring procedures - whatever it takes to bring about this reformation of student reading achievement. Topics under investigation include effective models, methods, and strategies of instruction; essential teacher knowledge; teacher decision-making; and professional development of in-service and pre-service teachers. From this body of research the terms "balanced literacy" and "engaged literacy" have emerged. These terms are defined as emphasizing instruction on identified student need and including skills development as well as experience in a variety of texts. The teaching/learning cycle, a model for evaluating student reading behaviors and planning focused instruction, providesa foundation for the development of essential teacher knowledge and skills. As teachers articulate their understanding of the reading process, the characteristics of the learner, and the supports and challenges offered in text and teaching approaches, their practice is changed to make it more congruent with these stated beliefs. When teacher instructional practice is aligned with clear understandings and beliefs, student achievement increases. This study explored how knowledge and use of the teaching/learning cycle in beginning reading instruction impacts teacher decision-making and student achievement. While investigating teachers' knowledge and use of the teaching/learning cycle, two research questions focused on how student learning was affected and the impact of the changes on student/teacher interactions.     

Shareability: The social psychology of epistemology

Psychologists and cognitive scientists interested in the nature of internal representations of human knowledge often use observable regularities or structures to infer what the innate constraints on those representations must be like. It is possible, however, that certain structures might come about only when a group of people share a knowledge domain. Furthermore, it is possible that there are analyzable constraints on knowledge structures that emerge when knowledge is being shared. Such constraints are referred to in this paper as “shareability” constraints. A number of examples of observable structures in human knowledge are discussed in terms of shareability constraints. An attempt is made to determine which sorts of structures are most shareable, and how those structures may differ from the sorts of structures that are easily represented by the individual mind but not easily shared between minds.     

多项式加工树模型次序约束的量化分析及其应用

多项式加工树（MPT）模型是一种认知测量模型,能够对潜在认知过程进行测量和检验。已有研究探讨了二链MPT模型次序约束的重新参数化问题,本研究探讨了MPT模型次序约束的量化分析方法并从二链推广到多链,同时归纳出MPT模型参数向量内和参数向量间两参数次序约束量化分析的结论。数据分析结果表明该方法不仅在MPT模型框架下验证了潜在参数次序关系,而且给出了约束的量化指标,为潜在认知测量提供更有意义的解释。     

Problem-Solving Restructuration: Elimination of Implicit Constraints

A general model of problem-solving processes based on misconception elimination is presented to simulate both impasses and solving processes. The model operates on goal-related rules and a set of constraint rules in the form of “if (state or goal), do not (Action)” for the explicit constraints in the instructions and the implicit constraints that come from misconceptions of legal moves. When impasses occur, a constraint elimination mechanism is applied. Because successive eliminations of implicit constraints enlarge the problem space and have an effect on planning, the model integrates “plan-based” and “constraint-based” approaches to problem-solving behavior. Simulating individual protocols of Tower of Hanoi situations shows that the model, which has a proper set of constraints, predicts a single move with no alternative on about 61% of the movements and that protocols are quite successfully simulated movement by movement. Finally, it is shown that many features of previous models are embedded in the constraint elimination model.     

Acquisition and extinction in autoshaping

C. R. Gallistel and J. Gibbon (2000) presented quantitative data on the speed with which animals acquire behavioral responses during autoshaping, together with a statistical model of learning intended to account for them. Although this model captures the form of the dependencies among critical variables, its detailed predictions are substantially at variance with the data. In the present article, further key data on the speed of acquisition are used to motivate an alternative model of learning, in which animals can be interpreted as paying different amounts of attention to stimuli according to estimates of their differential reliabilities as predictors.     

A computational model of early argument structure acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions —associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.     

Modeling the Development of Children's Use of Optional Infinitives in Dutch and English Using MOSAIC

In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterance-final bias in learning and the distributional characteristics of child-directed speech in the 2 languages. They also show how computational modeling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of universal grammar.     

Models of Machines and Models of Phenomena

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method—logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe.     

New nonlinear and dynamic avenues for the study of work and organizational psychology: an introduction to the special issue

ABSTRACT

Nonlinear and dynamic approaches are two main principles of complex systems. The work and organizational psychology field has recently applied these concepts to understand human behaviour at work. In this introductory paper, we describe how human behaviour at work can be understood from a nonlinear and dynamic perspective, developing and applying the advantages of this approach to continue moving beyond the frontiers of our knowledge. We focus on the theoretical, methodological, and epistemological implications of the nonlinear dynamic approach in the field. Then, we present the five papers included in this special issue. These papers have applied nonlinear and dynamic concepts to understand a wide range of phenomena: subordinates-supervisors relationships, team behaviour, deviant workplace behaviour, transformational leadership, and training transfer. Overall, these papers demonstrate the utility of nonlinear dynamics in our field and how we can create more time-sensitive and complex knowledge by paying special attention to when and how phenomena happen and how such phenomena interrelate over time.     

The Parallel Episodic Processing (PEP) model 2.0: A single computational model of stimulus-response binding,contingency learning,power curves,and mixing costs

The current paper presents an extension of the Parallel Episodic Processing model. The model is developed for simulating behaviour in performance (i.e., speeded response time) tasks and learns to anticipate both how and when to respond based on retrieval of memories of previous trials. With one fixed parameter set, the model is shown to successfully simulate a wide range of different findings. These include: practice curves in the Stroop paradigm, contingency learning effects, learning acquisition curves, stimulus-response binding effects, mixing costs, and various findings from the attentional control domain. The results demonstrate several important points. First, the same retrieval mechanism parsimoniously explains stimulus-response binding, contingency learning, and practice effects. Second, as performance improves with practice, any effects will shrink with it. Third, a model of simple learning processes is sufficient to explain phenomena that are typically (but perhaps incorrectly) interpreted in terms of higher-order control processes. More generally, we argue that computational models with a fixed parameter set and wider breadth should be preferred over those that are restricted to a narrow set of phenomena.     

Contemporary justice research: A new look at familiar questions

Research on organizational justice has flourished in the last 30 years. During that time, researchers have generally sought to answer three questions: (1) Why do people care about justice? (2) What affects justice judgments? and (3) What outcomes are associated with justice judgments? The papers in this special issue of Organizational Behavior and Human Decision Processes on organizational justice reflect how these three questions are explored in contemporary justice research. This introduction to the special issue considers how the papers represent trends and developments in current justice research. Several themes are identified: the role of justice in a broader model of group engagement, the empirical examination of justice as a moral virtue, the effect of social context on justice judgments, and the darker reactions to injustice. Thus, the special issue provides insight not only to familiar justice questions but also to the evolution of the field and its future direction.