Effects of Gestures on Older Adults' Learning from Video‐based Models

This study investigated whether the positive effects of gestures on learning by decreasing working memory load, found in children and young adults, also apply to older adults, who might especially benefit from gestures given memory deficits associated with aging. Participants learned a problem‐solving skill by observing a video‐based modeling example, with the human model using gesture cues, with a symbolic cue, or without cues. It was expected that gesture compared with symbolic or no cues (i) improves learning and transfer performance, (ii) more in complex than simple problems, and (iii) especially in older adults. Although older adults' learning outcomes were lower overall than that of children and young adults, the results only revealed a time‐on‐task advantage of gesture over no cues in the learning phase for the older adults. In conclusion, the present study did not provide strong support for the effectiveness of gestures on learning from video‐based modeling example. Copyright © 2014 John Wiley & Sons, Ltd.     

From implicit skills to explicit knowledge: a bottom-up model of skill learning

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.     

A process model for water jug problems

We develop and evaluate a model for the water jug task in which a subject is required to find a sequence of moves (pouring operations) which produce a specified amount of water in each jug. Experiment 1 was designed to evaluate the meansends, move selection heuristics that are assumed by the model. Experiment 2 tested the model's predictions concerning those aspects of the water jug task that determine problem difficulty.A three stage process model incorporating GPS-like, means-ends heuristics and assumptions concerning the utilization of short- and long-term memory was able to account for differences across problems as well as details of the performance of subjects solving a given problem. We conclude that a GPS-like model that only selects one move at a time (no forward planning of move sequences or setting up of subgoals) can provide a good account of solution behavior in the water jug task.     

Assessing Mathematics Misunderstandings via Bayesian Inverse Planning

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step-by-step solutions, we propose a Bayesian inverse planning model for equation solving that computes an assessment of a learner's skills based on her pattern of errors in individual steps and her choices about what sequence of problem-solving steps to take. Bayesian inverse planning builds on existing machine learning tools to create a generative model relating (mis)-understandings to equation solving choices. Two behavioral experiments demonstrate that the model can interpret people's equation solving and that its assessments are consistent with those of experienced teachers. A third experiment uses this model to tailor guidance for learners based on individual differences in misunderstandings, closing the loop between assessing understanding, and using that assessment within an educational technology. Finally, because the bottleneck in applying inverse planning to a new domain is in creating the model of possible student misunderstandings, we show how to combine inverse planning with an existing production rule model to make inferences about student misunderstandings of fraction arithmetic.     

Improving examples to improve transfer to novel problems

People often memorize a set of steps for solving problems when they study worked-out examples in domains such as math and physics without learning what domain-relevant subgoals or subtasks these steps achieve. As a result, they have trouble solving novel problems that contain the same structural elements but require different, lower-level steps. In three experiments, subjects who studied example solutions that emphasized a needed subgoal were more likely to solve novel problems that required a new approach for achieving this subgoal than were subjects who did not learn this subgoal. This result suggests that research aimed at determining the factors that influence subgoal learning may be valuable in improving transfer from examples to novel problems.     

Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry

We present a new model of skilled performance in geometry proof problem solving called the Diagram Configuration model (DC). While previous models plan proofs in a step-by-step fashion, we observed that experts plan at a more abstract level: They focus on the key steps and skip the less important ones. DC models this abstract planning behavior by parsing geometry problem diagrams into perceptual chunks, called diagram configurations, which cue relevant schematic knowledge. We provide verbal protocol evidence that DC's schemas correspond with the step-skipping inferences experts make in their initial planning. We compare DC with other models of geometry expertise and then, in the final section, we discuss more general implications of our research. DC's reasoning has important similarities with Larkin's (1988) display-based reasoning approach and Johnson-Laird's (1983) mental model approach. DC's perceptually based schemas are a step towards a unified explanation of (1) experts' superior problem-solving effectiveness, (2) experts' superior problem-state memory, and (3) experts' ability, in certain domains, to solve relatively simple problems by pure forward inferencing. We also argue that the particular and efficient knowledge organization of DC challenges current theories of skill acquisition as it presents an end-state of learning that is difficult to explain within such theories. Finally, we discuss the implications of DC for geometry instruction.     

Order of Presentation Effects in Learning Color Categories

Two studies, an experimental category learning task and a computational simulation, examined how sequencing training instances to maximize comparison and memory affects category learning. In Study 1, 2-year-old children learned color categories with three training conditions that varied in how categories were distributed throughout training and how similarity between exemplars progressed across instances. The results indicate that beginning learning by interacting with a limited set of highly similar exemplars leads to more learning than when the instances are distributed and dissimilar. In Study 2, order effects were examined with a symbolic connectionist model of general learning and representation discovery (DORA). The results of the studies suggest that when the presentation of instances is ordered in such a way that discrete instances of a category can be more readily connected in memory, category learning and discovery are more likely to occur.     

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network’s critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.     

Age differences in associative and strategic processes in human conditional learning

Research indicates associative and strategic deficits mediate age related deficits in memory, whereas simple associative processes are independent of strategic processing and strategic processes mediate resistance to interference. The present study showed age-related deficits in a contingency learning task, although older participants' resistance to interference was not disproportionately affected. Recognition memory predicted discrimination, whereas general cognitive ability predicted resistance to interference, suggesting differentiation between associative and strategic processes in learning and memory, and age declines in associative processes. Older participants' generalisation of associative strength from existing to novel stimulus–response associations was consistent with elemental learning theories, whereas configural models predicted younger participants' responses. This is consistent with associative deficits and reliance on item-level representations in memory during later life.     

The Family Peace Project

Summary

Intimate partner violence-assault by a spouse, ex-spouse, intimate friend or ex-friend-is the most common cause of injury for women and a serious public health problem. As a result, the American Medical Association has recognized physicians' ethical obligation to diagnose and treat partner violence, and called for new models of training. In this article, we review literature on the health care system's response to domestic violence. We then describe the Family Peace Project, a community oriented model for training health care professionals to identify, treat and prevent partner violence in primary health care settings. This training program provides knowledge, clinical skills and a sense of professional empowerment. Unique features of this program include: involvement of community mentors (survivors of family violence) as program faculty; training in specific clinical skills and protocols for screening, assessment and intervention; a hands-on assessment of community resources; coinmunity service; and, creation of an award winning web site to promote dissemination of the training program.     

Symbolic versus associative learning

Ramscar and colleagues (2010, this volume) describe the "feature-label-order" (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of "learning elements of a symbol system" (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of "solving the symbol grounding problem."     

The social foundation of executive function

In this study, we propose that infant social cognition may ‘bootstrap' the successive development of domain‐general cognition in line with the cultural intelligence hypothesis. Using a longitudinal design, 6‐month‐old infants (N = 118) were assessed on two basic social cognitive tasks targeting the abilities to share attention with others and understanding other peoples' actions. At 10 months, we measured the quality of the child's social learning environment, indexed by parent's abilities to provide scaffolding behaviors during a problem‐solving task. Eight months later, the children were followed up with a cognitive test‐battery, including tasks of inhibitory control and working memory. Our results showed that better infant social action understanding interacted with better parental scaffolding skills in predicting simple inhibitory control in toddlerhood. This suggests that infants' who are better at understanding other's actions are also better equipped to make the most of existing social learning opportunities, which in turn may benefit future non‐social cognitive outcomes.     

Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.     

Theory learning as stochastic search in the language of thought

We present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While their subsymbolic representations provide a smooth error surface that supports efficient gradient-based learning, our symbolic representations are better suited to capturing children's intuitive theories but give rise to a harder learning problem, which can only be solved by exploratory search. Our algorithm attempts to discover the theory that best explains a set of observed data by performing stochastic search at two levels of abstraction: an outer loop in the space of theories and an inner loop in the space of explanations or models generated by each theory given a particular dataset. We show that this stochastic search is capable of learning appropriate theories in several everyday domains and discuss its dynamics in the context of empirical studies of children's learning.     

A computational model of frontal lobe dysfunction: working memory and the Tower of Hanoi task

A symbolic computer model, employing the perceptual strategy, is presented for solving Tower of Hanoi problems. The model is calibrated—in terms of the number of problems solved, time taken, and number of moves made—to the performance of 20 normal subjects. It is then "lesioned" by increasing the decay rate of elements in working memory to model the performance of 20 patients with lesions to the prefrontal cortex. The model captures both the main effects of subject groups (patients and normal controls) performance, and the subject groups (patients and normal controls) by problem difficulty interactions. This leads us to support the working memory hypothesis of frontal lobe functions, but for a narrow range of problems.     

Control mechanisms in problem solving

Hypothesis-testing theories and information processing theories have both been used to explain the results of problem solving experiments. The theories have, in general, been applied to different classes of problems, resulting in little overlap or interaction between them. Information processing theories have tended to emphasize problems in which the problem solver’s primary source of information concerning the correctness of his moves is the problem goal and, as a result, the goal plays a major role in the control mechanisms determining moves. Hypothesis theories have emphasized problems in which direct feedback is the primary control mechanism available. It is suggested that altering a problem solver’s major source of information from a goal to feedback or vice versa will have important implications for the course of problem solving and what is learned during the solution process. Specifically, changes in the information feedback and/or goal-specificity characteristics of a problem should, in a predictable fashion, alter problem solving strategies, which should, in tum, through the presence or absence of information compression devices such as rule induction, strongly affect transfer. Experiments using the Tower of Hanoi and visual maze-tracing problems indicated that altering the density of subgoals resulted in alterations in subsequent transfer performance.     

Self-Regulated Learning in Younger and Older Adults: Does Aging Affect Metacognitive Control?

ABSTRACT

Two experiments examined whether younger and older adults' self-regulated study (item selection and study time) conformed to the region of proximal learning (RPL) model when studying normatively easy, medium, and difficult vocabulary pairs. Experiment 2 manipulated the value of recalling different pairs and provided learning goals for words recalled and points earned. Younger and older adults in both experiments selected items for study in an easy-to-difficult order, indicating the RPL model applies to older adults' self-regulated study. Individuals allocated more time to difficult items, but prioritized easier items when given less time or point values favoring difficult items. Older adults studied more items for longer but realized lower recall than did younger adults. Older adults' lower memory self-efficacy and perceived control correlated with their greater item restudy and avoidance of difficult items with high point values. Results are discussed in terms of RPL and agenda-based regulation models.     

小学儿童执行功能与问题解决能力的关系

本研究选取小学二、四、六年级儿童为被试,采用计算机操作的方式,考察小学生执行功能中抑制、工作记忆和认知灵活性与4类伦敦塔问题解决表现之间的关系。结果表明在问题解决的计划方面,抑制能力较高的被试体现出良好的适应性解决能力,而工作记忆高的被试更能有效的利用资源;根据问题解决路径的不同划分了三大伦敦塔问题解决表现类型,工作记忆和抑制在问题解决中均起到重要作用。     

Working Memory Capacity and Self‐Explanation Strategy Use Provide Additive Problem‐Solving Benefits

The present study examined the impact of working memory capacity (WMC) on college students' ability to solve probability problems while using a self‐explanation strategy. Participants learned to solve probability problems in one of three conditions: a backward‐faded self‐explanation condition, an example problem pairs self‐explanation condition, or a control (no self‐explanation) condition. Even when accounting for the impact of WMC, learning to problem‐solve using self‐explanation led to superior problem‐solving performance. Conditions that prompted self‐explanation during problem‐solving resulted in significantly better problem‐solving performance than the control condition. These findings provide insight into the influence of individual differences on problem‐solving when strategies are provided, as well as information about the effectiveness of the self‐explanation strategy during mathematical problem‐solving. Copyright © 2016 John Wiley & Sons, Ltd.