Social cues modulate the representations underlying cross-situational learning

Because children hear language in environments that contain many things to talk about, learning the meaning of even the simplest word requires making inferences under uncertainty. A cross-situational statistical learner can aggregate across naming events to form stable word-referent mappings, but this approach neglects an important source of information that can reduce referential uncertainty: social cues from speakers (e.g., eye gaze). In four large-scale experiments with adults, we tested the effects of varying referential uncertainty in cross-situational word learning using social cues. Social cues shifted learners away from tracking multiple hypotheses and towards storing only a single hypothesis (Experiments 1 and 2). In addition, learners were sensitive to graded changes in the strength of a social cue, and when it became less reliable, they were more likely to store multiple hypotheses (Experiment 3). Finally, learners stored fewer word-referent mappings in the presence of a social cue even when given the opportunity to visually inspect the objects for the same amount of time (Experiment 4). Taken together, our data suggest that the representations underlying cross-situational word learning of concrete object labels are quite flexible: In conditions of greater uncertainty, learners store a broader range of information.     

What you learn is what you see: using eye movements to study infant cross-situational word learning

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps in using eye movement data to infer underlying real-time processes in statistical word learning.     

Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.     

Cross-situational Learning From Ambiguous Egocentric Input Is a Continuous Process: Evidence Using the Human Simulation Paradigm

Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real-world contexts in which learning occurs. Thus, the feasibility of generalizing cross-situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross-situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross-situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word-object mappings. These results suggest that word learning is a continuous process by means of real-time information integration.     

Changing Structures in Midstream: Learning Along the Statistical Garden Path

Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low-level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue to the midstream change or when exposure to the second structure was tripled in duration. These results demonstrate that successful extraction of the structure in an auditory statistical learning task reduces the ability to learn subsequent structures, unless the presence of two structures is marked explicitly or the exposure to the second is quite lengthy. The mechanisms by which learners detect and use changes in distributional information to maintain sensitivity to multiple structures are discussed from both behavioral and computational perspectives.     

Detailed behavioral analysis as a window into cross-situational word learning

Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning across observations. Learners also adopted an exclusion strategy, which contributed conjointly with statistical tracking to performance. Implications for our understanding of the processes underlying cross-situational word learning are discussed.     

Competitive Processes in Cross‐Situational Word Learning

Cross‐situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross‐situational word learning provides information at multiple scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross‐situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process‐level understanding of cross‐situational learning.     

Learning harmony: the role of serial statistics

How do listeners learn about the statistical regularities underlying musical harmony? In traditional Western music, certain chords predict the occurrence of other chords: Given a particular chord, not all chords are equally likely to follow. In Experiments 1 and 2, we investigated whether adults make use of statistical information when learning new musical structures. Listeners were exposed to a novel musical system containing phrases generated using an artificial grammar. This new system contained statistical structure quite different from Western tonal music. Our results suggest that learners take advantage of the statistical patterning of chords to acquire new musical structures, similar to learning processes previously observed for language learning.     

Effects of visual information on adults' and infants' auditory statistical learning

Infant and adult learners are able to identify word boundaries in fluent speech using statistical information. Similarly, learners are able to use statistical information to identify word-object associations. Successful language learning requires both feats. In this series of experiments, we presented adults and infants with audio-visual input from which it was possible to identify both word boundaries and word-object relations. Adult learners were able to identify both kinds of statistical relations from the same input. Moreover, their learning was actually facilitated by the presence of two simultaneously present relations. Eight-month-old infants, however, do not appear to benefit from the presence of regular relations between words and objects. Adults, like 8-month-olds, did not benefit from regular audio-visual correspondences when they were tested with tones, rather than linguistic input. These differences in learning outcomes across age and input suggest that both developmental and stimulus-based constraints affect statistical learning.     

Language evolution by iterated learning with bayesian agents

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.     

Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning

Previous research on cross‐situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co‐occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated with a learning condition that engendered retrieval dynamics that initially challenged the learner but eventually led to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross‐situational word learning and is a powerful example of how learning dynamics affect long‐term learning outcomes.     

Variability and detection of invariant structure

Two experiments investigated learning of nonadjacent dependencies by adults and 18–month–olds. Each learner was exposed to three–element strings (e.g., pel–kicey–jic) produced by one of two artificial languages. Both languages contained the same adjacent dependencies, so learners could distinguish the languages only by acquiring dependencies between the first and third elements (the nonadjacent dependencies). The size of the pool from which the middle elements were drawn was systematically varied to investigate whether increasing variability (in the form of decreasing predictability between adjacent elements) would lead to better detection of nonadjacent dependencies. Infants and adults acquired nonadjacent dependencies only when adjacent dependencies were least predictable. The results point to conditions that might lead learners to focus on nonadjacent versus adjacent dependencies and are important for suggesting how learning might be dynamically guided by statistical structure.     

The learning ability paradox in adult metamemory research: Where are the metamemory differences between good and poor learners?

College students’ ability to judge whether a studied item had been learned well enough to be recalled on a later test was examined in three experiments with self-paced learning procedures. Generally, these learners compensated for item difficulty when allocating study time, studying hard items longer than easy items, but they still recalled more easy items than hard items and tended to drop items out too soon. When provided with test opportunities during study or a delay between study and judgment, learners compensated significantly more for item difficulty and recalled substantially more. Paradoxically, good and poor learners compensated similarly for item difficulty and benefited similarly from testing during study and from delayed decision making. Thus, although the ability to make metamemory decisions was shown to be important for effective learning, these decisions were made equally well by good and poor associative learners. An analysis of tasks used to investigate metamemory-memory relationships in adult learning may provide an account for this apparent learning ability paradox.     

Modeling cross-situational word-referent learning: prior questions

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.     

线索在多媒体学习中的作用

线索在多媒体学习中作为一种重要的教学设计形式,具有引导注意、组织和整合信息的功能。综述已有研究发现,线索具有引导学习者加工特定位置信息、忽视无关信息干扰的功能;在特定条件下帮助学习者整合图-文信息,并且提高学习者的学习效果;但对于线索信息组织功能尚缺少充分而有力的证据。关于线索对学习效果的影响,大部分研究证实线索能够促进学习。但是由于线索的多样性以及学习材料的复杂性,线索能否有效地提高学习效果仍然存在争议。从认知负荷理论视角综述发现,线索加入并没有明显降低学习者感知到的主观认知负荷;从知觉加工视角而言,线索作为突显的刺激会吸引学习者的注意,从而简化学习者在知觉加工阶段的视觉搜索。未来研究仍然需要关注以下问题:(1)线索是否真的被学习者所注意和加工?(2)重视学习材料多样性比较和不同线索类型差异探讨,(3)关注学习者经验影响,(4)如何在教学设计上保证线索的有效性。     

Acquiring and processing verb argument structure: distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.     

Perceptual selectivity and information processing by fast and slow learners in paired-associate learning

Utilizing a design requiring perceptual rather than verbal discrimination in a compound-stimulus, P-A learning task, two hypotheses related to individual differences in perceptual selection of a functional stimulus cue and the processing of information were explored. The hypothesis that fast learners (determined by pretest) would “process” more information than slow learners in P-A learning was supported. Slow learners consistently ignored the random shape to which instructions specifically directed them and selected instead the context color as the functional cue. A second hypothesis that negative transfer due to proactive inhibition would be greater for slow learners was also supported.     

预期性、词频和笔画数对中文词汇识别影响的眼动研究

预期性是阅读研究领域中的一个热点问题,但关于预期性发生作用的加工阶段问题缺少实证考察和解释(Huettig, 2015)。本研究采用眼动追踪技术,通过对预期性和词频、预期性和笔画数在词汇识别过程中是否存在交互作用进行考察,以探讨预期性效应对词汇识别产生影响的加工阶段。结果显示,预期性和词频、预期性和笔画数之间均不存在交互作用,由此表明,预期性、词频和笔画数均独立地对中文文本阅读中的词汇识别产生显著的影响,该结果符合E-Z读者模型的研究假设。     

知觉模糊度影响学习的延迟效应

基于迁移适当加工理论,采用连续识别任务范式,通过操纵字词和图片的模糊度来考察知觉模糊非流畅的延迟效应。结果发现：（1）模糊不流畅的材料学习判断值更低;（2） 知觉模糊程度对即时的再认成绩没有影响,但在延迟再认测试中流畅性较低的模糊材料成绩更好。该结果表明实验操纵方式所引发的编码过程和记忆测试所需的检索过程性质上相匹配,而且延迟测试时,知觉非流畅可以促进学习,说明该效应存在边界。     

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.