Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.     

How unexpected observations lead to new beliefs: A Peircean pathway

People acquire new beliefs in various ways. One of the most important of these is that new beliefs are acquired as a response to experiencing events that one did not expect. This involves a form of inference distinct from both deductive and inductive inference: abductive inference. The concept of abduction is due to the American pragmatist philosopher C. S. Peirce. Davies and Coltheart (in press) elucidated what Peirce meant by abduction, and identified two problems in his otherwise promising account requiring solution if that account were to become fully workable. Here we propose solutions to these problems and offer an explicit cognitive model of how people derive new beliefs from observations of unexpected events, based on Peirce’s work and Sokolov’s ideas about prediction error triggering new beliefs. We consider that this model casts light not only upon normal processes of belief formation but also upon the formation of delusional beliefs.     

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.     

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age‐appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.     

Semantic similarity of labels and inductive generalization: Taking a second look

Prior research suggests that preschoolers can generalize object properties based on category information conveyed by semantically-similar labels. However, previous research did not control for co-occurrence probability of labels in natural speech. The current studies re-assessed children’s generalization with semantically-similar labels. Experiment 1 indicated that adults made category-based inferences regardless of co-occurrence probability; however, 4-year-olds generalized with semantically-similar labels that co-occurred in child-directed speech (e.g., bunny–rabbit) but not with non-co-occurring labels (e.g., crocodile–alligator). Experiment 2 indicated that generalization with semantically-similar labels increased gradually between 4- and 6-years of age. These results are discussed in relation to theories of early learning.     

Secrets and Disclosures: How Young Children Handle Secrets

The authors examined the influence of content and verbal cues on young children's understanding of secret information and of its disclosure. Participants were 209 5- and 6-year-old children in an experiment where a puppet, named Zinc, was the protagonist. Children were asked to whom Zinc would disclose a list of pieces of information, some of which are considered secrets. Children could choose between Zinc's friend, a nonfriend, both friend and nonfriend, or nobody. Participants were divided in 2 experiment groups: In the first group a prior verbal cue was given to the participants that some information might be secret, whereas in the second group no prior cue was presented. Results showed that young children, similar to older individuals, differentiated between secrets and nonsecrets partly on the basis of content, and chose to withhold secrets more than nonsecrets or to share secrets more with friends than with nonfriends, following the restrictive self-disclosure pattern. In contrast, young children did not withhold nonsecret information, but they shared it with friends and nonfriends. However, when given verbal cues regarding potential secrecy of the information, young children chose more restrictive disclosure patterns for all types of content.     

Representing,Running, and Revising Mental Models: A Computational Model

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel assembled coherence (AC) theory of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with Timber , a computational model of conceptual change that revises its beliefs and generates human‐like explanations in commonsense science. Timber represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. Timber then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low‐cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in Timber ; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use Timber to simulate the belief changes of students during clinical interviews about how the seasons change. We show that Timber produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory.     

Bayes and blickets: effects of knowledge on causal induction in children and adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.     

期望、体验和回忆: 当消费者不能从体验中学习

人们对消费体验的期望、回忆与真实体验时常发生分离, 而且人们很少从真实体验中得到学习。本研究认为原因之一是期望、体验和回忆三者基于不同的输入信息, 以及三者特殊的交互关系。本研究首先提出了一个关于消费者期望、体验和回忆的理论框架, 系统阐述了消费者何时不能从体验中学习以及如何能促进学习, 然后在一个消费情景中通过3×2混合实验设计考察了三者的关系。实验比较了在“预期(无体验)”、“体验后即时评价”和“体验后延迟评价”三种评价方式下被试对两种品牌火腿肠的口味评价和偏好选择。结果发现: 消费者对口味体验的期望和回忆相似, 但两者都和真实体验发生分离。最后, 本研究还讨论了这一理论框架在消费者行为中的应用、未来研究拓展等。     

Acquiring variation in an artificial language: Children and adults are sensitive to socially conditioned linguistic variation

Languages exhibit sociolinguistic variation, such that adult native speakers condition the usage of linguistic variants on social context, gender, and ethnicity, among other cues. While the existence of this kind of socially conditioned variation is well-established, less is known about how it is acquired. Studies of naturalistic language use by children provide various examples where children’s production of sociolinguistic variants appears to be conditioned on similar factors to adults’ production, but it is difficult to determine whether this reflects knowledge of sociolinguistic conditioning or systematic differences in the input to children from different social groups. Furthermore, artificial language learning experiments have shown that children have a tendency to eliminate variation, a process which could potentially work against their acquisition of sociolinguistic variation. The current study used a semi-artificial language learning paradigm to investigate learning of the sociolinguistic cue of speaker identity in 6-year-olds and adults. Participants were trained and tested on an artificial language where nouns were obligatorily followed by one of two meaningless particles and were produced by one of two speakers (one male, one female). Particle usage was conditioned deterministically on speaker identity (Experiment 1), probabilistically (Experiment 2), or not at all (Experiment 3). Participants were given tests of production and comprehension. In Experiments 1 and 2, both children and adults successfully acquired the speaker identity cue, although the effect was stronger for adults and in Experiment 1. In addition, in all three experiments, there was evidence of regularization in participants’ productions, although the type of regularization differed with age: children showed regularization by boosting the frequency of one particle at the expense of the other, while adults regularized by conditioning particle usage on lexical items. Overall, results demonstrate that children and adults are sensitive to speaker identity cues, an ability which is fundamental to tracking sociolinguistic variation, and that children’s well-established tendency to regularize does not prevent them from learning sociolinguistically conditioned variation.     

The stability and flexibility of spatial categories

Four experiments examined the flexibility and stability with which children and adults organize locations into categories based on their spatiotemporal experience with locations. Seven-, 9-, 11-year-olds, and adults learned the locations of 20 objects in an open, square box. During learning, participants experienced the locations in four spatiotemporally defined groups (i.e., four sets of nearby locations learned together in time). At test, participants attempted to place the objects in the correct locations without the aid of the dots marking the locations. Children and adults displaced the objects toward the corners of the box consistent with the organization they experienced during learning, suggesting that they used spatiotemporal experience to organize the locations into groups. Importantly, the pattern of organization remained the same following a long delay for all four age groups, demonstrating stability. For adults, this organization shifted after a new pattern of spatiotemporal experience was introduced, suggesting that adults' categories based on spatiotemporal experience are quite flexible. Children only exhibited flexibility when the new pattern of spatiotemporal organization was consistent with available perceptual cues, demonstrating that the flexibility with which children organize locations into categories is intimately tied to both remembered and perceptual sources of information.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Of matchers and maximizers: How competition shapes choice under risk and uncertainty

In a world of limited resources, scarcity and rivalry are central challenges for decision makers—animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually ‘irrational’ choice phenomenon can be socially adaptive under competition.     

“触电”时代的儿童:触屏媒体与幼儿发展

数字媒体的飞速发展使得越来越多的研究者开始关注触屏媒体对幼儿发展的影响。综述从触屏媒体的特征、触屏学习、幼儿发展、触屏媒体能否作为测量工具及其在家庭和学校中推广使用的有效性方面进行探讨。综述发现：触屏媒体具有交互性、即时反馈性、渐进性和便携性。41项涉及触屏的研究在STEM学习（d_{与基线比较}=1.06,d_{与其它方法比较}=0.30）、词汇学习（d_{与基线比较}=0.51,d_{与其它方法比较}=0.47）和学习迁移（d=0.78）上发现了触屏的促进作用。触屏媒体对幼儿执行功能与亲子关系发展有促进作用,但是对精细运动的影响尚不明确。触屏可以作为测量工具或干预方式用于其它领域,推广使用时存在挑战。未来研究应该继续关注触屏媒体特征对学习效果及不同年龄阶段儿童的影响,探讨真实触屏媒体环境中儿童的学习与发展。     

Priors in perception: Top-down modulation,Bayesian perceptual learning rate,and prediction error minimization

I discuss top-down modulation of perception in terms of a variable Bayesian learning rate, revealing a wide range of prior hierarchical expectations that can modulate perception. I then switch to the prediction error minimization framework and seek to conceive cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate. This approach retains cognitive penetration as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability. It prevents rampant, relativistic cognitive penetration of perception and yet is consistent with the continuity of cognition and perception.     

From a fixation on sports to an exploration of mechanism: The past,present, and future of hot hand research

We review the literature on the hot hand fallacy by highlighting the positive and negative aspects of hot hand research over the past 20 years, and suggesting new avenues of research. Many researchers have focused on criticising Gilovich et al.'s claim that the hot hand fallacy exists in basketball and other sports, instead of exploring the general implications of the hot hand fallacy for human cognition and probabilistic reasoning. Noting that researchers have shown that people perceive hot streaks in a gambling domain in which systematic streaks cannot possibly exist, we suggest that researchers have paid too much attention to investigating the independence of outcomes in various sporting domains. Instead, we advocate a domain-general mechanistic approach to understanding the hot hand fallacy, and conclude by suggesting approaches that might refocus the literature on the important general implications of the hot hand fallacy for human probabilistic reasoning.     

Hand Gesture and Mathematics Learning: Lessons From an Avatar

A beneficial effect of gesture on learning has been demonstrated in multiple domains, including mathematics, science, and foreign language vocabulary. However, because gesture is known to co‐vary with other non‐verbal behaviors, including eye gaze and prosody along with face, lip, and body movements, it is possible the beneficial effect of gesture is instead attributable to these other behaviors. We used a computer‐generated animated pedagogical agent to control both verbal and non‐verbal behavior. Children viewed lessons on mathematical equivalence in which an avatar either gestured or did not gesture, while eye gaze, head position, and lip movements remained identical across gesture conditions. Children who observed the gesturing avatar learned more, and they solved problems more quickly. Moreover, those children who learned were more likely to transfer and generalize their knowledge. These findings provide converging evidence that gesture facilitates math learning, and they reveal the potential for using technology to study non‐verbal behavior in controlled experiments.