Seeing it happen and knowing how it works: how children understand the relation between perceptual causality and underlying mechanism

Two experiments studied how 5- to 10-year-olds integrate perceptual causality with their knowledge of the underlying causal mechanism. Children learned about 2 devices by which a ball dropped into one end of a box made a bell ring at the other end, either immediately (contiguous mechanism) or after a delay (noncontiguous mechanism). When 1 ball was dropped first and a 2nd ball was dropped after a delay, and then the bell rang immediately, 5- and 7-year-olds chose the contiguous cause regardless of the mechanism inside. This was not due to lack of specific knowledge or problems with salient distractors. The results suggest a link between temporal contiguity and causality in children's understanding. Children also considered causal mechanism, in agreement with previous research, but they may not understand that mechanism is superordinate to perceptual cues for causality.     

A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.

How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena and problems are sketched. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Analogical and category-based inference: A theoretical integration with Bayesian causal models.

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Macrolevel and Microlevel Frameworks of Experiential Learning Theory in Construction Engineering Education

The experiential learning theory is one of the best-known learning theories in education and has been explored and developed for decades through various studies in higher education. This learning method is expected to provide a significantly better learning environment for engineering subjects, such as construction techniques and design methods and alternatives, which may occur in most of the construction engineering disciplines. Nevertheless, the theory has hardly been used in construction engineering courses. In this study, the authors perform an in-depth review of the learning theory and present a case study, formwork design example, wherein the theory can be explicitly applied in construction engineering education. Research findings reveal students’ responses to the learning theory and lessons learned. Correlation analysis is conducted to explore how students’ performance in learning can be influenced by this learning theory.     

Flexible use of recent information in causal and predictive judgments.

Associative and statistical theories of causal and predictive learning make opposite predictions for situations in which the most recent information contradicts the information provided by older trials (e.g., acquisition followed by extinction). Associative theories predict that people will rely on the most recent information to best adapt their behavior to the changing environment. Statistical theories predict that people will integrate what they have learned in the two phases. The results of this study showed one or the other effect as a function of response mode (trial by trial vs. global), type of question (contiguity, causality, or predictiveness), and postacquisition instructions. That is, participants are able to give either an integrative judgment, or a judgment that relies on recent information as a function of test demands. The authors concluded that any model must allow for flexible use of information once it has been acquired. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dissociating Processes Supporting Causal Perception and Causal Inference in the Brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Seeing Versus Doing: Two Modes of Accessing Causal Knowledge.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed ("seeing") or was actively manipulated ("doing"). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Embodiment of emotion concepts.

Theories of embodied cognition hold that higher cognitive processes operate on perceptual symbols and that concept use involves partial reactivations of the sensory-motor states that occur during experience with the world. On this view, the processing of emotion knowledge involves a (partial) reexperience of an emotion, but only when access to the sensory basis of emotion knowledge is required by the task. In 2 experiments, participants judged emotional and neutral concepts corresponding to concrete objects (Experiment 1) and abstract states (Experiment 2) while facial electromyographic activity was recorded from the cheek, brow, eye, and nose regions. Results of both studies show embodiment of specific emotions in an emotion-focused but not a perceptual-focused processing task on the same words. A follow up in Experiment 3, which blocked selective facial expressions, suggests a causal, rather than simply a correlational, role for embodiment in emotion word processing. Experiment 4, using a property generation task, provided support for the conclusion that emotions embodied in conceptual tasks are context-dependent situated simulations rather than associated emotional reactions. Implications for theories of embodied simulation and for emotion theories are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB→E; CA→E; AD→E; and so forth. In 1 story, all variables came from the same domain; in the other, the recurring candidate cause, A, came from a different domain (A was a psychological cause of a biological effect). After receiving this statistical evidence, children were asked to identify the cause of the effect on a new trial. Consistent with the predictions of a Bayesian model, all children were more likely to identify A as the cause within domains than across domains. Whereas 3.5-year-olds learned only from the within-domain evidence, 4- and 5-year-olds learned from the cross-domain evidence and were able to transfer their new expectations about psychosomatic causality to a novel task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Schema acquisition from a single example.

Compares similarity-based learning (SBL) and explanation-based learning (EBL) approaches to schema acquisition. In SBL approaches, concept formation is based on similarity across multiple examples. However, these approaches seem to be appropriate when the learner cannot apply existing knowledge and when the concepts to be learned are nonexplanatory. EBL approaches assume that a schema can be acquired from even a single example by constructing an explanation of the example using background knowledge, and generalizing the resulting explanation. However, unlike the current EBL theories, Exp 1 showed significant EBL occurred only when the background information learned during the experiment was actively used by the Ss. Exp 2 showed the generality of EBL mechanisms across a variety of materials and test procedures. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Perceptual learning of spatiotemporal events: Evidence from an unfamiliar modality.

Perceptual learning was examined in two experiments in which subjects, originally unfamiliar with vibrotactile stimulation, were required to identify dynamic vibrotactile patterns with static visual patterns of the same two-dimensional shapes. In Experiment 1 we examined changes in performance with practice under a variety of vibrotactile spatial and temporal conditions. In Experiment 2 we investigated transfer of learning from one set of vibrotactile patterns to another different set. In neither experiment were subjects supplied with knowledge of results. Substantial perceptual learning (improvement in identification with practice) was observed in Experiment 1, although a minority of subjects did not exhibit improvement. Experiment 2 confirmed the general findings of Experiment 1 and also provided evidence of substantial positive transfer. In both experiments, multidimensional scaling of pattern confusion data revealed that practice (and improvement in identification) did not qualitatively change the relative confusability of patterns, suggesting that the (informative) structure of the patterns, irrespective of familiarity with a specific set of patterns, determined confusability. The findings are interpreted in terms of learning constructs offered by E. J. and J. J. Gibson. We conclude by considering the prospects that a connectionist mechanism can account for the observed perceptual learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Avoidance learning of perceptual defense and vigilance.

Thirty-two undergraduates were randomly assigned to defense and vigilance training groups. "This study supports the view that perceptual defense and vigilance are learned reactions to anxiety arousing stimuli." A behavior theory analysis of the learning process is proposed. "According to this analysis, perceptual defense is learned when the perceptual response to a threatening stimulus is punished and competing perceptual responses are instrumental to anxiety reduction. Competing perceptual responses when reinforced are strengthened at the expense of the critical perceptual response. Perceptual vigilance is learned when the perceptual response to a threatening stimulus is reinforced by anxiety reduction and competing perceptual responses are punished." Learning for both groups "proceeded in the absence of awareness." (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to Greville and Buehner (2010).

Reports an error in "Temporal predictability facilitates causal learning" by W. James Greville and Marc J. Buehner (Journal of Experimental Psychology: General, 2010[Nov], Vol 139[4], 756-771). Figure 2 (p. 759) contained an error. The corrected figure appears in the correction. (The following abstract of the original article appeared in record 2010-22538-005.) Temporal predictability refers to the regularity or consistency of the time interval separating events. When encountering repeated instances of causes and effects, we also experience multiple cause–effect temporal intervals. Where this interval is constant it becomes possible to predict when the effect will follow from the cause. In contrast, interval variability entails unpredictability. Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning. The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal intervals, (b) that causal judgments decline as a function of temporal uncertainty, and (c) that this effect remains undiminished with increased learning time. The results therefore clearly indicate that temporal predictability facilitates causal discovery. The authors considered the implications of their findings for various theoretical perspectives, including associative learning theory, the attribution shift hypothesis, and causal structure models. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Perceptual categories for spatial layout

The central problems of vision are often divided into object identification and localization. Object identification, at least at fine levels of discrimination, may require the application of top-down knowledge to resolve ambiguous image information. Utilizing top-down knowledge, however, may require the initial rapid access of abstract object categories based on low-level image cues. Does object localization require a different set of operating principles than object identification or is category determination also part of the perception of depth and spatial layout? Three-dimensional graphics movies of objects and their cast shadows are used to argue that identifying perceptual categories is important for determining the relative depths of objects. Processes that can identify the causal class (e.g. the kind of material) that generates the image data can provide information to determine the spatial relationships between surfaces. Changes in the blurriness of an edge may be characteristically associated with shadows caused by relative motion between two surfaces. The early identification of abstract events such as moving object/shadow pairs may also be important for depth from shadows. Knowledge of how correlated motion in the image relates to an object and its shadow may provide a reliable cue to access such event categories.     

因果推断三种分析框架及其应用综述

介绍因果推断所涉及的基本概念及其三种分析框架:反事实框架、潜在结果模型和结构因果模型。首先,从反事实框架介绍因果效应的发端;然后,从基于反事实的两个因果推断分析框架:潜在结果模型和结构因果模型,来分别阐述两个分析框架所涉及的关键理论和应用方法。其中,潜在结果模型使用数学和可计算的语言对因果理论进行阐述,是一种将假设、命题和结论清晰化表达的计算模型,其在原因和结果变量已知的前提下定量分析原因变量对结果变量的因果效应,并对缺失的潜在结果进行补齐,使观察性研究的效果接近试验性研究。结构因果模型则是一种基于图论的因果推断方法,它将事件分为观察、干预和反事实三个层级,并通过do运算将干预和反事实层级的因果关系都降维成可以通过统计学手段解决的问题。最后,探讨了现今多领域内因果推断的应用场景,并总结了三种分析框架的异同点。      

Word, thought, and deed: The role of object categories in children's inductive inferences and exploratory play.

Previous research (e.g., S. A. Gelman & E. M. Markman, 1986; A. Gopnik & D. M. Sobel, 2000) suggests that children can use category labels to make inductive inferences about nonobvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when (a) the property being projected varies within the category, (b) the category is arbitrary (e.g., things smaller than a bread box), or (c) the property being projected is due to an exogenous intervention rather than intrinsic to the object kind. In 4 studies, the authors showed that preschoolers (M = 48 months; range = 42-57 months) were sensitive to these constraints on induction and selectively engaged in exploration when evidence about objects' causal properties conflicted with inductive generalizations from the objects' kind to their causal powers. This suggests that the exploratory actions children generate in free play could support causal learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Elements of the Socratic method: II. Inductive reasoning.

The Socratic method uses systematic questioning and inductive reasoning to help clients derive universal definitions. In psychotherapy, inductive reasoning can be based on enumerative generalizations, analogical comparisons, and eliminative causal reasoning. Enumerative generalizations use pattern identification to support a conclusion about an entire group of events. Analogical comparisons help clients transfer knowledge from familiar to novel situations. Eliminative causal reasoning involves manipulating environmental conditions to examine possible causes of specific problematic events. After a probable cause has been identified, it can be modified through treatment. The clinical utility and fallacies of logical reasoning are discussed as they apply to each of the 3 forms of inductive reasoning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

"Common factors in the identification of an assortment of brief everyday sounds": Correction.

Reports that the article by J. A. Ballas in the Journal of Experimental Psychology: Human Perception & Performance, 1993(Apr) [1992 (sic)], Vol 19(2) [Vol 18 (sic)], 250–267, is in the public domain. (The following abstract of this article originally appeared in record 1993-28211-001.) Acoustic, ecological, perceptual and cognitive factors that are common in the identification of 41 brief, varied sounds were evaluated. In Exp 1, identification time and accuracy, causal uncertainty values, and spectral and temporal properties of the sounds were obtained. Exp 2 was a survey to obtain ecological frequency counts. Exp 3 solicited perceptual–cognitive ratings. Factor analyses of spectral parameters and perceptual–cognitive ratings were performed. Identification time and causal uncertainty are highly interrelated, and both are related to ecological frequency and the presence of harmonics and similar spectral bursts. Exps 4 and 5 used a priming paradigm to verify correlational relationships between identification time and causal uncertainty and to assess the effect of sound typicality. Results support a hybrid approach for theories of everyday sound identification. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reuniting perception and conception

Work in philosophy and psychology has argued for a dissociation between perceptually-based similarity and higher-level rules in conceptual thought. Although such a dissociation may be justified at times, our goal is to illustrate ways in which conceptual processing is grounded in perception, both for perceptual similarity and abstract rules. We discuss the advantages, power and influences of perceptually-based representations. First, many of the properties associated with amodal symbol systems can be achieved with perceptually-based systems as well (e.g. productivity). Second, relatively raw perceptual representations are powerful because they can implicitly represent properties in an analog fashion. Third, perception naturally provides impressions of overall similarity, exactly the type of similarity useful for establishing many common categories. Fourth, perceptual similarity is not static but becomes tuned over time to conceptual demands. Fifth, the original motivation or basis for sophisticated cognition is often less sophisticated perceptual similarity. Sixth, perceptual simulation occurs even in conceptual tasks that have no explicit perceptual demands. Parallels between perceptual and conceptual processes suggest that many mechanisms typically associated with abstract thought are also present in perception, and that perceptual processes provide useful mechanisms that may be co-opted by abstract thought.