Number versus continuous quantity in numerosity judgments by fish

A fundamental question in human cognition is how people reason about space. We use a computational model to explore cross-cultural commonalities and differences in spatial cognition. Our model is based upon two hypotheses: (1) the structure-mapping model of analogy can explain the visual comparisons used in spatial reasoning; and (2) qualitative, structural representations are computed by people’s visual systems and used in these comparisons. We apply our model to a visual oddity task, in which individuals are shown an array of two-dimensional images and asked to the pick the one that does not belong. This task was previously used to evaluate understanding of geometric concepts in two disparate populations: North Americans, and the Mundurukú, a South American indigenous group. Our model automatically generates representations of each hand-segmented image and compares them to solve the task. The model achieves human-level performance on this task, and problems that are hard for the model are also difficult for people in both cultures. Furthermore, ablation studies on the model suggest explanations for cross-cultural differences in terms of differences in spatial representations.     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

Computational exploration of metaphor comprehension processes using a semantic space model

Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view ( Bowdle & Gentner, 2005 ), aptness view ( Glucksberg & Haught, 2006b ), and interpretive diversity view ( Utsumi, 2007 ); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization and comparison processes. This article attempts to answer the question regarding which views are plausible by using cognitive modeling and computer simulation based on a semantic space model. In the simulation experiment, categorization and comparison processes are modeled in a semantic space constructed by latent semantic analysis. These two models receive word vectors for the constituent words of a metaphor and compute a vector for the metaphorical meaning. The resulting vectors can be evaluated according to the degree to which they mimic the human interpretation of the same metaphor; the maximum likelihood estimation determines which of the two models better explains the human interpretation. The result of the model selection is then predicted by three metaphor properties (i.e., vehicle conventionality, aptness, and interpretive diversity) to test the three views. The simulation experiment for Japanese metaphors demonstrates that both interpretive diversity and vehicle conventionality affect the choice between the two processes. On the other hand, it is found that metaphor aptness does not affect this choice. This result can be treated as computational evidence supporting the interpretive diversity and conventionality views.     

A memory-based model of Hick's law

We propose and evaluate a memory-based model of Hick’s law, the approximately linear increase in choice reaction time with the logarithm of set size (the number of stimulus–response alternatives). According to the model, Hick’s law reflects a combination of associative interference during retrieval from declarative memory and occasional savings for stimulus–response repetitions due to non-retrieval. Fits to existing data sets show that the model accounts for the basic set-size effect, changes in the set-size effect with practice, and stimulus–response-repetition effects that challenge the information-theoretic view of Hick’s law. We derive the model’s prediction of an interaction between set size, stimulus fan (the number of responses associated with a particular stimulus), and stimulus–response transition, which is subsequently tested and confirmed in two experiments. Collectively, the results support the core structure of the model and its explanation of Hick’s law in terms of basic memory effects.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.     

新生市民主动发展倾向对城市生活适应性的影响:社会支持的中介效应

为探究主动发展倾向和社会支持对新生市民城市生活适应性的作用机制,本研究构建了结构方程模型.采用两个自编量表和一个现成量表,运用两阶段抽样调查法,在作为统筹城乡发展试验区的重庆市范围内,对已经取得城镇户口的新生市民进行问卷调查,获得有效样本322人.对采集的数据运用统计分析软件SPSS17.0和AMOS18.0进行数据分析和模型检验.结果表明,城市化新生市民的主动发展倾向和社会支持均对新生市民的城市生活适应具有显著影响,其中,社会支持在主动发展倾向对城市生活适应影响中具有部分中介作用.     

In search of average growth: Describing within-year oral reading fluency growth across Grades 1–8

Measures of oral reading fluency (ORF) are perhaps the most often used assessment to monitor student progress as part of a response to intervention (RTI) model. Rates of growth in research and aim lines in practice are used to characterize student growth; in either case, growth is generally defined as linear, increasing at a constant rate. Recent research suggests ORF growth follows a nonlinear trajectory, but limitations related to the datasets used in such studies, composed of only three testing occasions, curtails their ability to examine the true functional form of ORF growth. The purpose of this study was to model within-year ORF growth using up to eight testing occasions for 1448 students in Grades 1 to 8 to assess (a) the average growth trajectory for within-year ORF growth, (b) whether students vary significantly in within-year ORF growth, and (c) the extent to which findings are consistent across grades. Results demonstrated that for Grades 1 to 7, a quadratic growth model fit better than either linear or cubic growth models, and for Grade 8, there was no substantial, stable growth. Findings suggest that the expectation for linear growth currently used in practice may be unrealistic.     

Exploring Pathways From Television Viewing to Academic Achievement in School Age Children

The author's purpose in this study was to test 4 hypotheses that proposed different paths for the influences of children's television viewing on their academic achievement. Data were drawn from the 1997 Child Development Supplement (CDS) to the Panel Study of Income Dynamics (PSID). The population for this study included 1,203 children between the ages of 6 and 13 years from the CDS-PSID data set. The author used structural equation modeling to test pathways from children's television viewing to their academic achievement. The author assumed that children's television viewing hindered their academic achievement by reducing certain traits that related to academic achievement. Results showed that 3 hypothetical models fit the data—the time-displacement hypothesis, the mental effort-passivity hypothesis, and the attention-arousal hypothesis. A 4th hypothetical model, the learning-information hypothesis, which proposed that children's television viewing practices stimulate their academic achievement, was not supported. In sum, children who watched more television tended to spend less time doing homework, studying, and reading for leisure. In addition, their behaviors became more impulsive, which resulted in an eventual decrease in their academic achievement.     

Using video modeling and reinforcement to teach perspective-taking skills to children with autism

We evaluated video modeling and reinforcement for teaching perspective-taking skills to 3 children with autism using a multiple baseline design. Video modeling and reinforcement were effective; however, only 2 children were able to pass an untrained task, indicating limited generalization. The findings suggest that video modeling may be an effective technology for teaching perspective taking if researchers can continue to develop strategies for enhancing the generalization of these new skills.