Comparison and Mapping Facilitate Relation Discovery and Predication

Relational concepts play a central role in human perception and cognition, but little is known about how they are acquired. For example, how do we come to understand that physical force is a higher-order multiplicative relation between mass and acceleration, or that two circles are the same-shape in the same way that two squares are? A recent model of relational learning, DORA (Discovery of Relations by Analogy; Doumas, Hummel & Sandhofer, 2008), predicts that comparison and analogical mapping play a central role in the discovery and predication of novel higher-order relations. We report two experiments testing and confirming this prediction

Order of Presentation Effects in Learning Color Categories

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Distinct Characteristics of Circulating Vascular Endothelial Growth Factor-A and C Levels in Human Subjects

The mechanisms that lead from obesity to atherosclerotic disease are not fully understood. Obesity involves angiogenesis in which vascular endothelial growth factor-A (VEGF-A) plays a key role. On the other hand, vascular endothelial growth factor-C (VEGF-C) plays a pivotal role in lymphangiogenesis. Circulating levels of VEGF-A and VEGF-C are elevated in sera from obese subjects. However, relationships of VEGF-C with atherosclerotic risk factors and atherosclerosis are unknown. We determined circulating levels of VEGF-A and VEGF-C in 423 consecutive subjects not receiving any drugs at the Health Evaluation Center. After adjusting for age and gender, VEGF-A levels were significantly and more strongly correlated with the body mass index (BMI) and waist circumference than VEGF-C. Conversely, VEGF-C levels were significantly and more closely correlated with metabolic (e.g., fasting plasma glucose, hemoglobin A1c, immunoreactive insulin, and the homeostasis model assessment of insulin resistance) and lipid parameters (e.g., triglycerides, total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), and non-high-density-lipoprotein cholesterol (non-HDL-C)) than VEGF-A. Stepwise regression analyses revealed that independent determinants of VEGF-A were the BMI and age, whereas strong independent determinants of VEGF-C were age, triglycerides, and non-HDL-C. In apolipoprotein E-deficient mice fed a high-fat-diet (HFD) or normal chow (NC) for 16 weeks, levels of VEGF-A were not significantly different between the two groups. However, levels of VEGF-C were significantly higher in HFD mice with advanced atherosclerosis and marked hypercholesterolemia than NC mice. Furthermore, immunohistochemistry revealed that the expression of VEGF-C in atheromatous plaque of the aortic sinus was significantly intensified by feeding HFD compared to NC, while that of VEGF-A was not. In conclusion, these findings demonstrate that VEGF-C, rather than VEGF-A, is closely related to dyslipidemia and atherosclerosis

A mechanism for the cortical computation of hierarchical linguistic structure

Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose—learning relational reasoning—processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and “cortical” oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism—using time to encode information across a layered network—generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computatio

Retrieval Dynamics of In-the-Moment and Long-Term Statistical Word Learning

Theories of word learning have proposed several tools that children and adults use to reduce the difficulty of the word learning problem. However, we propose that reducing difficulty may be detrimental—difficulty may promote long-term word learning. In this study, we tested predictions of desirable difficulties and the retrieval effort hypothesis in a crosssituational word learning paradigm. Learners were presented with objects and labels in three conditions of learning (easy, medium, and difficult) and tested either immediately or one week later. Results revealed a counterintuitive pattern of performance—initially, participants in the easy condition had the highest performance. However, after a one week delay, participants in the medium condition had the highest performance. Participants ‟ self-report of retrieval difficulty during learning is used to account for differences in performance over time. This work is discussed in terms of the implications for several fields of cognitive science: statistical learning, human memory, and language and cognitive development

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

Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum