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

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

Generic and Specific Numeral Classifier Input and its Relation to Children's Classifier and Number Learning

In Japanese, numeral classifiers-or measure words-co-occur with numbers in counting phrases. The present study characterized parent numeral classifier use and its relation to children's classifier acquisition and number learning. Twenty-four Japanese-speaking parents and their two- to six-year-old children viewed and talked about two wordless picture books about counting to each other. Children also participated in a Counting task and Give-N task. Results revealed (1) parents' classifier use changed in relation to children's age and classifier use, and (2) parents' increased use of specific classifiers was uniquely associated with children's number understanding. These results suggest that aspects of children's language and numerical development are related to parents' language input, demonstrating the importance of examining the relation between language and cognition in a developmental context

Generic and Specific Numeral Classifier Input and its Relation to Children's Classifier and Number Learning

In Japanese, numeral classifiers-or measure words-co-occur with numbers in counting phrases. The present study characterized parent numeral classifier use and its relation to children's classifier acquisition and number learning. Twenty-four Japanese-speaking parents and their two- to six-year-old children viewed and talked about two wordless picture books about counting to each other. Children also participated in a Counting task and Give-N task. Results revealed (1) parents' classifier use changed in relation to children's age and classifier use, and (2) parents' increased use of specific classifiers was uniquely associated with children's number understanding. These results suggest that aspects of children's language and numerical development are related to parents' language input, demonstrating the importance of examining the relation between language and cognition in a developmental context

Why children learn color and size words so differently: evidence from adults' learning of artificial terms.

An adult simulation study examined why children's learning of color and size terms follow different developmental patterns, one in which word comprehension precedes success in nonlinguistic matching tasks versus one in which matching precedes word comprehension. In 4 experiments, adults learned artificial labels for values on novel dimensions. Training mimicked that characteristic for children learning either color words or size words. The results suggest that the learning trajectories arise from the different frames in which different dimensions are trained: Using a comparison (size-like) training regimen helps learners pick out the relevant dimension, and using a categorization (color-like) training regimen helps the learner correctly comprehend and produce dimension terms. The results indicate that the training regimen, not the meanings of the terms or the specific dimensions, determines the pattern of learning