The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…

Production and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing--the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition?…

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we…

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…

Human neural and behavioral activities have been reported to exhibit fractal dynamics known as "1/f noise," which is more aptly named "1/f scaling." Some argue that 1/f scaling is a general and pervasive property of the dynamical substrate from which cognitive functions are formed. Others argue that it is an idiosyncratic property of…

An item that stands out (is isolated) from its context is better remembered than an item consistent with the context. This isolation effect cannot be accounted for by increased attention, because it occurs when the isolated item is presented as the first item, or by impoverished memory of nonisolated items, because the isolated item is better…