Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

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In embodied artificial intelligence is it of interest to study the informational relationships between the agent, its actions, and the environment. This paper presents a number of statistical measures to compute the informational distance between sensors including the information metric, correlation coefficient, Hellinger distance, Kullback-Leibler, and Jensen-Shannon divergence. The methods are compared using the sensory reconstruction method to find spatial positions of visual sensors of different modalities in a sensor integration task. The results show how the information metric can find relations not found by the other measures.

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from early infant psychology (up to 3 months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. An experimental learning model is described and results from robotic experiments using the model are presented and discussed.

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from psychological knowledge of pre-grasping infants (up to 3 months of age) to examine the issues and factors that might produce similar mechanisms for use in robotic systems. The study includes discussion of results from robotic experiments on sensory-motor models and key issues are raised throughout.

of mental concepts to a reactive layer:

The bodily change in infancy due to growth is a fundamental challenge for the bootstrapping of sensorimotor coordination. We argue that learning by doing, and thus a babbling of goals instead of motor commands provides an appealing explanation for the success of infants in that bootstrapping. We show that Goal Babbling allows to bootstrap reaching skills during different growth patterns on a robot arm with five degrees of freedom and on the infant like humanoid iCub. 1.

Coupling of mental concepts to a reactive layer: incremental approach in system design