We report a new phenomenon associated with language comprehension: the action–sentence compatibility effect (ACE). Participants judged whether sentences were sensible by making a response that required moving toward or away from their bodies. When a sentence implied action in one direction (e.g., “Close the drawer ” implies action away from the body), the participants had difficulty making a sensibility judgment requiring a response in the opposite direction. The ACE was demonstrated for three sentences types: imperative sentences, sentences describing the transfer of concrete objects, and sentences describing the transfer of abstract entities, such as “Liz told you the story. ” These data are inconsistent with theories of language comprehension in which meaning is represented as a set of relations among nodes. Instead, the data support an embodied theory of meaning that relates the meaning of sentences to human action. How language conveys meaning remains an open question. The dominant approach is to treat language as a symbol manipulation system: Language conveys meaning by using abstract, amodal, and arbitrary symbols (i.e., words) combined by syntactic rules (e.g., Burgess & Lund, 1997; Chomsky, 1980; Fodor, 2000; Kintsch, 1988; Pinker, 1994). Words are abstract in that the same word, such as “chair, ” is used for big chairs and little chairs, words are amodal in that the same word is used when chairs are spoken about or written about, and words are arbitrarily related to their referents in that the phonemic and orthographic characteristics of a word bear no relationship to the physical or functional characteristics of the word’s referent. An alternative view is that linguistic meaning is

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ing away from the specific coding of the spinal fields, the examples from neurobiology provide the framework for a motor control system based on a small number of additive primitives (or basis behaviors) sufficient for a rich output movement repertoire. Our previous work (Matari'c 1995, Matari'c 1997), inspired by the same biological results, has successfully applied the idea of basis behaviors to control of mobile robots 6 by fitting it directly into the modular behavior-based control paradigm. Applictions of schema theory (Arbib 1992) to behavior-based mobile robots (Arkin 1987) have employed a similar notion of composable behaviors, stemming from foundations in neuroscience (Arbib 1981, Arbib 1989). The idea of using such primitives for articulator control has been recently studied in robotics. Williamson (1996) and Marjanovi'c, Scassellati & Williamson (1996) developed a 6 DOF (degrees of freedom) robot arm controller. While in the biological and mobile robotics work primitives c...

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We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and understand the actions of others in intentional terms. Among the key ideas that we draw from work on the development of human social intelligence, the most crucial is the hypothesis that in human infants, imitative interactions, starting with facial mimicry, are a significant stepping-stone in developing appropriate social behavior, learning to predict other’s actions, and ultimately, understanding the intensions of others. 1

This paper describes experiments performed with forty subjects wearing an eye-tracker and watching and imitating videos of finger, hand, and arm movements. For all types of stimuli, the subjects tended to fixate on the hand, regardless of whether they were imitating or just watching. The results lend insight into the connection between visual perception and motor control, suggesting that: 1) people analyze human arm movements largely by tracking the hand or the end-point, even if the movement is performed with the entire arm, and 2) when imitating, people use internal innate and learned models of movement, possibly in the form of motor primitives, to recreate the details of whole-arm posture and movement from end-point trajectories. Keywords: Perceptual-motor interaction; Eye-tracking; Movement imitation Theme: Motor Systems and Sensorimotor Integration Topic: Control of Posture and Movement 1 Introduction Imitation is one of the most ubiquitous forms of human learning. What appea...

This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform basic visually guided movement.

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars.

Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the inheritance of acquired characteristics. Unlike trial-and-error-based learning methods such as reinforcement learning, imitation allows rapid learning.

How does an individual use the knowledge acquired through self exploration as a manipulable model through which to understand others and benefit from their knowledge? How can developmental and social learning be combined for their mutual benefit? In this paper we review a hierarchical architecture (HAMMER) which allows a principled way for combining knowledge through exploration and knowledge from others, through the creation and use of multiple inverse and forward models. We describe how Bayesian Belief Networks can be used to learn the association between a robot’s motor commands and sensory consequences (forward models), and how the inverse association can be used for imitation. Inverse models created through self exploration, as well as those from observing others can coexist and compete in a principled unified framework, that utilises the simulation theory of mind approach to mentally rehearse and understand the actions of others. 1.