Language Evolution and Computation Bibliography

The paper argues that the structure, derivation and evolution of syntax is given by the sequence (elements, concatenation, embedding). We discuss the implications of this sequence for language and the numeral system in general and on the evolution of language in particular. A four-stage model of the evolution of syntax, broadly compatible with several earlier scenarios, is proposed. The four stages are (1) signs, (2) increased number of signs, (3) commutative concatenation, and (4) noncommutative concatenation. We support the model by showing that its stages can be adaptive per se, which could explain why they evolved. We also identify two preconditions for maintaining the stages: stage (2) depends on the ability to conceptualize asymmetric relations between concepts and the adaptiveness of stage (3) depends on cultural constraints on linguistic interpretation.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary), and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval, learning, decay). A cognitive model is described that encodes abstract concepts with small sets of concrete, related concepts (directing), and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge, which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure, but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding, predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying.

The objective of this research is to develop a system for language learning based on a ``minimum'' of pre-wired language-specific functionality, that is compatible with observations of perceptual and language capabilities in the human developmental trajectory. In the proposed system, meaning (in terms of descriptions of events and spatial relations) is extracted from video images based on detection of position, motion, physical contact and their parameters. Meaning extraction requires attentional mechanisms that are implemented from low-level perceptual primitives. Mapping of sentence form to meaning is performed by learning grammatical constructions, i.e., sentence to meaning mappings as defined by Goldberg [Goldberg, A. (1995). Constructions. Chicago and London: Univ. of Chicago Press]. These are stored and retrieved from a ``construction inventory'' based on the constellation of grammatical function words uniquely identifying the target sentence structure. The resulting system displays robust acquisition behavior that reproduces certain observations from developmental studies, with very modest ``innate'' language specificity.

This paper presents an approach to solve the symbol grounding problem within the framework of embodied cognitive science. It will be argued that symbolic structures can be used within the paradigm of embodied cognitive science by adopting an alternative definition of a symbol. In this alternative definition, the symbol may be viewed as a structural coupling between an agent's sensorimotor activations and its environment. A robotic experiment is presented in which mobile robots develop a symbolic structure from scratch by engaging in a series of language games. In this experiment it is shown that robots can develop a symbolic structure with which they can communicate the names of a few objects with a remarkable degree of success. It is further shown that, although the referents may be interpreted differently on different occasions, the objects are usually named with only one form.