Language Evolution and Computation Bibliography

Computer models of evolutionary phenomena often assume that the fitness of an individual can be evaluated in isolation, but effective communication requires that individuals interact. Existing models directly reward speakers for improved behavior on the part of the listeners so that, essentially, effective communication is fitness. We present new models in which, even though 'speaking truthfully' provides no tangible benefit to the speaker, effective communication nonetheless evolves. A large population is spatially distributed so that 'communication range' approximately correlates with 'breeding range,' so that most of the time 'you'll be talking to family,' allowing kin selection to encourage the emergence of communication. However, the emergence of altruistic communication also creates niches that can be exploited by 'information parasites.' The new models display complex and subtle long-term dynamics as the global implications of such social dilemmas are played out.

Recurrent neural networks can be trained to recognize strings generated by context-free grammars, but the ability of the networks to do so depends on their having an appropriate set of initial connection weights. Simulations of evolution were performed on populations of simple recurrent networks where the selection criterion was the ability of the networks to recognize strings generated by grammars. The networks evolved sets of initial weights from which they could reliably learn to recognize the strings. In order to recognize if a string was generated by a given context-free grammar, it is necessary to use a stack or counter to keep track of the depth of embedding in the string. The networks that evolved in our simulations are able to use the values passed along their recurrent connections for this purpose. Furthermore, populations of networks can evolve a bias towards learning the underlying regularities in a class of related languages. These results suggest a new explanation for the ``critical period'' effects observed in the acquisition of language and other cognitive faculties. Instead of being the result of an exogenous maturational process, the degraded acquisition ability may be the result of the values of innately specified initial weights diverging in response to training on spurious input.

The tasks that animals perform require a high degree of intelligence. Animals forage for food, migrate, navigate, court mates, rear offspring, defend against predators, construct nests, and so on. These tasks commonly require social interaction/cooperation and are accomplished by animal nervous systems, which are the result of billions of years of evolution and complex developmental/learning processes. The Artificial Life (AL) approach to synthesizing intelligent behavior is guided by this biological perspective. In this article we examine some of the numerous open problems in synthesizing intelligent animal behavior (especially cooperative behavior involving communication) that face the field of AL, a discipline still in its infancy.

This paper presents the finding that the invocation of new words in human language samples is governed by a slowly changing Poisson process. The time dependent rate constant for this process has the form [small lambda, Greek(t)...]. This form implies that there are opening, middle and final phases to the introduction of new words, each distinguished by a dominant rate constant, or equivalently, rate of decay. With the occasional exception of the phase transition from beginning to middle, the rate small lambda, Greek(t) decays monotonically. Thus, small lambda, Greek(t) quantifies how the penchant of humans to introduce new words declines with the progression of their narratives, written or spoken.

In this classic study, the world's leading expert on language and the mind lucidly explains everything you always wanted to know about languages: how it works, how children learn it, how it changes, how the brain computes it, and how it envolved. With wit, erudition, and deft use it everyday examples of humor and wordplay, Steven Pinker weaves our vast knowledge of language into a compelling story: language is a human instinct, wired into our brains by evolution like web spinning in spiders or sonar bats. The Language Instinct received the William James Book Prize from the American Psychological Association and the Public Interest Award from the Linguistics Society of America.

Language and History: Voices from the Past.

Language Families: What Is Known.

Controversy: What Is Debated.

Native Americans: Language in the New World.

The Origin of Language: Are There Global Cognates?.

A Window on the World: What Has Been Resolved.

Genes: Biology and Language.

The Emerging Synthesis: On the Origin of Modern Humans.

Epilogue.

This thesis presents a connectionist theory of how infinite languages may fit within nite minds. Arguments are presented against the distinction between linguistic competence and observable language performance. It is suggested that certain kinds of finite state automata--i.e., recurrent neural networks|are likely to have sufficient computational power, and the necessary generalization capability, to serve as models for the processing and acquisition of linguistic structure. These arguments are further corroborated by a number of computer simulations, demonstrating that recurrent connectionist models are able to learn complex recursive regularities and have powerful generalization abilities. Importantly, the performance evinced by the networks are comparable with observed human behavior on similar aspects of language. Moreover, an evolutionary account is provided, advocating a learning and processing based explanation of the origin and subsequent phylogenetic development of language. This view construes language as a nonobligate symbiant, arguing that language has evolved to fit human learning and processing mechanisms, rather than vice versa. As such, this perspective promises to explain linguistic universals in functional terms, and motivates an account of language acquisition which incorporates innate, but not language-specific constraints on the learning process. The purported poverty of the stimulus is re-appraised in this light, and it is concluded that linguistic structure may be learnable by bottom-up statistical learning models, such as, connectionist neural networks.