How groups develop a specialized domain vocabulary: A cognitive multi-agent model

Abstract

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.

Introduction

Languages evolve: like biological systems, they undergo mutation and selection as they are passed on between speakers and generations. Human communication as well as its biological analog evolve under environmental constraints. Fitness of a set of linguistic devices is, thus, also a function of the cognitive facilities. In this paper, we assume that the acquisition and retention of linguistic facts in memory is a crucial factor determining how languages are developed by communities. We use a cognitive architecture to provide an independently validated model of human memory to simulate the evolutionary process that produces a crucial part of the communication system: the vocabulary.

Recent models of dialogue describe how interlocutors develop representation systems in order to communicate; such systems can, for instance, be observed using referring expressions that identify locations in a maze. Experiments have shown that referring expressions converge on a common standard (Garrod & Doherty, 1994). Pickering and Garrod’s (2004) Interactive Alignment Model suggests that explicit negotiation and separate models of the interlocutor’s mental state are not necessary, as long as each speaker is coherent and adapts to his interlocutors, as speakers are known to do on even simple, linguistic levels (lexical, syntactic). This shifts the weight of the task from a sophisticated reasoning device to the simpler learning mechanism of the individual.

Some evolutionary models see the transmission of cultural information as a directed process, in which information is passed only from the older to the younger generation. Other models explain the emergence of language as a continuous process within generations. This process may be modeled as convergence towards the bias set by innate learning and processing systems of the individual, but it can also be seen as the result of ongoing changes that interact with the cultural environment of the collaborating language users. There, meaning–symbol connections spread between collaborating agents and ultimately converge on a predominant one. It is the dichotomy between individual and community-based learning that motivated the experiments by Fay, Garrod, Roberts, and Swoboda (2010) and Garrod, Fay, Lee, Oberlander, and Macleod (2007), which serve as the basis for the model presented here.

In the society of cognitive agents in Fay’s study and in our experiments, agents adapt their communication system collaboratively to the environmentally shaped and cognitively constrained needs of each individual. With our model, we aim to use a cognitive framework – specifically a memory model – to reflect processes in the individual that give rise to emergent convergence and learning within the community. By this, we acknowledge the fact that cultural evolution is constrained by individual learning; each agent learns according to their cognitive faculty (cf., Christiansen & Chater, 2008). The possibility of cultural language evolution between generations has been supported by computational simulations (e.g., Brighton et al., 2005, Kirby and Hurford, 2002). Kirby and Hurford’s (2002) Iterated Learning model of language evolution describes vertical development of a language system by feeding developed linguistic signs or conventions back into another agent. Thus, it abstracts away from the processes within the community that forms a generation, yet does not rely only on emergence through biological evolution of the system that processes language.

The individual language faculty as a result of biological evolution and adaptation to cultural language has been the focus of psycholinguistic models proposing specialized mechanisms (theChomskian viewpoint). While syntactic theory has long relied on production rule systems, more recent lexicalist approaches (Jackendoff, 1975) also integrate well with theories of general cognition (ACT-R: Anderson et al., 2004; SOAR: Laird & Rosenbloom, 1987). In this sense, the model presented here reflects the development of a common vocabulary, which we see as prototypical for that of the lexicon, the central component of a language.

Indeed, the multi-agent model discussed in the present paper sees part of the linguistic process as an instantiation of general cognition: the composition and retrieval of signs follows general cognitive mechanisms. Adaptation according to experience is determined by human learning behavior. Simulation in validated cognitive frameworks allows us to constrain the learning process by the bounds of human memory.

Griffiths and Kalish (2007), for instance, model language evolution through iteration among rational learners in a Bayesian framework; the purpose of the present project is to tie the simulation of language evolution to a concrete experiment and a more process-oriented cognitive architecture than the Bayesian framework. ACT-R’s learning mechanisms add a notion of recency (decay) to the Bayesian view. Work on language processing has modeled the relationship to ACT-R memory retrieval, both for language comprehension (Ball et al., 2007, Budiu and Anderson, 2002, Lewis et al., 2005, Stocco and Crescentini, 2005) and for language production (Reitter, 2008).

We introduce a cognitive model that simulates a participant in the experiment; multiple models interact as a community of participants. The purpose of this paper is to observe how a compositional vocabulary is created between collaborating agents in a computational cognitive simulation. Like Smith, Brighton, and Kirby (2003), we represent meaning–signal mappings using associations between memory items to create compositional signs, but we augment this model of pre-existing knowledge with one of explicitly encoded and retrievable domain knowledge. Other simulations have shown that cultural evolution leads to compositional languages (Kirby & Hurford, 2002).

We will show that the model demonstrates learning behavior similar to the empirical data. We assume these agents share a common reference system initially, display cooperative behavior and adopt mixed roles as communicators. Therefore, we explore different scenarios that test the necessity of our preconditions, in particular the fact that each agent can be both on the sending and the receiving end of the communications. The underlying question is whether dialogue (producing and comprehending language) is necessary for participants to establish joint communication. In search for factors that influence community convergence, we also examine the effect of initial common ground between agents and the role of the structure of the network that describes each agent’s knowledge. Specifically, we present results suggesting that the specific power-law distribution found in ontologies is beneficial to the within-community convergence.