Cross-situational and supervised learning in the emergence of communication

📝 Abstract

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

💡 Analysis

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

📄 Content

arXiv:0901.4012v3 [cs.LG] 28 Nov 2009 Cross-situational and supervised learning in the emergence of communication Jos´e F. Fontanari1, ∗and Angelo Cangelosi2, † 1Instituto de F´ısica de S˜ao Carlos, Universidade de S˜ao Paulo, Caixa Postal 369, 13560-970 S˜ao Carlos, S˜ao Paulo, Brazil 2Centre for Robotics and Neural Systems, University of Plymouth, Plymouth PL4 8AA, United Kingdom Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. I. INTRODUCTION How a coherent lexicon can emerge in a group of inter- acting agents is a major open issue in the language evolu- tion and acquisition research area (Hurford, 1989; Nowak & Krakauer, 1999; Steels, 2002; Kirby, 2002; Smith, Kirby, & Brighton, 2003). In addition, the dynamics in the self-organization of shared lexicons is one of the issues to which computational and mathematical modeling can contribute the most, as the emergence of a lexicon from scratch implies some type of self-organization and, pos- sibly, threshold phenomenon. This cannot be completely understood without a thorough exploration of the pa- rameter space of the models (Baronchelli, Felici, Loreto, Caglioli, & Steels, 2006). There are two main research avenues to investigate the emergence or bootstrapping of a lexicon. The first approach, inspired by the seminal work of Pinker and Bloom (1990) who argued that natural selection is the main design principle to explain the emergence and com- plex structure of language, resorts to evolutionary algo- rithms to evolve the shared lexicon. The key element here is that an improvement on the communication ability of an individual results, in average, in an increase of the number of offspring it produces (Hurford, 1989; Nowak & Krakauer, 1999; Cangelosi, 2001; Fontanari & Perlovsky, 2007, 2008). The second research avenue, which we will follow in this paper, argues for a culturally based view of language evolution and so it assumes that the lexicons are acquired and modified solely through learning during the individual’s lifetime (Steels, 2002; Smith, Kirby, & Brighton, 2003). Of course, if there is a fact about language which is uncontroversial, it is that the lexicon must be learned from the active or passive interaction between children and language-proficient adults. The issue of whether this ability to learn the lexicon is due to some domain-general ∗Electronic address: fontanari@ifsc.usp.br †Electronic address: A.cangelosi@plymouth.ac.uk learning mechanism, or is an innate ability, unique to humans, is still on the table (Bates & Elman, 1996). In the problem we address here, there is simply no language- proficient individuals, so it is not so far-fetched to put forward a biological rather than a cultural explanation for the emergence of a self-organized lexicon. Nevertheless, in this contribution we will use many insights produced by research on language acquisition by children (see, e.g., Gleitman, 1990; Bloom, 2000) to study different learning strategies. From a developmental perspective, there are basi- cally two competing schemes for lexicon acquisition by children (Rosenthal & Zimmerman, 1978). The first scheme, termed cross-situational or observational learn- ing, is based on the intuitive idea that one way that a learner can determine the meaning of a word is to find something in common across all observed uses of that word (Pinker, 1984; Gleitman, 1990; Siskind, 1996). Hence learning takes place through the statistical sam- pling of the contexts in which a word appears. Since the learner receives no feedback about its inferences, we refer to this scheme as unsupervised learning. The sec- ond scheme, known generally as operant conditioning, in- volves the active participation of the agents in the learn- ing process, with exchange of non-linguistic cues to pro- vide feedback on the hearer inferences. This supervised learning scheme has been applied to the design of a sys- tem for communication by autonomous robots – the so- called language game in the Talking Heads experiments (Steels, 2003). Despite the technological appeal, the em- pirical evidence is that most part of the lexicon is ac- quired by children as a product of unsupervised learning (Pinker, 1984; Gleitman, 1990; Bloom, 2000). Interest

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