Year: 2011
Type: article
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 limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. Key words: lexicon bootstrapping; cross-situational learning; supervised learning; random occupancy problems
Authors: Jose Fernando Fontanari, Angelo Cangelosi
Institutions: Universidade de São Paulo, University of Plymouth
Language: English
Subfield: Cultural Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1974536209