Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success rates and the time taken to learn words are affected by the degree of referential uncertainty, with greater referential uncertainty leading to less reliable, slower learning. Words are also learned less successfully and more slowly (...) if they are presented interleaved with occurrences of other words, although this effect is relatively weak. We present additional analyses of participants’ trial-by-trial behavior showing that participants make use of various cross-situational learning strategies, depending on the difficulty of the word-learning task. When referential uncertainty is low, participants generally apply a rigorous eliminative approach to cross-situational learning. When referential uncertainty is high, or exposures to different words are interleaved, participants apply a frequentist approximation to this eliminative approach. We further suggest that these two ways of exploiting cross-situational information reside on a continuum of learning strategies, underpinned by a single simple associative learning mechanism. (shrink)
Research into child language reveals that it takes a long time for children to learn the correct mapping of colour words. Steels & Belpaeme's (S&B's) guessing game, however, models fast learning of words. We discuss computational studies based on cross-situational learning, which yield results that are more consistent with the empirical child language data than those obtained by S&B.
Protolanguage reconstructed.Andrew D. M. Smith - 2008 - Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies / Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies 9 (1):100-116.details
One important difference between existing accounts of protolanguage lies in their assumptions on the semantic complexity of protolinguistic utterances. I bring evidence about the nature of linguistic communication to bear on the plausibility of these assumptions, and show that communication is fundamentally inferential and characterised by semantic uncertainty. This not only allows individuals to maintain variation in linguistic representation, but also imposes a selection pressure that meanings be reconstructible from context. I argue that protolanguage utterances had varying degrees of semantic (...) complexity, and developed into complex language gradually, through the same processes of re-analysis and analogy which still underpin continual change in modern languages. (shrink)
We agree that language adapts to the brain, but we note that language also has to adapt to brain-external constraints, such as those arising from properties of the cultural transmission medium. The hypothesis that Christiansen & Chater (C&C) raise in the target article not only has profound consequences for our understanding of language, but also for our understanding of the biological evolution of the language faculty.