Alastair Charles Smith

An amodal shared resource model of language-mediated visual attention

Language-mediated visual attention describes the interaction of two fundamental components of the human cognitive system, language and vision. Within this paper we present an amodal shared resource model of language-mediated visual attention that offers a description of the information and processes involved in this complex multimodal behavior and a potential explanation for how this ability is acquired. We demonstrate that the model is not only sufficient to account for the experimental effects of Visual World Paradigm studies but also that these effects are emergent properties of the architecture of the model itself, rather than requiring separate information processing channels or modular processing systems. The model provides an explicit description of the connection between the modality-specific input from language and vision and the distribution of eye gaze in language-mediated visual attention. The paper concludes by discussing future applications for the model, specifically its potential for investigating the factors driving observed individual differences in language-mediated eye gaze.

Gavagai is as Gavagai does : learning nouns and verbs from cross-situational statistics

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category-level as well as individual word-level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning.

Complex word recognition behaviour emerges from the richness of the word learning environment

Computational models can reflect the complexity of human behaviour by implementing multiple constraints within their architecture, and/or by taking into account the variety and richness of the environment to which the human is responding. We explore the second alternative in a model of word recognition that learns to map spoken words to visual and semantic representations of the words’ concepts. Critically, we employ a phonological representation utilising coarse-coding of the auditory stream, to mimic early stages of language development that are not dependent on individual phonemes to be isolated in the input, which may be a consequence of literacy development. The model was tested at different stages during training, and was able to simulate key behavioural features of word recognition in children: a developing effect of semantic information as a consequence of language learning, and a small but earlier effect of phonological information on word processing. We additionally tested the role of visual information in word processing, generating predictions for behavioural studies, showing that visual information could have a larger effect than semantics on children’s performance, but that again this affects recognition later in word processing than phonological information. The model also provides further predictions for performance of a mature word recognition system in the absence of fine-coding of phonology, such as in adults who have low literacy skills. The model demonstrated that such phonological effects may be reduced but are still evident even when multiple distractors from various modalities are present in the listener’s environment. The model demonstrates that complexity in word recognition can emerge from a simple associative system responding to the interactions between multiple sources of information in the language learner’s environment.

What are the functional units in reading? Evidence for statistical variation influencing word processing

Computational models of reading have differed in terms of whether they propose a single route forming the mapping between orthography and phonology or whether there is a lexical/sublexical route distinction. A critical test of the architecture of the reading system is how it deals with multi-letter graphemes. Rastle and Coltheart (1998) found that the presence of digraphs in nonwords but not in words led to an increase in naming times, suggesting that nonwords were processed via a distinct sequential route to words. In contrast Pagliuca, Monaghan, and McIntosh (2008) implemented a single route model of reading and showed that under conditions of visual noise the presence of digraphs in words did have an effect on naming accuracy. In this study, we investigated whether such digraph effects could be found in both words and nonwords under conditions of visual noise. If so it would suggest that effects on words and nonwords are comparable. A single route connectionist model of reading showed greater accuracy for both words and nonwords containing digraphs. Experimental results showed participants were more accurate in recognising words if they contained digraphs. However contrary to model predictions they were less accurate in recognising nonwords containing digraphs compared to controls. We discuss the challenges faced by both theoretical perspectives in interpreting these findings and in light of a psycholinguistic grain size theory of reading.