Modeling the word recognition data of Vitevitch and Luce (1998): is it ARTful?

Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks

In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and accurately listeners retrieved words from that part of memory known as the mental lexicon. In the present study, we used three computer simulations to explore how three different cognitive network architectures could account for the previously observed effects of phonotactics on processing. The results of Simulation 1 showed that some—but not all—effects of phonotactics could be accounted for in a network where nodes represent words and edges connect words that are phonologically related to each other. In Simulation 2, a different network architecture was used to again account for some—but not all—effects of phonotactics and phonological neighborhood density. A bipartite network was used in Simulation 3 to account for many of the previously observed effects of phonotactic knowledge on spoken word recognition. The value of using computer simulations to explore different network architectures is discussed.

Novel stress phonotactics are learnable by English speakers: Novel tone phonotactics are not

Speech errors are sensitive to newly learned phonotactic constraints. For example, if speakers produce strings of syllables in which /f/ is an onset if the vowel is /æ/, but a coda if the vowel is /I/, their slips will respect that constraint after a period of sleep. Constraints in which the contextual factor is nonlinguistic, however, do not appear to be learnable by this method—for example, /f/ is an onset if the speech rate is fast, but /f/ is a coda if the speech rate is slow. The present study demonstrated that adult English speakers can learn (after a sleep period) constraints based on stress (e.g., /f/ is an onset if the syllable is stressed, but /f/ is a coda if the syllable is unstressed), but cannot learn analogous constraints based on tone (e.g., /f/ is an onset if the tone is rising, but /f/ is a coda if the tone is falling). The results are consistent with the fact that, in English, stress is a relevant lexical phonological property (e.g., “INsight” and “inCITE” are different words), but tone is not (e.g., “yes!” and “yes?” are the same word, despite their different pragmatic functions). The results provide useful constraints on how consolidation effects in learning may interact with early learning experiences.

Some behavioral and neurobiological constraints on theories of audiovisual speech integration: a review and suggestions for new directions

Summerfield (1987) proposed several accounts of audiovisual speech perception, a field of research that has burgeoned in recent years. The proposed accounts included the integration of discrete phonetic features, vectors describing the values of independent acoustical and optical parameters, the filter function of the vocal tract, and articulatory dynamics of the vocal tract. The latter two accounts assume that the representations of audiovisual speech perception are based on abstract gestures, while the former two assume that the representations consist of symbolic or featural information obtained from visual and auditory modalities. Recent converging evidence from several different disciplines reveals that the general framework of Summerfield's feature-based theories should be expanded. An updated framework building upon the feature-based theories is presented. We propose a processing model arguing that auditory and visual brain circuits provide facilitatory information when the inputs are correctly timed, and that auditory and visual speech representations do not necessarily undergo translation into a common code during information processing. Future research on multisensory processing in speech perception should investigate the connections between auditory and visual brain regions, and utilize dynamic modeling tools to further understand the timing and information processing mechanisms involved in audiovisual speech integration.

Evaluation and comparison of computational models

Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model evaluation and selection, in particular explaining why generalizability is the preferred criterion for model selection. This is followed by a review of measures of generalizability. The final section demonstrates the use of five versatile and easy-to-use selection methods for choosing between two mathematical models of protein folding.