Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

Background: Quantitative neural models of speech acquisition and speech processing are rare. Methods: In this paper, we describe a neural model for simulating speech acquisition, speech production, and speech perception. The model is based on two important neural features: associative learning and self-organization. The model describes an SOM-based approach to speech acquisition, i.e. how speech knowledge and speaking skills are learned and stored in the context of self-organizing maps (SOMs). Results:The model elucidates that phonetic features, such as high-low, front-back in the case of vowels, place and manner or articulation in the case of consonants and stressed vs. unstressed for syllables, result from the ordering of syllabic states at the level of a supramodal phonetic self-organizing map. After learning, the speech production and speech perception of speech items results from the co-activation of neural states within different cognitive and sensorimotor neural maps.Conclusion: This quantitative model gives an intuitive understanding of basic neurobiological principles from the viewpoint of speech acquisition and speech processing.

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In communication, different forms of language combinations are possible for bimodal bilinguals, who use a spoken and a signed language. They can either switch from one language to another (language switching) or produce a word and a sign simultaneously (language blending). The present study examines language control mechanisms in language switching and simultaneous bimodal language production, comparing single-response (German or German Sign Language) and dual-response trials (Blend of the German word and the German Sign Language sign). There were three pure blocks, one for each Target-response (German, German Sign Language, Blend), as well as mixed blocks, in which participants switched between all three Target-responses. We observed language mixing costs, switch costs and dual-response costs. Further, the data pattern showed a specific dual-response advantage for switching into a Blend (i.e., a dual-response trial), indicating the specific nature of a blended response in bimodal bilingual language production.

In language switching, it is assumed that in order to produce a response in one language, the other language must be inhibited. In unimodal (spoken-spoken) language switching, the fact that the languages share the same primary output channel (the mouth) means that only one language can be produced at a time. In bimodal (spoken-signed) language switching, however, it is possible to produce both languages simultaneously. In our study, we examined modality effects in language switching using multilingual subjects (speaking German, English, and German Sign Language). Focusing on German vocal responses, since they are directly compatible across conditions, we found shorter reaction times, lower error rates, and smaller switch costs in bimodal vs. unimodal switching. This result suggests that there are different inhibitory mechanisms at work in unimodal and bimodal language switching. We propose that lexical inhibition is involved in unimodal switching, whereas output channel inhibition is involved in bimodal switching.