Harald Baayen, Tübingen

Implicit morphology is a new computational theory of lexical processing in reading, listening, and speaking. Fundamental to Implicit Morphology are the Rescorla-Wagner equations, which define the model’s computational engine for implicit learning. This computational engine consists of a simple two-layer network with sublexical units of form (e.g., letter or phone triplets) and lexeme units (symbols for categorized experiences of the world). Applied to corpora or lexical databases, the model generates state-of-the art predictions for, e.g., reaction times, initial fixation durations, acoustic durations, and aspects of the brain’s electrophysiological response to lexical stimuli. Even though the model has no representations for exponents, stems, morphemes, or whole words, it properly captures the processing effects that in the traditional literature are taken as diagnostic evidence for the existence of such representations. Furthermore, the model properly deals with both existing and novel forms. Implicit Morphology replaces the three classic axioms of traditional linguistic theories — the dual articulation of language, abstraction as the key to prediction, and hierarchical morphemic (de)composition — by the hypothesis of pervasive sublexical co-learning. This course will provide participants with in-depth training in thinking about morphology and language from the perspective of error-driven discrimination learning, as well as with hands-on training in the use of the ndl2 package, which provides an optimized toolkit for applying the Rescorla-Wagner equations to big data.

Selected references

Baayen, R. H. (2010) Demythologizing the word frequency effect: A discriminative learning perspective. The Mental Lexicon 5, 436-461.

Baayen, R. H. (2011). Corpus linguistics and naive discriminative learning. Brazilian Journal of Applied Linguistics 11, 295-328.

Baayen, R. H., Hendrix, P. and Ramscar, M. (2013). Sidestepping the combinatorial explosion: Towards a processing model based on discriminative learning. Language and Speech 56 , 329-347.

Baayen, R. H., Milin, P., Filipovic Durdevic, D., Hendrix, P. and Marelli, M. (2011), An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review 118, 438-482.

Ramscar, M., Dye, M. & McCauley, S. (2013) Error and expectation in language learning: The curious absence of ‘mouses’ in adult speech. Language, 89(4), 760-793.

Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010) The Effects of Feature-Label-Order and their implications for symbolic learning. Cognitive Science, 34(6), 909-957.